MOQD-CSP: Multi-Objective Quality-Diversity for Crystal Structure Prediction

MOQD-CSP contains the code for the Multi-Objective Quality-Diversity for Crystal Structure Prediction paper. This builds on top of the QD4CSP repo in order to apply Quality-Diversity Algorithms to Crystal Structure Prediction. This method (illustrated below) uses domain-specific mutations and surrogate models for evaluation in order to generate a diverse collection of crystal structures that achieve different trade-offs on objectives:

Installation

To run this code, you need to install the necessary libraries as listed in requirements.txt via:

pip install -r requirements.txt

However, we recommend using a containerised environment such as Docker, Singularity or conda to use the repository. Further details are provided in the last section.

Basic API Usage

To run the MOME-X algorithm, or any other baseline algorithm mentioned in the paper, you just need to run the main.py script and specify the algorithm and system you wish to run. For example, to run MOME-X on Carbon, you can run:

python3 main.py -—algo=mome-x –system=C

The hyperparameters of the algorithms can be modified by changing their values in the configs directory of the repository. Alternatively, they can be modified directly in the command line. For example, to decrease the pareto_front_max_length parameter from 50 to 20 in MOME-P2C, you can run:

python3 main.py --algo=mome-x pareto_front_max_length=20

Analysis

Running each algorithm automatically saves metrics, visualisations and plots of performance into a results directory. However, you can compare performance between algorithms once they have been run using the analysis.py script. To do this, you need to edit the list of the algorithms and environments you wish to compare and the metrics you wish to compute (at the bottom of analysis.py). Then, the relevant plots and performance metrics will be computed by running:

python3 analysis.py

Similarly, you can run:

python3 plot_moqd_illumination.py

Or:

python3 plot_reference_illumination.py

To generate additional visualisation plots of the final results.

Generating Reference Data

By default, this repo contains the data for Carbon, Silicon, Silicon Carbide, Silicon Dioxide and Titanium Dioxide. However, you can generate reference data for other systems by modifying the reference_data/prepare_reference_data.py scrupt and then running it.

Singularity Usage

To build a final container (an executable file) using Singularity make sure you are in the root of the repository and then run:

singularity build --fakeroot --force singularity/[FINAL CONTAINER NAME].sif singularity/singularity.def

where you can replace '[FINAL CONTAINER NAME]' by your desired file name. When you get the final image, you can execute it via:

singularity -d run --cleanenv --containall --no-home --nv [FINAL CONTAINER NAME].sif [EXTRA ARGUMENTS]

where

• [FINAL CONTAINER NAME].sif is the final image built
• [EXTRA ARGUMENTS] is a list of any futher arguments that you want to add. For example, you may want to change the random seed or Brax environment.