MLINDEX - A data driven approach to powder diffraction indexing

A powder diffraction indexing program that uses machine learning models to initialize the SVD-Index algorithm. It takes an input peak list and returns a list of unit cells ranked by Figure of Merit.

Note: The paper describing the methods is currently in submission to the Journal of Applied Crystallography. This application is in beta stage. Usage and feedback would be greatly appreciated to improve user experience.

Installation

Currently the only way to install the program is by cloning the repository. The machine learning models take roughly 1 GB of hard-drive space and are version controlled through git-lfs. You will need to install git-lfs to download the models from GitHub.

Prerequisites

• Git LFS installed on your system
• Conda environment (recommended)

Installation Steps

1. Clone the repository:

   git clone git@github.com:dwmoreau/MLI.git

2. Retrieve the large files:

   git lfs fetch --all
   git lfs checkout

3. Install the project:

   cd /path/to/the/cloned/repo
   pip install .

Usage

Peak List Generation

Peak list files generated by GSAS-II can be used directly. GSAS-II provides tutorials for creating peak lists:

• GSAS-II Fit Peaks Tutorial

Alternatively, provide the d-spacings of the observed diffraction peaks in units of q², which is simply 1/resolution². Save this list to a numpy array.

Code Execution

Using GSAS-II pkslst files

When using a GSAS-II pkslst file, you must supply the wavelength:

mlindex.run --peak-file /path/to/your/file/11bmb_3844.pkslst --wavelength 0.413128

Using numpy arrays

When using a numpy array, the wavelength option is ignored:

mlindex.run --peak-file /path/to/your/file/11bmb_3844.npy

Parallel Execution

The examples above run serially. For parallel execution using MPI (currently supports six processes):

mpiexec -n 6 mlindex.run --peak-file /path/to/your/file/11bmb_3844.pkslst --wavelength 0.413128

Results Interpretation

The program outputs the top 20 unit cell candidates ranked by M20 score:

Column Descriptions

Column	Description
M20	de Wolff Figure of Merit (Wolff 1968)
Minfo	Figure of Merit from Taupin (1988)
n_indexed	Number of indexed peaks, using a probability from Taupin (1988) and a 95% threshold
bravais lattice	Assumed Bravais lattice for the unit cell optimization
spacegroup	Spacegroup whose systematic absences best align with the observed peak list

Acknowledgements

The US Department of Energy Integrated Computational and Data Infrastructure for Scientific Discovery supported this work via grant DE-SC0022215 to Aaron S. Brewster (LBL), Tess Smidt (MIT), and Nate Hohmann (UCONN).

Citations

• Taupin, D. (1988). J. Appl. Cryst. 21, 485-489.
• Wolff, P. M. D. (1968). J. Appl. Cryst. 1, 108.