seongsukim-ml / DB_DiffCSP

Database generation by DiffCSP

DB_DiffCSP

Use DiffCSP to generate a new materila database

DiffCSP

Implementation codes for Crystal Structure Prediction by Joint Equivariant Diffusion.

Dependencies

python==3.8.13
torch==1.9.0
torch-geometric==1.7.2
pytorch_lightning==1.3.8
pymatgen==2022.9.21

Training

python diffcsp/run.py data=<dataset> expname=<expname>

The <dataset> tag can be selected from perov_5, mp_20, mpts_52 and carbon_24.

Evaluation

Stable structure prediction & Property prediction

One sample

python scripts/evaluate.py --model_path <model_path>
python scripts/compute_metrics --root_path <model_path> --tasks struct --gt_file data/<dataset>/test.csv 

Multiple samples

python scripts/evaluate.py --model_path <model_path> --num_evals 20
python scripts/compute_metrics --root_path <model_path> --tasks struct prop --gt_file data/<dataset>/test.csv --multi_eval

Metastable structure generation

python scripts/generation.py --model_path <model_path> --dataset carbon
python scripts/compute_metrics --root_path <model_path> --tasks gen --gt_file data/carbon_24/test.csv

Sample from arbitrary composition

python scripts/sample.py --model_path <model_path> --save_path <save_path> --formula <formula> --num_evals <num_evals>

DiffCSP_Default