diff --git a/.gitignore b/.gitignore
index f3c3970..8a29f28 100644
--- a/.gitignore
+++ b/.gitignore
@@ -18,4 +18,5 @@ docs/build/
# ignore MacOS generated files
*.DS_Store
-
+#ignore openmpi folder
+testing/openmpi-4.1.1/
diff --git a/GRSlib/.GRS.py.swn b/GRSlib/.GRS.py.swn
new file mode 100644
index 0000000..925d46e
Binary files /dev/null and b/GRSlib/.GRS.py.swn differ
diff --git a/GRSlib/.GRS.py.swo b/GRSlib/.GRS.py.swo
new file mode 100644
index 0000000..345fc71
Binary files /dev/null and b/GRSlib/.GRS.py.swo differ
diff --git a/GRSlib/GRS.py b/GRSlib/GRS.py
index 35dab1b..182cc02 100644
--- a/GRSlib/GRS.py
+++ b/GRSlib/GRS.py
@@ -1,10 +1,11 @@
from GRSlib.parallel_tools import ParallelTools
from GRSlib.io.input import Config
from GRSlib.converters.convert_factory import convert
+from GRSlib.motion.scoring import Scoring, elems, get_desc_count
from GRSlib.motion.scoring_factory import scoring
+from GRSlib.motion.motion import Gradient, Genetic
from GRSlib.motion.scoring import Scoring
from GRSlib.motion.motion import Gradient, Optimize, Create
-
import random, copy, os, glob, shutil
import numpy as np
@@ -165,6 +166,38 @@ def get_score(self,data):
raise RuntimeError(">>> Found unmatched BASIS for target and current descriptors")
return score
+ def get_ensemble(self,data):
+ print("get ensemble called.")
+ print("Using elems:",elems)
+ print("Ensemble Target")
+ scores=None
+ try:
+ scores = self.score.ensemble_score(
+ n_totconfig=10, # Example value
+ data_path='bcc.data', # Example value
+ cross_weight=1.0,
+ self_weight=1.0,
+ randomize_comps=False,
+ mincellsize=54,
+ maxcellsize=55,
+ target_comps={'W': 1.0}, # Ensure this is a dictionary
+ min_typ_global='box',
+ soft_strength=0.0,
+ nelements=len(elems), # Ensure elems is defined
+ n_descs=get_desc_count('coupling_coefficients.yace'), # Ensure this function is defined
+ mask=None, # Set this as needed
+ rand_comp=1) # Ensure this is defined correctly
+ print("Scores returned from ensemble_score:", scores)
+ except Exception as e:
+ print(f"An error occurred while calculating the ensemble score: {e}")
+ return None # Optionally return None or handle the error as needed
+ # Check if score was calculated
+ if scores is None or len(scores) == 0:
+ print("No scores were calculated returning None")
+ raise RuntimeError("Ensemble score could not be calculated.")
+
+ return scores # Ensure you return the score
+#""" self.score = Scoring(data, self.current_desc, self.target_desc, self.prior_desc, self.pt, self.config)score = self.score.ensemble_score() return score"""
def propose_structure(self):
"""
@@ -187,21 +220,17 @@ def genetic_move(self,data):
#3) Hybridize, Mutate based on set of rules and probabilities
#4) Store socring information with best-of-generation and best-overall isolated
#5) Loop until generation limit or scoring residual below threshold
- if data == None:
- data = self.propose_structure()
- self.descriptors['current']= self.convert_to_desc(data)
- try:
- self.descriptors['target'] = np.load(self.config.sections['TARGET'].target_fdesc)
- except:
- self.descriptors['target'] = self.convert_to_desc(self.config.sections['TARGET'].target_fname)
-
- self.score = Scoring(self.pt, self.config, self.loss_func, data, self.descriptors) # Set scoring class to assign scores to moves
- self.genmove = Optimize(self.pt, self.config, self.score) #Set desired motion class with scoring attached
-
-
- #self.genmove.tournament_selection()
- #for iterations in top_candidates[1], convert.ase_to_lammps
- #self.write_output()
+ print("Called Genetic_Move")
+ if self.current_desc is None:
+ self.current_desc = self.propose_structure()
+ #if data == None:
+ #data = self.propose_structure()
+
+ self.descriptors['current'] = self.convert_to_desc(data)
+ self.descriptors['target'] = self.convert_to_desc(self.config.sections['TARGET'].target_fname)
+ self.genmove = Genetic(self.pt, self.config, data, self.descriptors)
+ #Dont want to make a func call the default here since the user will define this?
+ #Need a fallback to provide a good default if a genetic move is called.
# @self.pt.single_timeit
def gradient_move(self,data):
diff --git a/GRSlib/Ver0_Files/opt_tools.py b/GRSlib/Ver0_Files/opt_tools.py
index 625fee5..14dca25 100644
--- a/GRSlib/Ver0_Files/opt_tools.py
+++ b/GRSlib/Ver0_Files/opt_tools.py
@@ -311,14 +311,16 @@ def at_to_lmp(atoms,index,temperature=10000.0,min_typ='temp', coefftypes=True,so
return s
-from crystal_enum import *
+#from crystal_enum import *
+from GRSlib.Ver0_Files.crystal_enum import *
def prim_crystal(elem_list):
all_prims = get_prim_structs(elem_list, multiatom=False)
myind = np.random.choice(range(len(all_prims)))
return all_prims[myind]
-from hnf import *
+#from hnf import *
+from GRSlib.Ver0_Files.hnf import *
#elems[0],desired_size,volfrac=1.0,cubic=True,override_lat='fcc',override_a=2.98
def bulk_template(elem,desired_size,volfrac=1.0,cubic=True,override_lat=None,override_a=None):
if not override_a:
@@ -407,28 +409,34 @@ def internal_generate_cell(index,desired_size=4,template=None,desired_comps={'Ni
#from ase.build import bulk
#from ase import Atoms,Atom
chems = list(desired_comps.keys())
+ print(f"Generate new template for chemical: {chems[0]} with desired size: {desired_size}.")
template = Atoms([chems[0]]*desired_size)
atoms_base = bulk(chems[0])
vol_base = vnp.dot(vnp.cross(atoms_base.get_cell()[0],atoms_base.get_cell()[1]),atoms_base.get_cell()[2])
a_simp = vol_base**(1/3)
#cells = get_hnfs(hnf_trs=[desired_size])
cells_all = get_hnfs(hnf_trs=[desired_size])
+ print(f"Total cells generated: {len(cells_all)}")
toli = int((desired_size)**(1/3))
cells= limit_mats_len(cells_all,desired_size,tol=0.16)
#print ('ncells',len(cells))
try:
cell = a_simp * cells[vnp.random.choice(range(len(cells)))]
+ print(f"Selected cell shape:{cell}")
except ValueError:
cell = a_simp * cells_all [vnp.random.choice(range(len(cells_all)))]
+ print("ValueError encountered, using alt. cell.")
#print ('hnf',cell)
norms = np.array([np.linalg.norm(k) for k in cell])
norms /= (desired_size)
+ print(f"Normalized cell dimensions: {norms}")
#print('hnf norms',norms)
#cell = a_simp * cells[-2]
template.set_cell(cell)
template.set_scaled_positions(vnp.random.uniform(0,1,(desired_size,3)))
+ print(f"Template set with cell dimensions: {template.get_cell()}")
else:
- tempalte = template
+ template = template
new_comps = {elem:int(round(len(template)*cmp))/len(template) for elem,cmp in desired_comps.items()}
print ('for structure of size:%d'% len(template),'desired compositions:', desired_comps,'will be replaced with', new_comps)
diff --git a/GRSlib/converters/sections/lammps_ace.py b/GRSlib/converters/sections/lammps_ace.py
index 2667aa0..20ee5f8 100644
--- a/GRSlib/converters/sections/lammps_ace.py
+++ b/GRSlib/converters/sections/lammps_ace.py
@@ -23,7 +23,7 @@ def _prepare_lammps(self):
compute_name='pace'
numtypes = len(self.config.sections['BASIS'].elements)
- base_pace = "compute %s all pace coupling_coefficients.yace %s %s" % (compute_name,self.config.sections['BASIS'].bikflag,self.config.sections['BASIS'].dgradflag)
+ base_pace = "compute %s all pace coupling_coefficients.yace %d %d" % (compute_name,self.config.sections['BASIS'].bikflag,self.config.sections['BASIS'].dgradflag)
self._lmp.command(base_pace)
return compute_name
@@ -51,8 +51,7 @@ def _collect_lammps_single(self):
elems = elemstr4.split()
nelements = len(elems)
desclines = [line for line in lines if 'mu0' in line]
-
- #ncols_pace = int(len(desclines)/nelements)
+
ncols_pace = int(len(desclines)/nelements) + nelements
nrows_pace = num_atoms
lmp_pace = _extract_compute_np(self._lmp, "pace", 0, 2, (nrows_pace, ncols_pace))
diff --git a/GRSlib/io/sections/basis.py b/GRSlib/io/sections/basis.py
index 1a13eec..eb57ac2 100644
--- a/GRSlib/io/sections/basis.py
+++ b/GRSlib/io/sections/basis.py
@@ -2,89 +2,81 @@
import itertools
from GRSlib.io.sections.sections import Section
-try:
+#try:
- from fitsnap3lib.lib.sym_ACE.pa_gen import *
- from fitsnap3lib.lib.sym_ACE.yamlpace_tools.potential import *
- from fitsnap3lib.lib.sym_ACE.wigner_couple import *
- from fitsnap3lib.lib.sym_ACE.clebsch_couple import *
+from fitsnap3lib.lib.sym_ACE.pa_gen import *
+from fitsnap3lib.lib.sym_ACE.yamlpace_tools.potential import *
+from fitsnap3lib.lib.sym_ACE.wigner_couple import *
+from fitsnap3lib.lib.sym_ACE.clebsch_couple import *
- class Basis(Section):
-
- def __init__(self, name, config, pt, infile, args):
- super().__init__(name, config, pt, infile, args)
-
- allowedkeys = ['descriptor', 'numTypes', 'ranks', 'lmax', 'nmax', 'mumax', 'nmaxbase', 'rcutfac', 'lambda',
- 'elements', 'bzeroflag', 'erefs', 'rcinner', 'drcinner', 'RPI_heuristic', 'lmin',
- 'bikflag', 'dgradflag','wigner_flag','b_basis','manuallabs']
- for value_name in config['BASIS']:
- if value_name in allowedkeys: continue
- else:
- raise RuntimeError(">>> Found unmatched variable in BASIS section of input: ",value_name)
- self.descriptor = self.get_value("BASIS", "descriptor", "ACE")
- self.numtypes = len(self.get_value("BASIS", "elements", "H"))
- self.ranks = self.get_value("BASIS","ranks","3").split()
- self.lmin = self.get_value("BASIS", "lmin", "0").split()
- self.lmax = self.get_value("BASIS", "lmax", "2").split()
- self.nmax = self.get_value("BASIS", "nmax", "2").split()
- #self.mumax = self.get_value("BASIS","mumax", "1")
- self.nmaxbase = self.get_value("BASIS", "nmaxbase", "16","int")
- self.rcutfac = self.get_value("BASIS", "rcutfac", "4.5").split()
- self.lmbda = self.get_value("BASIS","lambda",'1.35').split()
- self.rcinner = self.get_value("BASIS","rcinner",'0.0').split()
- self.drcinner = self.get_value("BASIS","drcinner",'0.01').split()
- self.elements = self.get_value("BASIS", "elements", "H").split()
- self.mumax = len(self.elements)
- #self.erefs = self.get_value("ACE", "erefs", "0.0").split()
- self.erefs = [0.0] * len(self.elements)
- self.bikflag = self.get_value("BASIS", "bikflag", "1", "bool")
- self.dgradflag = self.get_value("BASIS", "dgradflag", "0", "bool")
- self.b_basis = self.get_value("BASIS" , "b_basis" , "pa_tabulated")
- self.manuallabs = self.get_value("BASIS", "manuallabs", 'None')
- self.type_mapping = {}
- for i, atom_type in enumerate(self.elements):
- self.type_mapping[atom_type] = i+1
+class Basis(Section):
+ def __init__(self, name, config, pt, infile, args):
+ super().__init__(name, config, pt, infile, args)
+
+ allowedkeys = ['descriptor', 'numTypes', 'ranks', 'lmax', 'nmax', 'mumax', 'nmaxbase', 'rcutfac', 'lambda',
+ 'elements', 'bzeroflag', 'erefs', 'rcinner', 'drcinner', 'RPI_heuristic', 'lmin',
+ 'bikflag', 'dgradflag','wigner_flag','b_basis','manuallabs']
+ for value_name in config['BASIS']:
+ if value_name in allowedkeys: continue
+ else:
+ raise RuntimeError(">>> Found unmatched variable in BASIS section of input: ",value_name)
+ self.descriptor = self.get_value("BASIS", "descriptor", "ACE")
+ self.numtypes = len(self.get_value("BASIS", "elements", "H"))
+ self.ranks = self.get_value("BASIS","ranks","3").split()
+ self.lmin = self.get_value("BASIS", "lmin", "0").split()
+ self.lmax = self.get_value("BASIS", "lmax", "2").split()
+ self.nmax = self.get_value("BASIS", "nmax", "2").split()
+ #self.mumax = self.get_value("BASIS","mumax", "1")
+ self.nmaxbase = self.get_value("BASIS", "nmaxbase", "16","int")
+ self.rcutfac = self.get_value("BASIS", "rcutfac", "4.5").split()
+ self.lmbda = self.get_value("BASIS","lambda",'1.35').split()
+ self.rcinner = self.get_value("BASIS","rcinner",'0.0').split()
+ self.drcinner = self.get_value("BASIS","drcinner",'0.01').split()
+ self.elements = self.get_value("BASIS", "elements", "H").split()
+ self.mumax = len(self.elements)
+ #self.erefs = self.get_value("ACE", "erefs", "0.0").split()
+ self.erefs = [0.0] * len(self.elements)
+ self.bikflag = self.get_value("BASIS", "bikflag", "0", "bool")
+ self.dgradflag = self.get_value("BASIS", "dgradflag", "0", "bool")
+ self.b_basis = self.get_value("BASIS" , "b_basis" , "pa_tabulated")
+ self.manuallabs = self.get_value("BASIS", "manuallabs", 'None')
+ self.type_mapping = {}
+ for i, atom_type in enumerate(self.elements):
+ self.type_mapping[atom_type] = i+1
- self.bzeroflag = self.get_value("BASIS", "bzeroflag", "1", "bool")
- self.wigner_flag = self.get_value("BASIS", "wigner_flag", "1", "bool")
+ self.bzeroflag = self.get_value("BASIS", "bzeroflag", "0", "bool")
+ self.wigner_flag = self.get_value("BASIS", "wigner_flag", "1", "bool")
- #if self.bikflag:
- # self._assert_dependency('bikflag', "CALCULATOR", "per_atom_energy", True)
- self.lmax_dct = {int(rnk):int(lmx) for rnk,lmx in zip(self.ranks,self.lmax)}
- if self.b_basis != 'pa_tabulated':
- self.pt.single_print('WARNING: Only change ACE basis flags if you know what you are doing!')
- self._generate_b_list()
- self._write_couple()
- Section.num_desc = len(self.blist)
- #print("Generating ACE lables yields this many cofficients: ",Section.num_desc)
- self.delete()
+ #if self.bikflag:
+ # self._assert_dependency('bikflag', "CALCULATOR", "per_atom_energy", True)
+ self.lmax_dct = {int(rnk):int(lmx) for rnk,lmx in zip(self.ranks,self.lmax)}
+ if self.b_basis != 'pa_tabulated':
+ self.pt.single_print('WARNING: Only change ACE basis flags if you know what you are doing!')
+ self._generate_b_list()
+ self._write_couple()
+ Section.num_desc = len(self.blist)
+ self.delete()
- def _generate_b_list(self):
- self.blist = []
- self.nus = []
- self.blank2J = []
- prefac = 1.0
- i = 0
+ def _generate_b_list(self):
+ self.blist = []
+ self.nus = []
+ self.blank2J = []
+ prefac = 1.0
+ i = 0
- if self.manuallabs != 'None':
- with open(self.manuallabs,'r') as readjson:
- labdata = json.load(readjson)
- ranked_chem_nus = [list(ik) for ik in list(labdata.values())]
- elif self.manuallabs == 'None' and self.b_basis == 'minsub':
- from fitsnap3lib.lib.sym_ACE.rpi_lib import descriptor_labels_YSG
- if type(self.lmin) == list:
- if len(self.lmin) == 1:
- self.lmin = self.lmin * len(self.ranks)
- ranked_chem_nus = [descriptor_labels_YSG(int(rnk), int(self.nmax[ind]), int(self.lmax[ind]), int(self.mumax),lmin = int(self.lmin[ind]) ) for ind,rnk in enumerate(self.ranks)]
- else:
- ranked_chem_nus = [descriptor_labels_YSG(int(rnk), int(self.nmax[ind]), int(self.lmax[ind]), int(self.mumax),lmin = int(self.lmin) ) for ind,rnk in enumerate(self.ranks)]
- elif self.manuallabs == 'None' and self.b_basis == 'pa_tabulated':
- ranked_chem_nus = []
+ if self.manuallabs != 'None':
+ with open(self.manuallabs,'r') as readjson:
+ labdata = json.load(readjson)
+ ranked_chem_nus = [list(ik) for ik in list(labdata.values())]
+ elif self.manuallabs == 'None' and self.b_basis == 'minsub':
+ from fitsnap3lib.lib.sym_ACE.rpi_lib import descriptor_labels_YSG
+ if type(self.lmin) == list:
if len(self.lmin) == 1:
self.lmin = self.lmin * len(self.ranks)
+ ranked_chem_nus = [descriptor_labels_YSG(int(rnk), int(self.nmax[ind]), int(self.lmax[ind]), int(self.mumax),lmin = int(self.lmin[ind]) ) for ind,rnk in enumerate(self.ranks)]
for ind,rank in enumerate(self.ranks):
rank = int(rank)
- PA_lammps, not_compat = pa_labels_raw(rank,int(self.nmax[ind]),int(self.lmax[ind]), int(self.mumax),lmin = int(self.lmin[ind]) )
+ PA_lammps, not_compat = pa_labels_raw(rank,int(self.nmax[ind]),int(self.lmax[ind]), int(self.mumax),lmin = int(self.lmin[ind]) )
ranked_chem_nus.append(PA_lammps)
if len(not_compat) > 0:
self.pt.single_print('Functions incompatible with lammps for rank %d : '% rank, not_compat)
@@ -131,65 +123,122 @@ def _generate_b_list(self):
self.blank2J = np.concatenate((onehot_atoms, self.blank2J), axis=1)
self.blank2J = np.reshape(self.blank2J, (len(self.blist) + self.numtypes))
else:
- self.blank2J = np.reshape(self.blank2J, len(self.blist))
-
- def _write_couple(self):
- @self.pt.sub_rank_zero
- def decorated_write_couple():
- reference_ens = [0.0] * len(self.elements)
- bondinds=range(len(self.elements))
- bonds = [b for b in itertools.product(bondinds,bondinds)]
- bondstrs = ['[%d, %d]' % b for b in bonds]
- assert len(self.lmbda) == len(bondstrs), "must provide rc, lambda, for each BOND type"
- assert len(self.rcutfac) == len(bondstrs), "must provide rc, lambda, for each BOND type"
- if len(self.lmbda) == 1:
- lmbdavals = self.lmbda
- rcvals = self.rcutfac
- rcinnervals = self.rcinner
- drcinnervals = self.drcinner
- if len(self.lmbda) > 1:
- lmbdavals = {bondstr:lmb for bondstr,lmb in zip(bondstrs,self.lmbda)}
- rcvals = {bondstr:lmb for bondstr,lmb in zip(bondstrs,self.rcutfac)}
- rcinnervals = {bondstr:lmb for bondstr,lmb in zip(bondstrs,self.rcinner)}
- drcinnervals = {bondstr:lmb for bondstr,lmb in zip(bondstrs,self.drcinner)}
+ ranked_chem_nus = [descriptor_labels_YSG(int(rnk), int(self.nmax[ind]), int(self.lmax[ind]), int(self.mumax),lmin = int(self.lmin) ) for ind,rnk in enumerate(self.ranks)]
+ elif self.manuallabs == 'None' and self.b_basis == 'pa_tabulated':
+ ranked_chem_nus = []
+ if len(self.lmin) == 1:
+ self.lmin = self.lmin * len(self.ranks)
+ for ind,rank in enumerate(self.ranks):
+ rank = int(rank)
+ PA_lammps, not_compat = pa_labels_raw(rank,int(self.nmax[ind]),int(self.lmax[ind]), int(self.mumax),lmin = int(self.lmin[ind]) )
+ ranked_chem_nus.append(PA_lammps)
+ if len(not_compat) > 0:
+ self.pt.single_print('Functions incompatible with lammps for rank %d : '% rank, not_compat)
- ldict = {int(rank):int(lmax) for rank,lmax in zip(self.ranks,self.lmax)}
- L_R = 0
- M_R = 0
- rankstrlst = ['%s']*len(self.ranks)
- rankstr = ''.join(rankstrlst) % tuple(self.ranks)
- lstrlst = ['%s']*len(self.ranks)
- lstr = ''.join(lstrlst) % tuple(self.lmax)
- if not self.wigner_flag:
- try:
- with open('cg_LR_%d_r%s_lmax%s.pickle' %(L_R,rankstr,lstr),'rb') as handle:
- ccs = pickle.load(handle)
- except FileNotFoundError:
- ccs = get_cg_coupling(ldict,L_R=L_R)
- #print (ccs)
- #store them for later so they don't need to be recalculated
- store_generalized(ccs, coupling_type='cg',L_R=L_R)
+ highranks = [int(r) for r in self.ranks if int(r) >= 5]
+ warnflag = any([ self.lmax_dct[rank] >= 5 and self.lmin[ind] > 1 for ind,rank in enumerate(highranks)])
+ if warnflag:
+ self.pt.single_print('WARNING: lmax and lmin for your current max rank will generate descriptors that cannot be entered into LAMMPS_PACE - try a lower lmax for ranks >= 4' % warnflag[0])
+ nus_unsort = [item for sublist in ranked_chem_nus for item in sublist]
+ nus = nus_unsort.copy()
+ mu0s = []
+ mus =[]
+ ns = []
+ ls = []
+ for nu in nus_unsort:
+ mu0ii,muii,nii,lii = get_mu_n_l(nu)
+ mu0s.append(mu0ii)
+ mus.append(tuple(muii))
+ ns.append(tuple(nii))
+ ls.append(tuple(lii))
+ nus.sort(key = lambda x : mus[nus_unsort.index(x)],reverse = False)
+ nus.sort(key = lambda x : ns[nus_unsort.index(x)],reverse = False)
+ nus.sort(key = lambda x : ls[nus_unsort.index(x)],reverse = False)
+ nus.sort(key = lambda x : mu0s[nus_unsort.index(x)],reverse = False)
+ nus.sort(key = lambda x : len(x),reverse = False)
+ nus.sort(key = lambda x : mu0s[nus_unsort.index(x)],reverse = False)
+ byattyp = srt_by_attyp(nus)
+ #config.nus = [item for sublist in list(byattyp.values()) for item in sublist]
+ for atype in range(self.numtypes):
+ nus = byattyp[str(atype)]
+ for nu in nus:
+ i += 1
+ mu0,mu,n,l,L = get_mu_n_l(nu,return_L=True)
+ if L != None:
+ flat_nu = [mu0] + mu + n + l + list(L)
else:
- try:
- with open('wig_LR_%d_r%s_lmax%s.pickle' %(L_R,rankstr,lstr),'rb') as handle:
- ccs = pickle.load(handle)
- except FileNotFoundError:
- ccs = get_wig_coupling(ldict,L_R)
- #print (ccs)
- #store them for later so they don't need to be recalculated
- store_generalized(ccs, coupling_type='wig',L_R=L_R)
+ flat_nu = [mu0] + mu + n + l
+ self.blist.append([i] + flat_nu)
+ self.nus.append(nu)
+ self.blank2J.append([prefac])
+ self.ncoeff = int(len(self.blist)/self.numtypes)
+ if not self.bzeroflag:
+ self.blank2J = np.reshape(self.blank2J, (self.numtypes, int(len(self.blist)/self.numtypes)))
+ onehot_atoms = np.ones((self.numtypes, 1))
+ self.blank2J = np.concatenate((onehot_atoms, self.blank2J), axis=1)
+ self.blank2J = np.reshape(self.blank2J, (len(self.blist) + self.numtypes))
+ else:
+ self.blank2J = np.reshape(self.blank2J, len(self.blist))
+
+ def _write_couple(self):
+ @self.pt.sub_rank_zero
+ def decorated_write_couple():
+ reference_ens = [0.0] * len(self.elements)
+ bondinds=range(len(self.elements))
+ bonds = [b for b in itertools.product(bondinds,bondinds)]
+ bondstrs = ['[%d, %d]' % b for b in bonds]
+ assert len(self.lmbda) == len(bondstrs), "must provide rc, lambda, for each BOND type"
+ assert len(self.rcutfac) == len(bondstrs), "must provide rc, lambda, for each BOND type"
+ if len(self.lmbda) == 1:
+ lmbdavals = self.lmbda
+ rcvals = self.rcutfac
+ rcinnervals = self.rcinner
+ drcinnervals = self.drcinner
+ if len(self.lmbda) > 1:
+ lmbdavals = {bondstr:lmb for bondstr,lmb in zip(bondstrs,self.lmbda)}
+ rcvals = {bondstr:lmb for bondstr,lmb in zip(bondstrs,self.rcutfac)}
+ rcinnervals = {bondstr:lmb for bondstr,lmb in zip(bondstrs,self.rcinner)}
+ drcinnervals = {bondstr:lmb for bondstr,lmb in zip(bondstrs,self.drcinner)}
+
+ ldict = {int(rank):int(lmax) for rank,lmax in zip(self.ranks,self.lmax)}
+ L_R = 0
+ M_R = 0
+ rankstrlst = ['%s']*len(self.ranks)
+ rankstr = ''.join(rankstrlst) % tuple(self.ranks)
+ lstrlst = ['%s']*len(self.ranks)
+ lstr = ''.join(lstrlst) % tuple(self.lmax)
+ if not self.wigner_flag:
+ try:
+ with open('cg_LR_%d_r%s_lmax%s.pickle' %(L_R,rankstr,lstr),'rb') as handle:
+ ccs = pickle.load(handle)
+ except FileNotFoundError:
+ ccs = get_cg_coupling(ldict,L_R=L_R)
+ #print (ccs)
+ #store them for later so they don't need to be recalculated
+ store_generalized(ccs, coupling_type='cg',L_R=L_R)
+ else:
+ try:
+ with open('wig_LR_%d_r%s_lmax%s.pickle' %(L_R,rankstr,lstr),'rb') as handle:
+ ccs = pickle.load(handle)
+ except FileNotFoundError:
+ ccs = get_wig_coupling(ldict,L_R)
+ #print (ccs)
+ #store them for later so they don't need to be recalculated
+ store_generalized(ccs, coupling_type='wig',L_R=L_R)
- apot = AcePot(self.elements, reference_ens, [int(k) for k in self.ranks], [int(k) for k in self.nmax], [int(k) for k in self.lmax], self.nmaxbase, rcvals, lmbdavals, rcinnervals, drcinnervals, [int(k) for k in self.lmin], self.b_basis, **{'ccs':ccs[M_R]})
- apot.write_pot('coupling_coefficients')
+ apot = AcePot(self.elements, reference_ens, [int(k) for k in self.ranks], [int(k) for k in self.nmax], [int(k) for k in self.lmax], self.nmaxbase, rcvals, lmbdavals, rcinnervals, drcinnervals, [int(k) for k in self.lmin], self.b_basis, **{'ccs':ccs[M_R]})
+ apot.write_pot('coupling_coefficients')
- decorated_write_couple()
+ decorated_write_couple()
+"""
except ModuleNotFoundError:
class Ace(Section):
- """
- Dummy class for factory to read if torch is not available for import.
- """
+
+ #Dummy class for factory to read if torch is not available for import.
+
def __init__(self, name, config, pt, infile, args):
super().__init__(name, config, pt, infile, args)
raise ModuleNotFoundError("Missing sympy or pyyaml modules.")
+"""
diff --git a/GRSlib/io/sections/scoring.py b/GRSlib/io/sections/scoring.py
index 777d36c..1c1ae62 100644
--- a/GRSlib/io/sections/scoring.py
+++ b/GRSlib/io/sections/scoring.py
@@ -1,5 +1,4 @@
from GRSlib.io.sections.sections import Section
-
#pt = ParallelTools()
#output = Output()
class Scoring(Section):
@@ -29,3 +28,4 @@ def __init__(self, name, config, pt,infile, args):
# Positive value attracts current structure to priors, negative value promotes diversity (higher entropy)
self.delete()
+
diff --git a/GRSlib/motion/genetic_moves/density.py b/GRSlib/motion/genetic_moves/density.py
index f2a23e3..4d1bfee 100644
--- a/GRSlib/motion/genetic_moves/density.py
+++ b/GRSlib/motion/genetic_moves/density.py
@@ -16,9 +16,10 @@ def readd():
rnd_pos_scale = vnp.random.rand(1,3)
rnd_pos = vnp.matmul(atoms.get_cell(),rnd_pos_scale.T)
rnd_pos = rnd_pos.T[0]
+ #new atom being created and added to a list of already existing atoms
new_atom = Atom('Ne',rnd_pos)
tst_atoms = atoms.copy()
- tst_atoms.append(new_atom)
+ tst_atoms.append(new_atom) #add a new atom to copy
tst_atoms.wrap()
rc = 5.
@@ -32,6 +33,7 @@ def readd():
for i,j in zip(nl[0],nl[1]):
bond_types[i].append( (atomic_numbers[tst_atoms[i].symbol] , atomic_numbers[tst_atoms[j].symbol]) )
return symbol, tst_atoms, at_dists, rnd_pos, bond_types
+ #positioning check for the atoms to make sure the periodic boundary conditions are met
symbol, tst_atoms , at_dists , rnd_pos, bond_types = readd()
bondtyplst = list(bond_types.keys())
syms = [tst_atom.symbol for tst_atom in tst_atoms]
@@ -54,8 +56,54 @@ def readd():
def remove_atom(atoms,symbols,tol = 0.5):
#TODO Currently this is a copy/paste of the old code, needs work.
blmin = closest_distances_generator(atom_numbers=[atomic_numbers[symbol] for symbol in symbols] + [atomic_numbers['Ne']], ratio_of_covalent_radii=0.5)
- return atoms
+ remove_index = vnp.random.choice(len(atoms))
+ remove = atoms[remove_index]
-def change_cell():
- #TODO Currently this is a copy/paste of the old code, needs work.
- return atoms
+ tst_atoms = atoms.copy()
+ tst_atoms.pop(remove_index) #remove the randomly selected atom from copy
+
+ tst_atoms.wrap()
+
+ atinds = [atom.index for atom in tst_atoms]
+ at_dists = {i: [] for i in atinds}
+ nl = primitive_neighbor_list('ijdD', pbc=tst_atoms.pbc, positions=tst_atoms.positions,
+ cell = atoms.get_cell(), cutoff=5.0)
+ bond_types = {i: [] for i in atinds}
+ for i,j in zip(nl[0], nl[-1]):
+ at_dists[i].append(j)
+ for i,j in zip(nl[0],nl[l]):
+ bond_types[i].append((atomic_numbers[tst_atoms[i].symbol],
+ atomic_numbers[tst_atoms[j].symbol]))
+
+ for i, atom in enumerate(tst_atoms):
+ tst_dists = at_dists[i]
+ tst_bonds = bond_types[i]
+ conds = all([vnp.linalg.norm(tst_atoms[i].position - test_atoms[j].position) >=
+ blmin[(atomic_numbers[atom.symbol], tst_bonds[k][1])] - tol for k,j in enumerate(tst_dists)])
+ if not conds:
+ tst_atoms.append(remove)
+ return atoms
+ return tst_atoms
+
+
+def change_cell(): #change cell size through scale variable.
+ cell = atoms.get_cell() #current cell
+ new_atoms = atoms.copy()
+ new_cell = cell * scale
+ new_atoms.set_cell(new_cell, scale_atoms=True) #scale with wanted density
+ return new_atoms
+
+#change cell
+
+#Change density but keep crystals structure polyhedral template to show what map hit is
+
+#Change volume
+
+#Cell is the box that has the atoms
+
+#Change cell - lets them change the ase atoms object - a variable that could scale the object by
+
+# ase doc note
+# --get_cell(complete=False)[source]
+#Get the three unit cell vectors as a :ase.cell.Cell` object.
+#The Cell object resembles a 3x3 ndarray, and cell[i, j] is the jth Cartesian coordinate of the ith cell vector.
diff --git a/GRSlib/motion/lossfunc/moments.py b/GRSlib/motion/lossfunc/moments.py
index 11b5b83..5aca4f1 100644
--- a/GRSlib/motion/lossfunc/moments.py
+++ b/GRSlib/motion/lossfunc/moments.py
@@ -4,6 +4,15 @@
from jax import grad, jit
from functools import partial
+class LossFunction:
+ def __init__(self, config, current_desc, target_desc, prior_desc):
+ self.n_params = 1
+ self.config = config
+ self.current_desc = current_desc.copy()
+ self.target_desc = jnp.array(target_desc)
+ #self.target_desc = target_desc.copy() #copy.deepcopy(target_desc)
+ self.prior_desc = prior_desc.copy() #copy.deepcopy(target_desc)
+ self.loss_ff_grad = grad(self.construct_loss)
class Moments(Scoring):
def __init__(self, *args): #pt, config, target_desc, prior_desc):
self.pt, self.config, descriptors = args
@@ -103,8 +112,9 @@ def construct_loss(self, current_desc, target_desc):
@partial(jit, static_argnums=(0,))
def first_moment(self, current_desc, target_desc):
+ print(type(target_desc))
current_avg = jnp.average(current_desc, axis=0)
- target_avg = jnp.average(target_desc, axis=0)
+ target_avg = jnp.average(jnp.array(target_desc), axis=0)
tst_residual = jnp.sum(jnp.nan_to_num(jnp.abs(current_avg-target_avg)))
is_zero = jnp.array(jnp.isclose(tst_residual,jnp.zeros(tst_residual.shape)),dtype=int)
bonus = -jnp.sum(is_zero*(float(self.config.sections['SCORING'].moments_bonus[0])))
diff --git a/GRSlib/motion/scoring.py b/GRSlib/motion/scoring.py
index 4d39f2f..dd0fe79 100644
--- a/GRSlib/motion/scoring.py
+++ b/GRSlib/motion/scoring.py
@@ -1,11 +1,35 @@
+from GRSlib.Ver0_Files.opt_tools import internal_generate_cell
+from GRSlib.parallel_tools import ParallelTools
+#from GRSlib.motion.lossfunc.moments import LossFunction
+from GRSlib.Ver0_Files.opt_tools import get_desc_count
#from GRSlib.parallel_tools import ParallelTools
#from GRSlib.motion.lossfunc.moments import Moments
#from GRSlib.motion.lossfunc import Gradient
from GRSlib.converters.sections.lammps_base import Base, _extract_compute_np
+from examples.simple_test.GRS_protocol import GRSModel, GRSSampler
import lammps, lammps.mliap
from lammps.mliap.loader import *
from functools import partial
-import numpy as np
+import numpy as vnp
+from GRSlib.Ver0_Files.opt_tools import *
+from GRSlib.GRS import *
+#from examples.simple_test.GRS_protocol import *
+#Scoring has to be a class within motion because we want a consistent reference for scores, ans this
+#refrence will be LAMMPS using a constructed potential energy surface from the representation loss function
+n_totconfig = 10
+data_path = 'bcc.data'
+cross_weight =1.000000
+self_weight = 1.000000
+randomize_comps= False # # flag to use randomized compositions for elements in the dictionary: target_comps = {'Cr':1.0 }
+mincellsize = 54
+maxcellsize=55
+target_comps = {'W:1.0'}
+min_typ_global='box' #box or min
+soft_strength=0.0
+elems=get_desc_count('coupling_coefficients.yace',return_elems=True)
+nelements= len(elems)
+n_descs= get_desc_count('coupling_coefficients.yace')
+rand_comp =1
#Scoring has to be a class within motion because we want a consistent reference for scores, and this
#refrence will be LAMMPS using a constructed potential energy surface from the representation loss function.
@@ -94,4 +118,110 @@ def _extract_commands(self,string):
#Can be given a block of text where it will split them into individual commands
add_lmp_lines = [x for x in string.splitlines() if x.strip() != '']
for line in add_lmp_lines:
- self.lmp.command(line)
+ self.lmp.command(line)
+#internal generate cell is only used in scoring.py but defined in opt_tools + gsqsmodel only in scoring.py currently
+#class ensemble_score(): #will take in the target, and compare it to multiple generated structures -- look at fitsnap sections
+# min and maxatoms the same as maxcellsize?
+# target_comps is in input
+#numelements = num types?
+ def ensemble_score(self, n_totconfig, data_path, cross_weight, self_weight, randomize_comps, mincellsize, maxcellsize, target_comps, min_typ_global, soft_strength, nelements, n_descs, mask, rand_comp):
+ if mask == None:
+ mask = range(n_descs)
+ self.mask = mask # Generates the multiple structures
+ scores = [] # Initialize a list to store scores
+
+ print(f"Starting ensemble_score with {n_totconfig} configurations.") # Debugging line
+
+ for i in range(1, n_totconfig + 1):
+ print(f"Configuration {i}/{n_totconfig} - Using indices: {mask}") # Debugging line
+
+ # Generate the cell
+ g = internal_generate_cell(i, desired_size=vnp.random.choice(range(mincellsize, maxcellsize)), template=None, desired_comps=target_comps, use_template=None, min_typ=min_typ_global, soft_strength=soft_strength)
+ print(g)
+ print(f"Cell generated for configuration {i}: {g}") # Debugging line
+
+ em = GRSModel(nelements, n_descs, mask=mask)
+ sampler = GRSSampler(em, g)
+ em.K_cross = cross_weight
+ em.K_self = self_weight
+
+ print(f"Running minimization for configuration {i}.") # Debugging line
+ # Run the minimization process
+ sampler.run("minimize 1e-6 1e-6 1000 10000")
+
+ print(f"Minimization completed for configuration {i}. Writing data.")
+ sampler.run("write_data %s/sample.%i.dat " % (data_path, i))
+
+ print(f"Data written for configuration {i}. Updating model.") # Debugging line
+ sampler.update_model() # Updating the model combines generated structures
+
+ # Calculate the score for the current configuration
+ score = self.get_score(g) # Pass the generated structure to get_score
+ print(f"Score for configuration {i}: {score}") # Debugging line
+
+ if score is None:
+ print(f"Score for configuration {i} is None.") # Debugging line
+
+ scores.append(score) # Append the score to the list
+
+ print("Final scores list:", scores) # Debugging line
+ return scores # Return the list of scores
+"""def ensemble_score(self, n_totconfig, data_path, cross_weight, self_weight, randomize_comps, mincellsize, maxcellsize, target_comps, min_typ_global, soft_strength, nelements, n_descs, mask, rand_comp):
+ self.mask = mask # Generates the multiple structures
+ scores = [] # Initialize a list to store scores
+ print(f"Starting ensemble_score with {n_totconfig} configs.")
+ for i in range(1, n_totconfig + 1):
+ print(i, "/", n_totconfig, "Using indices:", mask)
+
+ if not randomize_comps:
+ g = internal_generate_cell(i, desired_size=vnp.random.choice(range(mincellsize, maxcellsize)), template=None, desired_comps=target_comps, use_template=None, min_typ=min_typ_global, soft_strength=soft_strength)
+ else:
+ target_comps_rnd = rand_comp(target_comps) # Randomize compositions
+ g = internal_generate_cell(i, desired_size=vnp.random.choice(range(mincellsize, maxcellsize)), template=None, desired_comps=target_comps_rnd, use_template=None, min_typ=min_typ_global, soft_strength=soft_strength)
+
+ em = GRSModel(nelements, n_descs, mask=mask)
+ sampler = GRSSampler(em, g)
+ em.K_cross = cross_weight
+ em.K_self = self_weight
+
+ # Run the minimization process
+ sampler.run("minimize 1e-6 1e-6 1000 10000")
+
+ # Write the data to a file
+ sampler.run("write_data %s/sample.%i.dat " % (data_path, i))
+
+ sampler.update_model() # Updating the model combines generated structures
+
+ # Calculate the score for the current configuration
+ score = self.get_score() # Assuming get_score is defined to return the score for the current state
+ if score is None:
+ print(f"Score for configuration {i} is None.")
+ scores.append(score) # Append the score to the list
+ print("Final scores list:", scores)
+ return scores # Return the list of scores
+"""
+""" def ensemble_score(self, n_totconfig, data_path,cross_weight, self_weight, randomize_comps, mincellsize, maxcellsize, target_comps, min_typ_global,soft_strength, nelements,n_descs,mask,rand_comp):
+ self.mask=mask#generates the multiple structures -- needs internal generate cell , some of these should be defined in the input file like n_totconfig if they choose multiple and the crossweight and self weigths
+ i = 1
+ while i <= n_totconfig:
+ print(i,"/",n_totconfig,"Using indicies :",mask)
+ if not randomize_comps:
+ g = internal_generate_cell(i,desired_size=vnp.random.choice(range(mincellsize,maxcellsize)),template=None,desired_comps=target_comps,use_template=None,min_typ=min_typ_global,soft_strength=soft_strength)
+ else:
+ target_comps_rnd = rand_comp(target_comps) #randomize_comp in input? and target_comps in input?
+ g = internal_generate_cell(i,desired_size=vnp.random.choice(range(mincellsize,maxcellsize)),template=None,desired_comps=target_comps_rnd,use_template=None,min_typ=min_typ_global,soft_strength=soft_strength)
+ em=GRSModel(nelements,n_descs,mask=mask)
+ sampler = GRSSampler(em, g)
+ em.K_cross = cross_weight
+ em.K_self = self_weight
+ #min type
+ sampler.run("minimize 1e-6 1e-6 1000 10000")
+
+ sampler.run("write_data %s/sample.%i.dat " % (data_path, i))
+
+ sampler.update_model() #updating the model is how all of the generated structures get combined into one.
+
+"""
+# total number of configurations
+#n_totconfig = int(sys.argv[2])
+ # self.lmp.command(line)
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index f288702..0000000
--- a/LICENSE
+++ /dev/null
@@ -1,674 +0,0 @@
- GNU GENERAL PUBLIC LICENSE
- Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc.
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
- Preamble
-
- The GNU General Public License is a free, copyleft license for
-software and other kinds of works.
-
- The licenses for most software and other practical works are designed
-to take away your freedom to share and change the works. By contrast,
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-share and change all versions of a program--to make sure it remains free
-software for all its users. We, the Free Software Foundation, use the
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-any other work released this way by its authors. You can apply it to
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diff --git a/Notes b/Notes
deleted file mode 100644
index 2290cd3..0000000
--- a/Notes
+++ /dev/null
@@ -1,72 +0,0 @@
-What are the unit features of this code?
- Convert between (xyz) and (D)
- Create random or designated structures (xyz)
- I/O of structures (xyz)
- Construct fictitious potential energy surfaces based on (D,D*)
- Single point (xyz, D) evaluation
-
-
-What would a user want from this code?
- Generate a set of structures that would constitute a training set for a given material.
- Reproduction of a large structure into a set of DFT-sized structures.
- 'Augment Existing' training set where holes are filled in
-
-Nomenclature:
-(xyz) is the cartesian representation of a structure
-(D) is the descriptor vector reprensentation of a structure
-(xyz*) and (D*) represent the target structure, or set of target structures
-
-------------------------------
-Information flow of an example run:
-
-Read in user-defined inputs from config file -> Stores as dictionary
-Read in target structure(s) as (xyz*) -> Stores as dictionary
-Convert target structure(s) from (xyz*) to (D*) -> Calls LAMMPS, returns npy array
-Calculate moments of (D*)
-
-Construct or read in starting structure -> Stores as dictionary
-Convert starting structure from (xyz) to (D) -> Calls LAMMPS, returns npy array
-
-Construct loss function (need to think on how to flexibly implement this, just define moments up to some point for now)
-
-One of three options now:
- 1)
-Begin optimization
-
-------------------------------
-Representative input file:
-
-[GENERATE] #This should override other sections if present, returns a set of stock structures
-composition = (A:0.5, B:0.5)
-count = #
-
-
-[TARGET]
-filetype = (lammps data, lammps dump, ase atoms, xyz)
-(take from Fitsnap groups section)
-stock structure / No target
-
-[MOTION]
-opt_type = GA, Minimize
-GA_accept = [0-1]
-GA_moves = (chemical, cell, position)
-GA_generations = #
-GA_populations = #
-MIN_repulsion = soft/zbl/other (can take from fitsnap reference potential)
-
-
-[BASIS]
-(take from fitsnap ACE section)
-
-[SCORING]
-moments = mean, std, skew, kurt
-moments_coeff = #,#,#,#
-single/ensemble
-structure count limit
-structure size limit
-perfect match bonus
-
-------------------------------
-
-Before running:
-export PYTHONPATH=${PYTHONPATH}:/home/mitwood/Documents/StructureGeneration/Fork:/home/mitwood/Documents/StructureGeneration/Fork/lib:/home/mitwood/Documents/FitSNAP3/master/
\ No newline at end of file
diff --git a/README.md b/README.md
index 20f6d5d..b368a36 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
FitSNAP - (post library update)
ASE - v 3.22.1
lammps - LAMMPS (post mliap update)
-
+
You must add the fitsnap folder to your PYTHONPATH in order to use
the ACE descriptor setup functionalities. Otherwise, you must bring your own
ACE descriptor file (.yace).
diff --git a/examples/EntropyMaximizing/EntropyLossFF.py b/examples/EntropyMaximizing/EntropyLossFF.py
deleted file mode 100644
index 07fdbb1..0000000
--- a/examples/EntropyMaximizing/EntropyLossFF.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from mpi4py import MPI
-from GRSlib.GRS import GRS
-import random, copy, os, glob, shutil
-import numpy as np
-
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-nprocs = comm.Get_size()
-
-settings = \
-{
-"BASIS":
- {
- "descriptor": "ACE",
- "numTypes": 1,
- "elements": "W",
- "rcutfac": 5.5,
- "lambda": 1.4,
- "ranks": "1 2 3",
- "lmax": "0 3 3",
- "lmin": "0 0 0",
- "nmax": "8 1 1",
- "nmaxbase": 8,
- "bzeroflag": 0
- },
-"SCORING":
- {
- "score_type": "entropy",
- "internal_entropy": 1.0,
- "ensemble_entropy": 1.0,
- "strength_target": 0.0, # Positive value forces atoms *toward* target
- "strength_prior": -1.0 # Negative value forces atoms *away* from prior
- },
-"TARGET":
- {
-# "target_fname": "bcc.data",
- "target_fdesc": "bcc.npy",
- "start_fname": "notbcc.data",
- "job_prefix": "EntMax"
- },
-"MOTION":
- {
- "soft_strength": 0.5,
- "ml_strength": 1.0,
- "nsteps": 10000,
- "temperature": 0.0,
- "min_type": "line",
- "randomize_comps": False
- }
-}
-
-grs = GRS(settings,comm=comm)
-
-updated_struct = settings["TARGET"]["start_fname"]
-grs.set_prior([updated_struct])
-
-for i in range(10):
- updated_struct = grs.gradient_move(updated_struct)
- updated_struct = grs.update_start(updated_struct,"MaxScore")
- grs.set_prior(glob.glob(settings['TARGET']["job_prefix"]+"*.data"))
-
-exit()
-
diff --git a/examples/EntropyMaximizing/bcc.npy b/examples/EntropyMaximizing/bcc.npy
deleted file mode 100644
index dcc52c4..0000000
Binary files a/examples/EntropyMaximizing/bcc.npy and /dev/null differ
diff --git a/examples/EntropyMaximizing/notbcc.data b/examples/EntropyMaximizing/notbcc.data
deleted file mode 100644
index 0d812b2..0000000
--- a/examples/EntropyMaximizing/notbcc.data
+++ /dev/null
@@ -1,70 +0,0 @@
-# LAMMPS data file written by OVITO Pro 3.10.6
-
-54 atoms
-1 atom types
-
--3.1804 6.3608 xlo xhi
--3.1804 6.3608 ylo yhi
--3.1804 6.3608 zlo zhi
-
-Masses
-
-1 183.83
-
-Atoms # atomic
-
-1 1 -2.1804 -3.1804 -3.1804
-2 1 -2.5902 -1.5902 -1.5902
-3 1 -3.1804 -3.1804 0.0
-4 1 -2.5902 -1.5902 1.5902
-5 1 -3.1804 -3.1804 3.1804
-6 1 -1.5902 -1.5902 4.7706
-7 1 -1.1804 0.0 -3.1804
-8 1 -1.5902 1.5902 -1.5902
-9 1 -3.1804 0.0 0.0
-10 1 -1.5902 1.5902 1.5902
-11 1 -2.1804 0.0 3.1804
-12 1 -1.5902 1.5902 4.7706
-13 1 -3.1804 3.1804 -3.1804
-14 1 -1.5902 4.7706 -1.5902
-15 1 -3.1804 3.1804 0.0
-16 1 -1.5902 4.7706 1.5902
-17 1 -3.1804 3.1804 3.1804
-18 1 -1.5902 4.7706 4.7706
-19 1 0.0 -3.1804 -3.1804
-20 1 1.5902 -1.5902 -1.5902
-21 1 0.0 -3.1804 0.0
-22 1 1.5902 -1.5902 1.5902
-23 1 0.0 -3.1804 3.1804
-24 1 1.5902 -1.5902 4.7706
-25 1 0.0 0.0 -3.1804
-26 1 1.5902 1.5902 -1.5902
-27 1 0.0 0.0 0.0
-28 1 1.5902 1.5902 1.5902
-29 1 0.0 0.0 3.1804
-30 1 1.5902 1.5902 4.7706
-31 1 0.0 3.1804 -3.1804
-32 1 1.5902 4.7706 -1.5902
-33 1 0.0 3.1804 0.0
-34 1 1.5902 4.7706 1.5902
-35 1 0.0 3.1804 3.1804
-36 1 1.5902 4.7706 4.7706
-37 1 3.1804 -3.1804 -3.1804
-38 1 4.7706 -1.5902 -1.5902
-39 1 3.1804 -3.1804 0.0
-40 1 4.7706 -1.5902 1.5902
-41 1 3.1804 -3.1804 3.1804
-42 1 4.7706 -1.5902 4.7706
-43 1 3.1804 0.0 -3.1804
-44 1 4.7706 1.5902 -1.5902
-45 1 3.1804 0.0 0.0
-46 1 4.7706 1.5902 1.5902
-47 1 3.1804 0.0 3.1804
-48 1 4.7706 1.5902 4.7706
-49 1 3.1804 3.1804 -3.1804
-50 1 4.7706 4.7706 -1.5902
-51 1 3.1804 3.1804 0.0
-52 1 4.7706 4.7706 1.5902
-53 1 3.1804 3.1804 3.1804
-54 1 4.7706 4.7706 4.7706
-
diff --git a/examples/README.md b/examples/README.md
deleted file mode 100644
index 5db975d..0000000
--- a/examples/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
-#examples
-
-### SimpleGradient_BCC
-Uses the moments of ACE descriptor distributions to construct the loss function used by LAMMPS.
-Energy (loss) minimization is performed by LAMMPS taking a defective BCC structure back to BCC.
-
-### EntropyMaximizing
-Constructs a loss function based on the information entropy of descriptors contained within a structure.
-This loss function (energy) is used by LAMMPS to find structures that maximize the internal information entropy.
-
diff --git a/examples/SimpleGradient_BCC/SimpleGradient.py b/examples/SimpleGradient_BCC/SimpleGradient.py
deleted file mode 100644
index fbadcda..0000000
--- a/examples/SimpleGradient_BCC/SimpleGradient.py
+++ /dev/null
@@ -1,57 +0,0 @@
-from mpi4py import MPI
-from GRSlib.GRS import GRS
-import random, copy, os, glob, shutil
-import numpy as np
-
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-nprocs = comm.Get_size()
-
-settings = \
-{
-"BASIS":
- {
- "descriptor": "ACE",
- "numTypes": 1,
- "elements": "W",
- "rcutfac": 5.5,
- "lambda": 1.4,
- "ranks": "1 2 3",
- "lmax": "0 3 3",
- "lmin": "0 0 0",
- "nmax": "8 1 1",
- "nmaxbase": 8,
- "bzeroflag": 0
- },
-"SCORING":
- {
- "moments": "mean stdev" ,
- "moments_coeff": "1.0 0.1",
- "strength_target": 1.0, #Positive number attracts structures to target, negative away from
- "strength_prior": 0.0 #Positive number attracts structures to prior, negative away from
- },
-"TARGET":
- {
- "target_fname": "bcc.data", #54 atom cell of perfect BCC crystal
- "start_fname": "notbcc.data", #54 atom cell where atoms have been displaced away from BCC
- "job_prefix": "GRSOutput"
- },
-"MOTION":
- {
- "soft_strength": 0.5,
- "ml_strength": 1.0,
- "nsteps": 10000,
- "min_type": "line"
- }
-}
-
-grs = GRS(settings,comm=comm)
-
-updated_struct = settings["TARGET"]["start_fname"] # set your starting point
-grs.set_prior([updated_struct]) # set the prior descriptors to your starting point so you can 'push' away
- # from the starting point with a non-zero strength_prior.
-
-for i in range(10):
- updated_struct = grs.gradient_move(updated_struct) #Take a gradient move of the structure
- updated_struct = grs.update_start(updated_struct,"MinScore") # Check to see if the score improved
- grs.set_prior(glob.glob(settings['TARGET']["job_prefix"]+"*")) #Update prior descriptors with structures generated by the gradient move
diff --git a/examples/SimpleGradient_BCC/bcc.data b/examples/SimpleGradient_BCC/bcc.data
deleted file mode 100644
index 531b4b5..0000000
--- a/examples/SimpleGradient_BCC/bcc.data
+++ /dev/null
@@ -1,70 +0,0 @@
-# LAMMPS data file written by OVITO Pro 3.10.6
-
-54 atoms
-1 atom types
-
--3.1804 6.3608 xlo xhi
--3.1804 6.3608 ylo yhi
--3.1804 6.3608 zlo zhi
-
-Masses
-
-1 183.83
-
-Atoms # atomic
-
-1 1 -3.1804 -3.1804 -3.1804
-2 1 -1.5902 -1.5902 -1.5902
-3 1 -3.1804 -3.1804 0.0
-4 1 -1.5902 -1.5902 1.5902
-5 1 -3.1804 -3.1804 3.1804
-6 1 -1.5902 -1.5902 4.7706
-7 1 -3.1804 0.0 -3.1804
-8 1 -1.5902 1.5902 -1.5902
-9 1 -3.1804 0.0 0.0
-10 1 -1.5902 1.5902 1.5902
-11 1 -3.1804 0.0 3.1804
-12 1 -1.5902 1.5902 4.7706
-13 1 -3.1804 3.1804 -3.1804
-14 1 -1.5902 4.7706 -1.5902
-15 1 -3.1804 3.1804 0.0
-16 1 -1.5902 4.7706 1.5902
-17 1 -3.1804 3.1804 3.1804
-18 1 -1.5902 4.7706 4.7706
-19 1 0.0 -3.1804 -3.1804
-20 1 1.5902 -1.5902 -1.5902
-21 1 0.0 -3.1804 0.0
-22 1 1.5902 -1.5902 1.5902
-23 1 0.0 -3.1804 3.1804
-24 1 1.5902 -1.5902 4.7706
-25 1 0.0 0.0 -3.1804
-26 1 1.5902 1.5902 -1.5902
-27 1 0.0 0.0 0.0
-28 1 1.5902 1.5902 1.5902
-29 1 0.0 0.0 3.1804
-30 1 1.5902 1.5902 4.7706
-31 1 0.0 3.1804 -3.1804
-32 1 1.5902 4.7706 -1.5902
-33 1 0.0 3.1804 0.0
-34 1 1.5902 4.7706 1.5902
-35 1 0.0 3.1804 3.1804
-36 1 1.5902 4.7706 4.7706
-37 1 3.1804 -3.1804 -3.1804
-38 1 4.7706 -1.5902 -1.5902
-39 1 3.1804 -3.1804 0.0
-40 1 4.7706 -1.5902 1.5902
-41 1 3.1804 -3.1804 3.1804
-42 1 4.7706 -1.5902 4.7706
-43 1 3.1804 0.0 -3.1804
-44 1 4.7706 1.5902 -1.5902
-45 1 3.1804 0.0 0.0
-46 1 4.7706 1.5902 1.5902
-47 1 3.1804 0.0 3.1804
-48 1 4.7706 1.5902 4.7706
-49 1 3.1804 3.1804 -3.1804
-50 1 4.7706 4.7706 -1.5902
-51 1 3.1804 3.1804 0.0
-52 1 4.7706 4.7706 1.5902
-53 1 3.1804 3.1804 3.1804
-54 1 4.7706 4.7706 4.7706
-
diff --git a/examples/SimpleGradient_BCC/notbcc.data b/examples/SimpleGradient_BCC/notbcc.data
deleted file mode 100644
index 0d812b2..0000000
--- a/examples/SimpleGradient_BCC/notbcc.data
+++ /dev/null
@@ -1,70 +0,0 @@
-# LAMMPS data file written by OVITO Pro 3.10.6
-
-54 atoms
-1 atom types
-
--3.1804 6.3608 xlo xhi
--3.1804 6.3608 ylo yhi
--3.1804 6.3608 zlo zhi
-
-Masses
-
-1 183.83
-
-Atoms # atomic
-
-1 1 -2.1804 -3.1804 -3.1804
-2 1 -2.5902 -1.5902 -1.5902
-3 1 -3.1804 -3.1804 0.0
-4 1 -2.5902 -1.5902 1.5902
-5 1 -3.1804 -3.1804 3.1804
-6 1 -1.5902 -1.5902 4.7706
-7 1 -1.1804 0.0 -3.1804
-8 1 -1.5902 1.5902 -1.5902
-9 1 -3.1804 0.0 0.0
-10 1 -1.5902 1.5902 1.5902
-11 1 -2.1804 0.0 3.1804
-12 1 -1.5902 1.5902 4.7706
-13 1 -3.1804 3.1804 -3.1804
-14 1 -1.5902 4.7706 -1.5902
-15 1 -3.1804 3.1804 0.0
-16 1 -1.5902 4.7706 1.5902
-17 1 -3.1804 3.1804 3.1804
-18 1 -1.5902 4.7706 4.7706
-19 1 0.0 -3.1804 -3.1804
-20 1 1.5902 -1.5902 -1.5902
-21 1 0.0 -3.1804 0.0
-22 1 1.5902 -1.5902 1.5902
-23 1 0.0 -3.1804 3.1804
-24 1 1.5902 -1.5902 4.7706
-25 1 0.0 0.0 -3.1804
-26 1 1.5902 1.5902 -1.5902
-27 1 0.0 0.0 0.0
-28 1 1.5902 1.5902 1.5902
-29 1 0.0 0.0 3.1804
-30 1 1.5902 1.5902 4.7706
-31 1 0.0 3.1804 -3.1804
-32 1 1.5902 4.7706 -1.5902
-33 1 0.0 3.1804 0.0
-34 1 1.5902 4.7706 1.5902
-35 1 0.0 3.1804 3.1804
-36 1 1.5902 4.7706 4.7706
-37 1 3.1804 -3.1804 -3.1804
-38 1 4.7706 -1.5902 -1.5902
-39 1 3.1804 -3.1804 0.0
-40 1 4.7706 -1.5902 1.5902
-41 1 3.1804 -3.1804 3.1804
-42 1 4.7706 -1.5902 4.7706
-43 1 3.1804 0.0 -3.1804
-44 1 4.7706 1.5902 -1.5902
-45 1 3.1804 0.0 0.0
-46 1 4.7706 1.5902 1.5902
-47 1 3.1804 0.0 3.1804
-48 1 4.7706 1.5902 4.7706
-49 1 3.1804 3.1804 -3.1804
-50 1 4.7706 4.7706 -1.5902
-51 1 3.1804 3.1804 0.0
-52 1 4.7706 4.7706 1.5902
-53 1 3.1804 3.1804 3.1804
-54 1 4.7706 4.7706 4.7706
-
diff --git a/testing/AttractorTarget.py b/testing/AttractorTarget.py
deleted file mode 100644
index fcbe2ee..0000000
--- a/testing/AttractorTarget.py
+++ /dev/null
@@ -1,79 +0,0 @@
-from mpi4py import MPI
-from GRSlib.GRS import GRS
-import random, copy, os, glob, shutil
-import numpy as np
-
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-nprocs = comm.Get_size()
-
-settings = \
-{
-"BASIS":
- {
- "descriptor": "ACE",
- "numTypes": 1,
- "elements": "W",
- "rcutfac": 5.5,
- "lambda": 1.4,
- "ranks": "1 2 3",
- "lmax": "0 3 3",
- "lmin": "0 0 0",
- "nmax": "8 1 1",
- "nmaxbase": 8,
- "bzeroflag": 0
- },
-"SCORING":
- {
- "score_type": "moments",
- "strength_target": 1.0,
- "strength_prior": 0.0,
- "moments": "mean stdev" ,
- "moments_coeff": "1.0 0.1",
- "moments_bonus": "0 0" ,
- },
-"TARGET":
- {
- "target_fname": "bcc.data",
- "target_fdesc": "bcc.npy",
- "start_fname": "notbcc.data",
- "job_prefix": "TrialGRS"
- },
-"MOTION":
- {
- "soft_strength": 0.5,
- "ml_strength": 1.0,
- "nsteps": 10000,
- "temperature": 0.0,
- "min_type": "line",
- "randomize_comps": False
- }
-}
-
-grs = GRS(settings,comm=comm)
-
-#testing of io class
-#grs.config.view_state()
-#-----------------------
-
-#testing of convert class
-#attributes = [attr for attr in dir(grs.convert) if not attr.startswith('__')]
-#print("attr of grs.convert:")
-#print(attributes)
-#current_desc = grs.convert_to_desc('bcc.data')
-#grs.genetic_move.tournament_selection(data=None)
-
-#score = grs.get_score(settings["TARGET"]["start_fname"])
-#print(" Score calculated through LAMMPS:",score)
-#print("Done checking socring!")
-
-updated_struct = settings["TARGET"]["start_fname"]
-grs.set_prior([updated_struct])
-
-for i in range(10):
-# grs.update_prior()
- updated_struct = grs.gradient_move(updated_struct)
- updated_struct = grs.update_start(updated_struct,"MinScore")
- grs.set_prior(glob.glob(settings['TARGET']["job_prefix"]+"*.data"))
-
-exit()
diff --git a/testing/ClassInheritance.py b/testing/ClassInheritance.py
deleted file mode 100644
index 4d238c9..0000000
--- a/testing/ClassInheritance.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from mpi4py import MPI
-from GRSlib.GRS import GRS
-import random, copy, os, glob, shutil
-import numpy as np
-
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-nprocs = comm.Get_size()
-
-settings = \
-{
-"BASIS":
- {
- "descriptor": "ACE",
- "numTypes": 1,
- "elements": "W",
- "rcutfac": 5.5,
- "lambda": 1.4,
- "ranks": "1 2 3",
- "lmax": "0 3 3",
- "lmin": "0 0 0",
- "nmax": "8 1 1",
- "nmaxbase": 8,
- "bzeroflag": 0
- },
-"SCORING":
- {
- "score_type": "moments",
- "strength_target": 1.0,
- "strength_prior": 0.0,
- "moments": "mean stdev" ,
- "moments_coeff": "1.0 0.0",
- "moments_bonus": "0 0" ,
- },
-"TARGET":
- {
- "target_fname": "bcc.data",
-# "target_fdesc": "fcc.npy",
- "start_fname": "notbcc.data",
- "job_prefix": "TrialGRS"
- },
-"MOTION":
- {
- "soft_strength": 0.5,
- "ml_strength": 1.0,
- "nsteps": 10000,
- "temperature": 0.0,
- "min_type": "line",
- "randomize_comps": False
- }
-}
-
-grs = GRS(settings,comm=comm)
-
-#grs.config.view_state()
-
-#attributes = [attr for attr in dir(grs.gradient_move) if not attr.startswith('__')]
-#print("attr of grs.gradient_move:")
-#print(attributes)
-
-#starting_struc = grs.convert_to_desc(settings["TARGET"]["start_fname"])
-
-score = grs.get_score(settings["TARGET"]["start_fname"])
-print(" Score calculated through LAMMPS:",score)
-
-#updated_struct = settings["TARGET"]["start_fname"]
-#grs.set_prior([updated_struct])
-#print("Updated Prior")
-#updated_struct = grs.gradient_move(updated_struct)
-#print("Made a single gradient move")
-#updated_struct = grs.update_start(updated_struct,"MinScore")
-#print("Updated starting structure")
-#grs.set_prior(glob.glob(settings['TARGET']["job_prefix"]+"*.data"))
-#print("Updated Prior")
-
-exit()
diff --git a/testing/EntropyLossFF.py b/testing/EntropyLossFF.py
deleted file mode 100644
index 0db6118..0000000
--- a/testing/EntropyLossFF.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from mpi4py import MPI
-from GRSlib.GRS import GRS
-import random, copy, os, glob, shutil
-import numpy as np
-
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-nprocs = comm.Get_size()
-
-settings = \
-{
-"BASIS":
- {
- "descriptor": "ACE",
- "numTypes": 1,
- "elements": "W",
- "rcutfac": 5.5,
- "lambda": 1.4,
- "ranks": "1 2 3",
- "lmax": "0 3 3",
- "lmin": "0 0 0",
- "nmax": "8 1 1",
- "nmaxbase": 8,
- "bzeroflag": 0
- },
-"SCORING":
- {
- "score_type": "entropy",
- "internal_entropy": 1.0,
- "ensemble_entropy": 1.0,
- "strength_target": 0.0,
- "strength_prior": -1.0
- },
-"TARGET":
- {
- "target_fname": "bcc.data",
-# "target_fdesc": "fcc.npy",
- "start_fname": "notbcc.data",
- "job_prefix": "GetENT"
- },
-"MOTION":
- {
- "soft_strength": 0.5,
- "ml_strength": 1.0,
- "nsteps": 10000,
- "temperature": 0.0,
- "min_type": "line",
- "randomize_comps": False
- }
-}
-
-grs = GRS(settings,comm=comm)
-
-#grs.config.view_state()
-
-#attributes = [attr for attr in dir(grs.gradient_move) if not attr.startswith('__')]
-#print("attr of grs.gradient_move:")
-#print(attributes)
-
-#starting_struc = grs.convert_to_desc(settings["TARGET"]["start_fname"])
-
-#score = grs.get_score(settings["TARGET"]["start_fname"])
-#print(" Score calculated through LAMMPS:",score)
-
-updated_struct = settings["TARGET"]["start_fname"]
-grs.set_prior([updated_struct])
-
-for i in range(10):
- updated_struct = grs.gradient_move(updated_struct)
- updated_struct = grs.update_start(updated_struct,"MaxScore")
- grs.set_prior(glob.glob(settings['TARGET']["job_prefix"]+"*.data"))
-
-exit()
-
-exit()
diff --git a/testing/FS_HONC-ACE.tmp b/testing/FS_HONC-ACE.tmp
deleted file mode 100644
index eafdd47..0000000
--- a/testing/FS_HONC-ACE.tmp
+++ /dev/null
@@ -1,284 +0,0 @@
-"""
-Python script using library API to:
-- Loop over all configurations in parallel and perform the transpose trick C = A^T * A and d = A^T * b.
-- Sum these `C` and `d` arrays for all configurations.
-- Perform least squares (or ridge regression) fit.
-
-Usage:
-
- mpirun -np P python example.py
-
-Afterwards, use the `in.run` LAMMPS script to run MD with:
-
- mpirun -np P lmp -in in.run
-
-NOTE: This workflow is under development and therefore script requires changes.
-
-- `settings` variable can be a dictionary like the example provided, or path to a fitsnap input script.
-- `alval`: Ridge regression regularization parameter.
-- Comment or uncomment `least_squares` or `ridge` at end of script to choose fitting method.
-
-"""
-
-from time import time
-from mpi4py import MPI
-from fitsnap3lib.fitsnap import FitSnap
-import numpy as np
-from scipy.linalg import lstsq
-from sys import float_info as fi
-from sklearn.linear_model import Ridge
-
-def least_squares(c, d):
- """
- Normal least squares fit.
- """
- coeffs, residues, rank, s = lstsq(c, d, 1.0e-13)
- return coeffs
-
-def ridge(c, d):
- """
- Least squares fit with ridge regularization.
- """
- alval = 1.e-7
- reg = Ridge(alpha = alval, fit_intercept = False)
- reg.fit(c, d)
- return reg.coef_.T # return transpose if using sklearn ridge
-
-def error_analysis(instance):
- """
- Calculate errors associated with a fitsnap instance that does not have shared arrays for the
- entire A matrix or b vector, e.g. like we have when doing transpose trick. Here we loop over
- all configurations and accumulate errors one at a time.
-
- Args:
- instance: fitsnap instance that contains a valid `fit`.
-
- Prints total MAE for all configurations in the data set.
- TODO: Organize this to calculate group errors or other kinds of errors.
- """
-
- # Get total number of atoms and configs across all procs for calculating average errors.
- nconfigs_all = len(instance.pt.shared_arrays["number_of_atoms"].array)
- natoms_all = instance.pt.shared_arrays["number_of_atoms"].array.sum()
-
- energy_mae = 0.0
- force_mae = 0.0
- stress_mae = 0.0
- for i, configuration in enumerate(instance.data):
- # TODO: Add option to print descriptor calculation progress on single proc.
- # if (i % 1 == 0):
- # self.pt.single_print(i)
- a,b,w = instance.calculator.process_single(configuration, i)
- aw, bw = w[:, np.newaxis] * a, w * b
-
- pred = a @ coeffs
-
- # Energy error.
- energy = pred[0,0]
- ediff = np.abs(energy-b[0])
- energy_mae += ediff/nconfigs_all
-
- # Force error.
- ndim_force = 3
- nrows_force = ndim_force * configuration['NumAtoms']
- force = pred[1:nrows_force+1,0]
- force_mae += np.sum(abs(force-b[1:nrows_force+1]))/(3*natoms_all)
-
- # Stress error.
- stress = pred[-6:,0]
- sdiff_mae = np.mean(np.abs(stress-b[-6:]))
- stress_mae += sdiff_mae/nconfigs_all
- # Good practice after a large parallel operation is to impose a barrier to wait for all procs to complete.
- instance.pt.all_barrier()
-
- # Reduce errors across procs.
- energy_mae = np.array([energy_mae])
- force_mae = np.array([force_mae])
- stress_mae = np.array([stress_mae])
- energy_mae_all = np.array([0.0])
- force_mae_all = np.array([0.0])
- stress_mae_all = np.array([0.0])
- comm.Allreduce([energy_mae, MPI.DOUBLE], [energy_mae_all, MPI.DOUBLE])
- comm.Allreduce([force_mae, MPI.DOUBLE], [force_mae_all, MPI.DOUBLE])
- comm.Allreduce([stress_mae, MPI.DOUBLE], [stress_mae_all, MPI.DOUBLE])
-
- # Print errors.
- if (rank==0):
- print(energy_mae_all[0])
- print(force_mae_all[0])
- print(stress_mae_all[0])
-
-
-# Declare a communicator (this can be a custom communicator as well).
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-nprocs = comm.Get_size()
-
-if rank == 0:
- start = time()
-
-# Create an input dictionary containing settings.
-settings = \
-{
-"ACE":
- {
- "numTypes": 4,
- "rcutfac": "rcut1 rcut2 rcut3 rcut4 rcut5 rcut6 rcut7 rcut8 rcut9 rcut10 rcut11 rcut12 rcut13 rcut14 rcut15 rcut16 ",
- "lambda": "lam1 lam2 lam3 lam4 lam5 lam6 lam7 lam8 lam9 lam10 lam11 lam12 lam13 lam14 lam15 lam16 ",
- "rcinner": "0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050",
- "drcinner": "0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01",
- "ranks": "1 2 3",
- "lmax": "1 6 2",
- "nmax": "18 4 2",
- "mumax": 4,
- "nmaxbase": 18,
- "type": "C H N O",
- "lmin": "0 0 1",
- "bzeroflag": 0
- },
-"CALCULATOR":
- {
- "calculator": "LAMMPSPACE",
- "energy": 1,
- "force": 1,
- "stress": 0
- },
-"ESHIFT":
- {
- "C": 0.0,
- "H": 0.0,
- "N": 0.0,
- "O": 0.0
- },
-"SOLVER":
- {
- "solver": "RIDGE",
- "compute_testerrs": 1,
- "detailed_errors": 1
- },
-"RIDGE":
- {
- "alpha": 1.0E-7
- },
-"SCRAPER":
- {
- "scraper": "XYZ"
- },
-"PATH":
- {
- "dataPath": "/nscratch/mitwood/ASC_PEM/CP2K/CL20/TrainingSet/MaxForce5_TrainingData_19Aug24/"
- },
-"OUTFILE":
- {
- "output_style": "PACE",
- "metrics": "HONC_metrics.md",
- "potential": "HONC_pot"
- },
-"REFERENCE":
- {
- "units": "metal",
- "atom_style": "atomic",
- "pair_style": "zero 7.01",
- "pair_coeff": "* *"
- },
-"EXTRAS":
- {
- "dump_descriptors": 0,
- "dump_truth": 0,
- "dump_weights": 0,
- "dump_dataframe": 0,
- "multinode_testing": 1
- },
-"GROUPS":
- {
- "group_sections": "name training_size testing_size eweight fweight",
- "group_types": "str float float float float",
- "smartweights": 1,
- "random_sampling": 1,
- "AllTemps_1atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_10001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_20001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_30001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_40001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_50001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_60001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_70001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_80001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_90001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_100001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_110001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_120001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_130001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AllTemps_140001atm_ReaxPressureCooker" : "0.5 0.0 EW1 FW1 ",
- "AlphaEOS" : "0.4 0.0 EW2 FW2 ",
- "BetaEOS" : "0.13 0.0 EW2 FW2 ",
- "EpsillonEOS" : "0.5 0.0 EW3 FW3 ",
- "GammaEOS" : "0.008 0.0 EW2 FW2 ",
- "ZetaEOS" : "0.18 0.0 EW2 FW2 ",
- "SmallMols" : "1.0 0.0 EW2 FW2 ",
- "TotalCyclicReaxSnapshots" : "1.0 0.0 EW2 FW2 "
- },
-"MEMORY":
- {
- "override": 1
- }
-}
-
-# Alternatively, settings could be provided in a traditional input file:
-#settings = "../../Ta_Linear_JCP2014/Ta-example.in"
-
-# Create a FitSnap instance using the communicator and settings:
-fs = FitSnap(settings, comm=comm, arglist=["--overwrite"])
-
-# Scrape configurations to create and populate the `snap.data` list of dictionaries with structural info.
-fs.scrape_configs()
-
-# Allocate `C` and `d` fitting arrays.
-a_width = fs.calculator.get_width()
-c = np.zeros((a_width,a_width)) # This will also include weights.
-d = np.zeros((a_width,1))
-
-# Create fitsnap dictionaries (optional if you want access to distributed lists of groups, etc.)
-fs.calculator.create_dicts(len(fs.data))
-# Create `C` and `d` arrays for solving lstsq with transpose trick.
-a_width = fs.calculator.get_width()
-c = np.zeros((a_width,a_width))
-d = np.zeros((a_width,1))
-c_all = np.zeros((a_width,a_width))
-d_all = np.zeros((a_width,1))
-if rank==0:
- print(f">>> Looping over {len(fs.data)} structures.")
-for i, configuration in enumerate(fs.data):
- # TODO: Add option to print descriptor calculation progress on single proc.
- if (i % 10 == 0):
- fs.pt.single_print(i)
- a,b,w = fs.calculator.process_single(configuration, i)
- aw, bw = w[:, np.newaxis] * a, w * b
-
- cm = np.matmul(np.transpose(aw), aw)
- dm = np.matmul(np.transpose(aw), bw[:,np.newaxis])
- c += cm
- d += dm
-# Good practice after a large parallel operation is to impose a barrier to wait for all procs to complete.
-fs.pt.all_barrier()
-
-# Reduce C and D arrays across procs.
-comm.Allreduce([c, MPI.DOUBLE], [c_all, MPI.DOUBLE])
-comm.Allreduce([d, MPI.DOUBLE], [d_all, MPI.DOUBLE])
-
-if rank == 0:
- # Perform least squares fit.
- #coeffs = least_squares(c_all,d_all)
- coeffs = ridge(c_all, d_all)
- # Now `coeffs` is owned by all procs, good for parallel error analysis.
-
- # Calculate errors for this instance (not required).
- # error_analysis(fitsnap)
-
- # Write LAMMPS files.
- # NOTE: Without error analysis, `fitsnap.solver.errors` is an empty list and will not be written to file.
- fs.output.output(coeffs, fs.solver.errors)
-
- end = time()
- sec = round(end-start,3)
- print(f"Time to complete fit: {sec} s")
diff --git a/testing/Summary.dat b/testing/Summary.dat
deleted file mode 100644
index 2d16508..0000000
--- a/testing/Summary.dat
+++ /dev/null
@@ -1 +0,0 @@
--nan -nan 8.88947367255218e+251 54
diff --git a/testing/TwoAtoms.data b/testing/TwoAtoms.data
deleted file mode 100644
index dfaf9ec..0000000
--- a/testing/TwoAtoms.data
+++ /dev/null
@@ -1,17 +0,0 @@
-# LAMMPS data file written by OVITO Pro 3.10.6
-
-2 atoms
-1 atom types
-
-0.0000 3.1804 xlo xhi
-0.0000 3.1804 ylo yhi
-0.0000 3.1804 zlo zhi
-
-Masses
-
-1 183.83
-
-Atoms # atomic
-
-1 1 0.000 0.000 0.000
-2 1 1.5902 1.5902 1.5902
diff --git a/testing/W54.lammps-data b/testing/W54.lammps-data
deleted file mode 100644
index c79382d..0000000
--- a/testing/W54.lammps-data
+++ /dev/null
@@ -1,69 +0,0 @@
-(written by ASE)
-
-54 atoms
-1 atom types
-
-0.0 9.5411999999999999 xlo xhi
-0.0 9.5411999999999999 ylo yhi
-0.0 9.5411999999999999 zlo zhi
-
-Masses
-
- 1 183.82999999999998 # W
-
-Atoms # atomic
-
- 1 1 -3.1804000000000001 -3.1804000000000001 -3.1804000000000001
- 2 1 -1.5902000000000001 -1.5902000000000001 -1.5902000000000001
- 3 1 -3.1804000000000001 -3.1804000000000001 0
- 4 1 -1.5902000000000001 -1.5902000000000001 1.5902000000000001
- 5 1 -3.1804000000000001 -3.1804000000000001 3.1804000000000001
- 6 1 -1.5902000000000001 -1.5902000000000001 4.7706
- 7 1 -3.1804000000000001 0 -3.1804000000000001
- 8 1 -1.5902000000000001 1.5902000000000001 -1.5902000000000001
- 9 1 -3.1804000000000001 0 0
- 10 1 -1.5902000000000001 1.5902000000000001 1.5902000000000001
- 11 1 -3.1804000000000001 0 3.1804000000000001
- 12 1 -1.5902000000000001 1.5902000000000001 4.7706
- 13 1 -3.1804000000000001 3.1804000000000001 -3.1804000000000001
- 14 1 -1.5902000000000001 4.7706 -1.5902000000000001
- 15 1 -3.1804000000000001 3.1804000000000001 0
- 16 1 -1.5902000000000001 4.7706 1.5902000000000001
- 17 1 -3.1804000000000001 3.1804000000000001 3.1804000000000001
- 18 1 -1.5902000000000001 4.7706 4.7706
- 19 1 0 -3.1804000000000001 -3.1804000000000001
- 20 1 1.5902000000000001 -1.5902000000000001 -1.5902000000000001
- 21 1 0 -3.1804000000000001 0
- 22 1 1.5902000000000001 -1.5902000000000001 1.5902000000000001
- 23 1 0 -3.1804000000000001 3.1804000000000001
- 24 1 1.5902000000000001 -1.5902000000000001 4.7706
- 25 1 0 0 -3.1804000000000001
- 26 1 1.5902000000000001 1.5902000000000001 -1.5902000000000001
- 27 1 0 0 0
- 28 1 1.5902000000000001 1.5902000000000001 1.5902000000000001
- 29 1 0 0 3.1804000000000001
- 30 1 1.5902000000000001 1.5902000000000001 4.7706
- 31 1 0 3.1804000000000001 -3.1804000000000001
- 32 1 1.5902000000000001 4.7706 -1.5902000000000001
- 33 1 0 3.1804000000000001 0
- 34 1 1.5902000000000001 4.7706 1.5902000000000001
- 35 1 0 3.1804000000000001 3.1804000000000001
- 36 1 1.5902000000000001 4.7706 4.7706
- 37 1 3.1804000000000001 -3.1804000000000001 -3.1804000000000001
- 38 1 4.7706 -1.5902000000000001 -1.5902000000000001
- 39 1 3.1804000000000001 -3.1804000000000001 0
- 40 1 4.7706 -1.5902000000000001 1.5902000000000001
- 41 1 3.1804000000000001 -3.1804000000000001 3.1804000000000001
- 42 1 4.7706 -1.5902000000000001 4.7706
- 43 1 3.1804000000000001 0 -3.1804000000000001
- 44 1 4.7706 1.5902000000000001 -1.5902000000000001
- 45 1 3.1804000000000001 0 0
- 46 1 4.7706 1.5902000000000001 1.5902000000000001
- 47 1 3.1804000000000001 0 3.1804000000000001
- 48 1 4.7706 1.5902000000000001 4.7706
- 49 1 3.1804000000000001 3.1804000000000001 -3.1804000000000001
- 50 1 4.7706 4.7706 -1.5902000000000001
- 51 1 3.1804000000000001 3.1804000000000001 0
- 52 1 4.7706 4.7706 1.5902000000000001
- 53 1 3.1804000000000001 3.1804000000000001 3.1804000000000001
- 54 1 4.7706 4.7706 4.7706
diff --git a/testing/bcc.data b/testing/bcc.data
deleted file mode 100644
index 531b4b5..0000000
--- a/testing/bcc.data
+++ /dev/null
@@ -1,70 +0,0 @@
-# LAMMPS data file written by OVITO Pro 3.10.6
-
-54 atoms
-1 atom types
-
--3.1804 6.3608 xlo xhi
--3.1804 6.3608 ylo yhi
--3.1804 6.3608 zlo zhi
-
-Masses
-
-1 183.83
-
-Atoms # atomic
-
-1 1 -3.1804 -3.1804 -3.1804
-2 1 -1.5902 -1.5902 -1.5902
-3 1 -3.1804 -3.1804 0.0
-4 1 -1.5902 -1.5902 1.5902
-5 1 -3.1804 -3.1804 3.1804
-6 1 -1.5902 -1.5902 4.7706
-7 1 -3.1804 0.0 -3.1804
-8 1 -1.5902 1.5902 -1.5902
-9 1 -3.1804 0.0 0.0
-10 1 -1.5902 1.5902 1.5902
-11 1 -3.1804 0.0 3.1804
-12 1 -1.5902 1.5902 4.7706
-13 1 -3.1804 3.1804 -3.1804
-14 1 -1.5902 4.7706 -1.5902
-15 1 -3.1804 3.1804 0.0
-16 1 -1.5902 4.7706 1.5902
-17 1 -3.1804 3.1804 3.1804
-18 1 -1.5902 4.7706 4.7706
-19 1 0.0 -3.1804 -3.1804
-20 1 1.5902 -1.5902 -1.5902
-21 1 0.0 -3.1804 0.0
-22 1 1.5902 -1.5902 1.5902
-23 1 0.0 -3.1804 3.1804
-24 1 1.5902 -1.5902 4.7706
-25 1 0.0 0.0 -3.1804
-26 1 1.5902 1.5902 -1.5902
-27 1 0.0 0.0 0.0
-28 1 1.5902 1.5902 1.5902
-29 1 0.0 0.0 3.1804
-30 1 1.5902 1.5902 4.7706
-31 1 0.0 3.1804 -3.1804
-32 1 1.5902 4.7706 -1.5902
-33 1 0.0 3.1804 0.0
-34 1 1.5902 4.7706 1.5902
-35 1 0.0 3.1804 3.1804
-36 1 1.5902 4.7706 4.7706
-37 1 3.1804 -3.1804 -3.1804
-38 1 4.7706 -1.5902 -1.5902
-39 1 3.1804 -3.1804 0.0
-40 1 4.7706 -1.5902 1.5902
-41 1 3.1804 -3.1804 3.1804
-42 1 4.7706 -1.5902 4.7706
-43 1 3.1804 0.0 -3.1804
-44 1 4.7706 1.5902 -1.5902
-45 1 3.1804 0.0 0.0
-46 1 4.7706 1.5902 1.5902
-47 1 3.1804 0.0 3.1804
-48 1 4.7706 1.5902 4.7706
-49 1 3.1804 3.1804 -3.1804
-50 1 4.7706 4.7706 -1.5902
-51 1 3.1804 3.1804 0.0
-52 1 4.7706 4.7706 1.5902
-53 1 3.1804 3.1804 3.1804
-54 1 4.7706 4.7706 4.7706
-
diff --git a/testing/coupling_coefficients.yace b/testing/coupling_coefficients.yace
deleted file mode 100644
index d5e8e51..0000000
--- a/testing/coupling_coefficients.yace
+++ /dev/null
@@ -1,29 +0,0 @@
-elements: [W]
-E0: [0.000000]
-deltaSplineBins: 0.001000
-embeddings:
- 0: {ndensity: 1, FS_parameters: [1.0, 1.0], npoti: FinnisSinclair, rho_core_cutoff: 100000, drho_core_cutoff: 250}
-bonds:
- [0, 0]: {nradmax: 8, lmax: 3, nradbasemax: 8, radbasename: ChebExpCos, radparameters: [1.4], radcoefficients: [[[1, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0]], [[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0]], [[0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 0]], [[0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 1]]], prehc: 0, lambdahc: 1.4, rcut: 5.5, dcut: 0.01, rcut_in: 0.0, dcut_in: 0.01, inner_cutoff_type: distance}
-functions:
- 0:
- - {mu0: 0, rank: 1, ndensity: 1, num_ms_combs: 1, mus: [0], ns: [1], ls: [0], ms_combs: [0], ctildes: [1.0]}
- - {mu0: 0, rank: 1, ndensity: 1, num_ms_combs: 1, mus: [0], ns: [2], ls: [0], ms_combs: [0], ctildes: [1.0]}
- - {mu0: 0, rank: 1, ndensity: 1, num_ms_combs: 1, mus: [0], ns: [3], ls: [0], ms_combs: [0], ctildes: [1.0]}
- - {mu0: 0, rank: 1, ndensity: 1, num_ms_combs: 1, mus: [0], ns: [4], ls: [0], ms_combs: [0], ctildes: [1.0]}
- - {mu0: 0, rank: 1, ndensity: 1, num_ms_combs: 1, mus: [0], ns: [5], ls: [0], ms_combs: [0], ctildes: [1.0]}
- - {mu0: 0, rank: 1, ndensity: 1, num_ms_combs: 1, mus: [0], ns: [6], ls: [0], ms_combs: [0], ctildes: [1.0]}
- - {mu0: 0, rank: 1, ndensity: 1, num_ms_combs: 1, mus: [0], ns: [7], ls: [0], ms_combs: [0], ctildes: [1.0]}
- - {mu0: 0, rank: 1, ndensity: 1, num_ms_combs: 1, mus: [0], ns: [8], ls: [0], ms_combs: [0], ctildes: [1.0]}
- - {mu0: 0, rank: 2, ndensity: 1, num_ms_combs: 1, mus: [0, 0], ns: [1, 1], ls: [0, 0], ms_combs: [0, 0], ctildes: [1.0]}
- - {mu0: 0, rank: 2, ndensity: 1, num_ms_combs: 3, mus: [0, 0], ns: [1, 1], ls: [1, 1], ms_combs: [-1, 1, 0, 0, 1, -1], ctildes: [0.5773502588272095, -0.5773502588272095, 0.5773502588272095]}
- - {mu0: 0, rank: 2, ndensity: 1, num_ms_combs: 5, mus: [0, 0], ns: [1, 1], ls: [2, 2], ms_combs: [-2, 2, -1, 1, 0, 0, 1, -1, 2, -2], ctildes: [0.4472135901451111, -0.4472135901451111, 0.4472135901451111, -0.4472135901451111, 0.4472135901451111]}
- - {mu0: 0, rank: 2, ndensity: 1, num_ms_combs: 7, mus: [0, 0], ns: [1, 1], ls: [3, 3], ms_combs: [-3, 3, -2, 2, -1, 1, 0, 0, 1, -1, 2, -2, 3, -3], ctildes: [0.37796446681022644, -0.37796446681022644, 0.37796446681022644, -0.37796446681022644, 0.37796446681022644, -0.37796446681022644, 0.37796446681022644]}
- - {mu0: 0, rank: 3, ndensity: 1, num_ms_combs: 1, mus: [0, 0, 0], ns: [1, 1, 1], ls: [0, 0, 0], ms_combs: [0, 0, 0], ctildes: [1.0]}
- - {mu0: 0, rank: 3, ndensity: 1, num_ms_combs: 3, mus: [0, 0, 0], ns: [1, 1, 1], ls: [0, 1, 1], ms_combs: [0, -1, 1, 0, 0, 0, 0, 1, -1], ctildes: [0.33333332136784577, -0.33333332136784577, 0.33333332136784577]}
- - {mu0: 0, rank: 3, ndensity: 1, num_ms_combs: 5, mus: [0, 0, 0], ns: [1, 1, 1], ls: [0, 2, 2], ms_combs: [0, -2, 2, 0, -1, 1, 0, 0, 0, 0, 1, -1, 0, 2, -2], ctildes: [0.1999999952104794, -0.1999999952104794, 0.1999999952104794, -0.1999999952104794, 0.1999999952104794]}
- - {mu0: 0, rank: 3, ndensity: 1, num_ms_combs: 7, mus: [0, 0, 0], ns: [1, 1, 1], ls: [0, 3, 3], ms_combs: [0, -3, 3, 0, -2, 2, 0, -1, 1, 0, 0, 0, 0, 1, -1, 0, 2, -2, 0, 3, -3], ctildes: [0.14285713817113876, -0.14285713817113876, 0.14285713817113876, -0.14285713817113876, 0.14285713817113876, -0.14285713817113876, 0.14285713817113876]}
- - {mu0: 0, rank: 3, ndensity: 1, num_ms_combs: 9, mus: [0, 0, 0], ns: [1, 1, 1], ls: [1, 1, 2], ms_combs: [-1, -1, 2, -1, 0, 1, -1, 1, 0, 0, -1, 1, 0, 0, 0, 0, 1, -1, 1, -1, 0, 1, 0, -1, 1, 1, -2], ctildes: [0.1999999952104794, -0.14142135359335306, 0.08164966239889981, -0.14142135359335306, 0.16329932479779963, -0.14142135359335306, 0.08164966239889981, -0.14142135359335306, 0.1999999952104794]}
- - {mu0: 0, rank: 3, ndensity: 1, num_ms_combs: 15, mus: [0, 0, 0], ns: [1, 1, 1], ls: [1, 2, 3], ms_combs: [-1, -2, 3, -1, -1, 2, -1, 0, 1, -1, 1, 0, -1, 2, -1, 0, -2, 2, 0, -1, 1, 0, 0, 0, 0, 1, -1, 0, 2, -2, 1, -2, 1, 1, -1, 0, 1, 0, -1, 1, 1, -2, 1, 2, -3], ctildes: [0.14285713817113876, -0.11664237211493589, 0.09035078971800603, -0.06388765326736667, 0.03688555491939627, -0.08247860448643873, 0.10432810273894777, -0.11065666757424353, 0.10432810273894777, -0.08247860448643873, 0.03688555491939627, -0.06388765326736667, 0.09035078971800603, -0.11664237211493589, 0.14285713817113876]}
- - {mu0: 0, rank: 3, ndensity: 1, num_ms_combs: 19, mus: [0, 0, 0], ns: [1, 1, 1], ls: [2, 2, 2], ms_combs: [-2, 0, 2, -2, 1, 1, -2, 2, 0, -1, -1, 2, -1, 0, 1, -1, 1, 0, -1, 2, -1, 0, -2, 2, 0, -1, 1, 0, 0, 0, 0, 1, -1, 0, 2, -2, 1, -2, 1, 1, -1, 0, 1, 0, -1, 1, 1, -2, 2, -2, 0, 2, -1, -1, 2, 0, -2], ctildes: [0.106904496560845, -0.13093073535988964, 0.106904496560845, -0.13093073535988964, 0.0534522482804225, 0.0534522482804225, -0.13093073535988964, 0.106904496560845, 0.0534522482804225, -0.106904496560845, 0.0534522482804225, 0.106904496560845, -0.13093073535988964, 0.0534522482804225, 0.0534522482804225, -0.13093073535988964, 0.106904496560845, -0.13093073535988964, 0.106904496560845]}
- - {mu0: 0, rank: 3, ndensity: 1, num_ms_combs: 29, mus: [0, 0, 0], ns: [1, 1, 1], ls: [2, 3, 3], ms_combs: [-2, -1, 3, -2, 0, 2, -2, 1, 1, -2, 2, 0, -2, 3, -1, -1, -2, 3, -1, -1, 2, -1, 0, 1, -1, 1, 0, -1, 2, -1, -1, 3, -2, 0, -3, 3, 0, -2, 2, 0, -1, 1, 0, 0, 0, 0, 1, -1, 0, 2, -2, 0, 3, -3, 1, -3, 2, 1, -2, 1, 1, -1, 0, 1, 0, -1, 1, 1, -2, 1, 2, -3, 2, -3, 1, 2, -2, 0, 2, -1, -1, 2, 0, -2, 2, 1, -3], ctildes: [0.058321186057467944, -0.08247860448643873, 0.09035078971800603, -0.08247860448643873, 0.058321186057467944, -0.09221388589046331, 0.07142856908556938, -0.026082025684736942, -0.026082025684736942, 0.07142856908556938, -0.09221388589046331, 0.09221388589046331, -0.0, -0.055328333787121764, 0.07377110983879254, -0.055328333787121764, -0.0, 0.09221388589046331, -0.09221388589046331, 0.07142856908556938, -0.026082025684736942, -0.026082025684736942, 0.07142856908556938, -0.09221388589046331, 0.058321186057467944, -0.08247860448643873, 0.09035078971800603, -0.08247860448643873, 0.058321186057467944]}
diff --git a/testing/log.lammps b/testing/log.lammps
deleted file mode 100644
index 57a87cd..0000000
--- a/testing/log.lammps
+++ /dev/null
@@ -1,100 +0,0 @@
-LAMMPS (12 Jun 2025 - Development - 919ef6d369)
-OMP_NUM_THREADS environment is not set. Defaulting to 1 thread.
- using 1 OpenMP thread(s) per MPI task
-Total wall time: 0:00:00
- atom_style atomic
- read_data notbcc.data
-Reading data file ...
- orthogonal box = (-3.1804 -3.1804 -3.1804) to (6.3608 6.3608 6.3608)
- 1 by 1 by 1 MPI processor grid
- reading atoms ...
- 54 atoms
- read_data CPU = 0.001 seconds
- pair_style mliap model mliappy LATER descriptor ace coupling_coefficients.yace
-Loading python model deferred.
- pair_coeff * * W
- neighbor 2.3 bin
- neigh_modify one 10000
- thermo 10
- thermo_style custom step etotal temp press
-
-Loading python model complete.
-run 0
-WARNING: No fixes with time integration, atoms won't move
-For more information see https://docs.lammps.org/err0028 (src/verlet.cpp:60)
-Neighbor list info ...
- update: every = 1 steps, delay = 0 steps, check = yes
- max neighbors/atom: 10000, page size: 100000
- master list distance cutoff = 7.8
- ghost atom cutoff = 7.8
- binsize = 3.9, bins = 3 3 3
- 1 neighbor lists, perpetual/occasional/extra = 1 0 0
- (1) pair mliap, perpetual
- attributes: full, newton on
- pair build: full/bin/atomonly
- stencil: full/bin/3d
- bin: standard
-Per MPI rank memory allocation (min/avg/max) = 3.793 | 3.793 | 3.793 Mbytes
- Step TotEng Temp Press
- 0 1.4656356e-308 0 -63832.528
-Loop time of 3.073e-06 on 1 procs for 0 steps with 54 atoms
-
-97.6% CPU use with 1 MPI tasks x 1 OpenMP threads
-
-MPI task timing breakdown:
-Section | min time | avg time | max time |%varavg| %total
----------------------------------------------------------------
-Pair | 0 | 0 | 0 | 0.0 | 0.00
-Neigh | 0 | 0 | 0 | 0.0 | 0.00
-Comm | 0 | 0 | 0 | 0.0 | 0.00
-Output | 0 | 0 | 0 | 0.0 | 0.00
-Modify | 0 | 0 | 0 | 0.0 | 0.00
-Other | | 3.073e-06 | | |100.00
-
-Nlocal: 54 ave 54 max 54 min
-Histogram: 1 0 0 0 0 0 0 0 0 0
-Nghost: 807 ave 807 max 807 min
-Histogram: 1 0 0 0 0 0 0 0 0 0
-Neighs: 0 ave 0 max 0 min
-Histogram: 1 0 0 0 0 0 0 0 0 0
-FullNghs: 7262 ave 7262 max 7262 min
-Histogram: 1 0 0 0 0 0 0 0 0 0
-
-Total # of neighbors = 7262
-Ave neighs/atom = 134.48148
-Neighbor list builds = 0
-Dangerous builds = 0
-run 0
-WARNING: No fixes with time integration, atoms won't move
-For more information see https://docs.lammps.org/err0028 (src/verlet.cpp:60)
-Per MPI rank memory allocation (min/avg/max) = 3.793 | 3.793 | 3.793 Mbytes
- Step TotEng Temp Press
- 0 15.577732 0 -63832.528
-Loop time of 1.0616e-05 on 1 procs for 0 steps with 54 atoms
-
-75.4% CPU use with 1 MPI tasks x 1 OpenMP threads
-
-MPI task timing breakdown:
-Section | min time | avg time | max time |%varavg| %total
----------------------------------------------------------------
-Pair | 0 | 0 | 0 | 0.0 | 0.00
-Neigh | 0 | 0 | 0 | 0.0 | 0.00
-Comm | 0 | 0 | 0 | 0.0 | 0.00
-Output | 0 | 0 | 0 | 0.0 | 0.00
-Modify | 0 | 0 | 0 | 0.0 | 0.00
-Other | | 1.062e-05 | | |100.00
-
-Nlocal: 54 ave 54 max 54 min
-Histogram: 1 0 0 0 0 0 0 0 0 0
-Nghost: 807 ave 807 max 807 min
-Histogram: 1 0 0 0 0 0 0 0 0 0
-Neighs: 0 ave 0 max 0 min
-Histogram: 1 0 0 0 0 0 0 0 0 0
-FullNghs: 7262 ave 7262 max 7262 min
-Histogram: 1 0 0 0 0 0 0 0 0 0
-
-Total # of neighbors = 7262
-Ave neighs/atom = 134.48148
-Neighbor list builds = 0
-Dangerous builds = 0
-Total wall time: 0:00:00
diff --git a/testing/notbcc.data b/testing/notbcc.data
deleted file mode 100644
index 0d812b2..0000000
--- a/testing/notbcc.data
+++ /dev/null
@@ -1,70 +0,0 @@
-# LAMMPS data file written by OVITO Pro 3.10.6
-
-54 atoms
-1 atom types
-
--3.1804 6.3608 xlo xhi
--3.1804 6.3608 ylo yhi
--3.1804 6.3608 zlo zhi
-
-Masses
-
-1 183.83
-
-Atoms # atomic
-
-1 1 -2.1804 -3.1804 -3.1804
-2 1 -2.5902 -1.5902 -1.5902
-3 1 -3.1804 -3.1804 0.0
-4 1 -2.5902 -1.5902 1.5902
-5 1 -3.1804 -3.1804 3.1804
-6 1 -1.5902 -1.5902 4.7706
-7 1 -1.1804 0.0 -3.1804
-8 1 -1.5902 1.5902 -1.5902
-9 1 -3.1804 0.0 0.0
-10 1 -1.5902 1.5902 1.5902
-11 1 -2.1804 0.0 3.1804
-12 1 -1.5902 1.5902 4.7706
-13 1 -3.1804 3.1804 -3.1804
-14 1 -1.5902 4.7706 -1.5902
-15 1 -3.1804 3.1804 0.0
-16 1 -1.5902 4.7706 1.5902
-17 1 -3.1804 3.1804 3.1804
-18 1 -1.5902 4.7706 4.7706
-19 1 0.0 -3.1804 -3.1804
-20 1 1.5902 -1.5902 -1.5902
-21 1 0.0 -3.1804 0.0
-22 1 1.5902 -1.5902 1.5902
-23 1 0.0 -3.1804 3.1804
-24 1 1.5902 -1.5902 4.7706
-25 1 0.0 0.0 -3.1804
-26 1 1.5902 1.5902 -1.5902
-27 1 0.0 0.0 0.0
-28 1 1.5902 1.5902 1.5902
-29 1 0.0 0.0 3.1804
-30 1 1.5902 1.5902 4.7706
-31 1 0.0 3.1804 -3.1804
-32 1 1.5902 4.7706 -1.5902
-33 1 0.0 3.1804 0.0
-34 1 1.5902 4.7706 1.5902
-35 1 0.0 3.1804 3.1804
-36 1 1.5902 4.7706 4.7706
-37 1 3.1804 -3.1804 -3.1804
-38 1 4.7706 -1.5902 -1.5902
-39 1 3.1804 -3.1804 0.0
-40 1 4.7706 -1.5902 1.5902
-41 1 3.1804 -3.1804 3.1804
-42 1 4.7706 -1.5902 4.7706
-43 1 3.1804 0.0 -3.1804
-44 1 4.7706 1.5902 -1.5902
-45 1 3.1804 0.0 0.0
-46 1 4.7706 1.5902 1.5902
-47 1 3.1804 0.0 3.1804
-48 1 4.7706 1.5902 4.7706
-49 1 3.1804 3.1804 -3.1804
-50 1 4.7706 4.7706 -1.5902
-51 1 3.1804 3.1804 0.0
-52 1 4.7706 4.7706 1.5902
-53 1 3.1804 3.1804 3.1804
-54 1 4.7706 4.7706 4.7706
-
diff --git a/testing/somestr.data b/testing/somestr.data
deleted file mode 100644
index 82a52ac..0000000
--- a/testing/somestr.data
+++ /dev/null
@@ -1,126 +0,0 @@
-LAMMPS data file via write_data, version 27 Jun 2024, timestep = 0, units = metal
-
-54 atoms
-1 atom types
-
--3.1804 6.3608 xlo xhi
--3.1804 6.3608 ylo yhi
--3.1804 6.3608 zlo zhi
-
-Masses
-
-1 183.83
-
-Atoms # atomic
-
-1 1 -3.1804 -3.1804 -3.1804 0 0 0
-2 1 -1.5902 -1.5902 -1.5902 0 0 0
-3 1 -3.1804 -3.1804 0 0 0 0
-4 1 -1.5902 -1.5902 1.5902 0 0 0
-5 1 -3.1804 -3.1804 3.1804 0 0 0
-6 1 -1.5902 -1.5902 4.7706 0 0 0
-7 1 -3.1804 0 -3.1804 0 0 0
-8 1 -1.5902 1.5902 -1.5902 0 0 0
-9 1 -3.1804 0 0 0 0 0
-10 1 -1.5902 1.5902 1.5902 0 0 0
-11 1 -3.1804 0 3.1804 0 0 0
-12 1 -1.5902 1.5902 4.7706 0 0 0
-13 1 -3.1804 3.1804 -3.1804 0 0 0
-14 1 -1.5902 4.7706 -1.5902 0 0 0
-15 1 -3.1804 3.1804 0 0 0 0
-16 1 -1.5902 4.7706 1.5902 0 0 0
-17 1 -3.1804 3.1804 3.1804 0 0 0
-18 1 -1.5902 4.7706 4.7706 0 0 0
-19 1 0 -3.1804 -3.1804 0 0 0
-20 1 1.5902 -1.5902 -1.5902 0 0 0
-21 1 0 -3.1804 0 0 0 0
-22 1 1.5902 -1.5902 1.5902 0 0 0
-23 1 0 -3.1804 3.1804 0 0 0
-24 1 1.5902 -1.5902 4.7706 0 0 0
-25 1 0 0 -3.1804 0 0 0
-26 1 1.5902 1.5902 -1.5902 0 0 0
-27 1 0 0 0 0 0 0
-28 1 1.5902 1.5902 1.5902 0 0 0
-29 1 0 0 3.1804 0 0 0
-30 1 1.5902 1.5902 4.7706 0 0 0
-31 1 0 3.1804 -3.1804 0 0 0
-32 1 1.5902 4.7706 -1.5902 0 0 0
-33 1 0 3.1804 0 0 0 0
-34 1 1.5902 4.7706 1.5902 0 0 0
-35 1 0 3.1804 3.1804 0 0 0
-36 1 1.5902 4.7706 4.7706 0 0 0
-37 1 3.1804 -3.1804 -3.1804 0 0 0
-38 1 4.7706 -1.5902 -1.5902 0 0 0
-39 1 3.1804 -3.1804 0 0 0 0
-40 1 4.7706 -1.5902 1.5902 0 0 0
-41 1 3.1804 -3.1804 3.1804 0 0 0
-42 1 4.7706 -1.5902 4.7706 0 0 0
-43 1 3.1804 0 -3.1804 0 0 0
-44 1 4.7706 1.5902 -1.5902 0 0 0
-45 1 3.1804 0 0 0 0 0
-46 1 4.7706 1.5902 1.5902 0 0 0
-47 1 3.1804 0 3.1804 0 0 0
-48 1 4.7706 1.5902 4.7706 0 0 0
-49 1 3.1804 3.1804 -3.1804 0 0 0
-50 1 4.7706 4.7706 -1.5902 0 0 0
-51 1 3.1804 3.1804 0 0 0 0
-52 1 4.7706 4.7706 1.5902 0 0 0
-53 1 3.1804 3.1804 3.1804 0 0 0
-54 1 4.7706 4.7706 4.7706 0 0 0
-
-Velocities
-
-1 0 0 0
-2 0 0 0
-3 0 0 0
-4 0 0 0
-5 0 0 0
-6 0 0 0
-7 0 0 0
-8 0 0 0
-9 0 0 0
-10 0 0 0
-11 0 0 0
-12 0 0 0
-13 0 0 0
-14 0 0 0
-15 0 0 0
-16 0 0 0
-17 0 0 0
-18 0 0 0
-19 0 0 0
-20 0 0 0
-21 0 0 0
-22 0 0 0
-23 0 0 0
-24 0 0 0
-25 0 0 0
-26 0 0 0
-27 0 0 0
-28 0 0 0
-29 0 0 0
-30 0 0 0
-31 0 0 0
-32 0 0 0
-33 0 0 0
-34 0 0 0
-35 0 0 0
-36 0 0 0
-37 0 0 0
-38 0 0 0
-39 0 0 0
-40 0 0 0
-41 0 0 0
-42 0 0 0
-43 0 0 0
-44 0 0 0
-45 0 0 0
-46 0 0 0
-47 0 0 0
-48 0 0 0
-49 0 0 0
-50 0 0 0
-51 0 0 0
-52 0 0 0
-53 0 0 0
-54 0 0 0
diff --git a/testing/testing.py b/testing/testing.py
deleted file mode 100644
index 0727f08..0000000
--- a/testing/testing.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from time import time
-from mpi4py import MPI
-from GRSlib.GRS import GRS
-import numpy as np
-from ase.build import bulk
-from ase.io import read,write
-
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-nprocs = comm.Get_size()
-
-settings = "../GenericInput.in"
-grs = GRS(settings, comm=comm)
-
-#testing of io class
-#grs.config.view_state()
-#-----------------------
-
-#testing of convert class
-#attributes = [attr for attr in dir(grs.convert) if not attr.startswith('__')]
-#print("attr of grs.convert:")
-#print(attributes)
-
-#atoms = grs.convert.lammps_to_ase('bcc.data')
-#print(atoms)
-#file = grs.convert.ase_to_lammps(atoms)
-#print(file)
-#grs.convert.run_lammps_single('bcc.data')
-#current_desc = grs.convert_to_desc(file)
-#-----------------------
-#grs.genetic_move.tournament_selection(data=None)
-
-score = grs.get_score('bcc.data')
-print(score)
-#print("!")
-exit()
diff --git a/testing/tmp b/testing/tmp
deleted file mode 100644
index ee55e12..0000000
--- a/testing/tmp
+++ /dev/null
@@ -1,54 +0,0 @@
- 2.74E+00 7.92E-01 2.17E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 1.34791020e+00 2.53E-01 7.82E-01 7.31642122e+00 2.01194972e+00 5.63E+00
- 2.75046170e+00 7.92E-01 2.18E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 1.34791020e+00 2.53E-01 7.82E-01 7.31642122e+00 2.01194972e+00 5.63E+00
- 6.21182391e+00 1.78E+00 4.93E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 6.60260677e+00 1.88E+00 5.23E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 2.75046170e+00 7.92E-01 2.18E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 7.13814451e-01 1.30E-01 4.05E-01 7.31642122e+00 2.01194972e+00 5.63E+00
- 4.57245433e-02 8.86E-04 3.47E-03 7.31642122e+00 2.01194972e+00 5.63E+00
- 7.27485345e+00 2.01E+00 5.63E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 6.68232547e+00 1.89E+00 5.26E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 6.60260677e+00 1.88E+00 5.23E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 2.74214814e+00 7.92E-01 2.17E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 1.26819149e+00 2.45E-01 7.54E-01 7.31642122e+00 2.01194972e+00 5.63E+00
- 4.28575137e+00 1.24E+00 3.41E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 6.60260677e+00 1.88E+00 5.23E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 7.28316700e+00 2.01E+00 5.63E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 6.59429321e+00 1.88E+00 5.23E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 2.74214814e+00 7.92E-01 2.17E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 1.34791020e+00 2.53E-01 7.82E-01 7.31642122e+00 2.01194972e+00 5.63E+00
- 2.75046170e+00 7.92E-01 2.18E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 2.22914937e+00 5.18E-01 1.52E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 6.13626198e+00 1.77E+00 4.91E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 6.60260677e+00 1.88E+00 5.23E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 2.75046170e+00 7.92E-01 2.18E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 7.13814451e-01 1.30E-01 4.05E-01 7.31642122e+00 2.01194972e+00 5.63E+00
- 4.98813199e-02 9.66E-04 3.79E-03 7.31642122e+00 2.01194972e+00 5.63E+00
- 7.29979411e+00 2.01E+00 5.63E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 7.22007541e+00 2.00E+00 5.60E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 6.60260677e+00 1.88E+00 5.23E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 2.74214814e+00 7.92E-01 2.17E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 1.72622273e+00 3.53E-01 1.08E+00 7.31642122e+00 2.01194972e+00 5.63E+00
- 5.96851102e+00 1.76E+00 4.85E+00 7.31642122e+00 2.01194972e+00 5.63E+00
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diff --git a/testing/wig_LR_0_r123_lmax033.pickle b/testing/wig_LR_0_r123_lmax033.pickle
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