Releases: deepmodeling/deepmd-kit
Releases · deepmodeling/deepmd-kit
v1.2.0
New features of dp train:
- Polarizability and dipole fitting
- If provided with
stop_lr
, thedecay_rate
will be computed automatically - Support non-pbc system: add an empty file named nopbc to the data system.
- Use envs
TF_INTRA_OP_PARALLELISM_THREADS
andTF_INTER_OP_PARALLELISM_THREADS
to control the multi-threading of tf, clean up command line options. - When the key
systems
is provided with a string, all possible systems will be recursively searched within the dir given bysystems
- User specific atomic energy.
- Exclude types when building descriptors.
- User specific activation function and network precision.
- Transform neural network parameters.
New features of dp test:
- Recursively search all possible systems in a directory.
- Weighted average of test results
Bug fixings:
- Cuda version compatibility
- raw_to_set when nframe == 1
- Slow test when testing multiple systems
- Potentially wrong neighbor list when number of neighbors is larger than 256.
- Fix bug #163 : broken lib link of dp_ipi
- Infering number of types from data
v1.1.4
v1.1.3
Bug fixings:
- raw_to_set.sh does not work for data system that has only one frame.
- dp test ignores type_map.raw
- cannot compile lammps with deepmd-kit that does not use GPU
- compiling problems in debug mode
- GPU kernel threading bug that leads to wrong model deviation
v1.1.2
v1.1.1
v1.1.0
New features:
- Atom parameter
- More robust data statistics
- Relative force loss
Code structure:
- Implement WFC and Polarizability by TensorModel
- Select the appropriate shared library suffix based on the operating system
Bug fixings:
- The minimal model devi when a proc has none local atom.
- Correctly unset USE_CUDA_TOOLKIT