A package designed to automate the process of phase identification from XRD spectra using a probabilistic deep learning trained with physics-informed data augmentation.

The corresponding manuscript can be found at arXiv. Reproducing the published results can be accomplished using the data provided in figshare.

First clone the repository:

git clone https://github.com/njszym/XRD-AutoAnalyzer.git

Then, to install all required modules, navigate to the cloned directory and execute:

python setup.py install --user

A pre-trained model for the Li-Mn-Ti-O-F chemical space is available in the Example/ directory. To classify the experimentally measured patterns tabulated in the Spectra/ sub-folder, run the following:

python run_CNN.py

The characterization of each spectrum should take around 1-2 minutes on a single processor. Once all classifications are made, the predicted phases will be printed along with their associated probabilities.

To develop a model that can be used to perform phase identification in a new chemical space, place all relevant CIFs into Novel_Space/All_CIFs. Then navigate to the Novel_Space/directory and execute:

python construct_model.py

This script will:

1. Filter all unique stoichiometric phases from the provided CIFs.

2. If --include_ns is specified: generate hypothetical solid solutions between the stoichiometric phases.

3. Simulate augmented XRD spectra from the phases produced by (1) and (2).

4. Train a convolutional neural network on the augmented spectra.

This process may require a substantial amount of computational resources depending on the size of the composition space considered. For example: training our model in the Li-Mn-Ti-O-F space, which included 255 reference phases, required about 12 hours of computational runtime on 16 cores. Necessary computational time should scale linearly with the number of reference phases. Similarily, time is reduced linearly with the number of cores used as all processes executed here are perfectly parallel (i.e., independent of one another).

When the procedure is completed, a trained Model.h5 file will be made available.

By default, the following bounds are used on artifacts included during data augmentation:

• Peak shifts: up to +/- 4% strain applied to each lattice parameter
• Peak broadening: domain size ranging from 1-100 nm
• Peak intensity variation: texture causing as much as +/- 50% change in peak height

However, custom bounds can also be specified, e.g., as follows:

python construct_model.py --max_strain=0.04 --min_domain_size=1 --max_domain_size=100 --max_texture=0.5

In the directory containing Model.h5, place all spectra to be classified in the Spectra/ folder. These files should be in xy format and the diffraction angles should span at least 10-80 degrees. Wider ranges may be used, but will be curtailed to make predictions based only on angles between 10-80 degrees.

Once all files are placed in the Spectra/ folder, they can be classified by executing:

python run_CNN.py

Output will appear as:

Filename: (name of the spectrum)
Predicted phases: (phase_1 + phase_2 + ...)
Confidence: (probabilities associated with the phases above)

Phase labels are denoted as formula_spacegroup.

By default, only phases with a confidence above 25% will be shown. To also show low-confidence phases, the -all argument can be used at runtime.

The model assumes that spectra are measured using Cu K-alpha radiation. However, the user can specify any arbitary wavelength (lambda, in angstroms) as follows:

python run_CNN.py --wavelength=lambda

For each spectrum, the phase identification algorithm runs until either (i) a maximum of three unique compounds have been identified, or (ii) all peaks with intensities greater than or equal to 10% of the spectrum's maximum intensity have been identified. To change these parameters (denoted N and I), the following arguments can be specified:

python run_CNN.py --max_phases=N --cutoff_intensity=I

To plot the line profiles of the predicted phases in the measured spectrum for comparison, the --plot option may also be used:

python run_CNN.py --plot

Which will yield a plot of the form: