learningmatter-mit/alchemical-mlip

Alchemical machine learning interatomic potentials

Alchemical MLIP

This repository contains the code to modify machine learning interatomic potentials (MLIPs) to enable continuous and differentiable alchemical transformations. Currently, we provide the alchemical modification for the MACE model. The details of the method are described in the paper: Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials.

Installation

We tested the code with Python 3.10 and the packages in requirements.txt. For example, you can create a conda environment and install the required packages as follows (assuming CUDA 11.8):

conda create -n alchemical-mlip python=3.10
conda activate alchemical-mlip
pip install torch==2.0.1 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
pip install -e .

Static calculations

We provide the jupyter notebooks for the lattice parameter calculations (Fig. 2 in the paper) and the compositional optimization (Fig. 3) in the notebook directory.

notebook/
├── 1_solid_solution.ipynb
└── 2_compositional_optimization.ipynb

Free energy calculations

We provide the scripts for the free energy calculations for the vacancy (Fig. 4) and perovskites (Fig. 5) in the scripts directory.

scripts/
├── vacancy_frenkel_ladd.py
├── perovskite_frenkel_ladd.py
└── perovskite_alchemy.py

The arguments for the scripts are as follows:

# Vacancy Frenkel-Ladd calculation
python vacancy_frenkel_ladd.py \
    --structure-file data/structures/Fe.cif \
    --supercell 5 5 5 \
    --temperature 100 \
    --output-dir data/results/vacancy/Fe_5x5x5_100K/0

# Perovskite Frenkel-Ladd calculation (alpha phase)
python perovskite_frenkel_ladd.py \
    --structure-file data/structures/CsPbI3_alpha.cif \
    --supercell 6 6 6 \
    --temperature 400 \
    --output-dir data/results/perovskite/frenkel_ladd/CsPbI3_alpha_6x6x6_400K/0

# Perovskite Frenkel-Ladd calculation (delta phase)
python perovskite_frenkel_ladd.py \
    --structure-file data/structures/CsPbI3_delta.cif \
    --supercell 6 3 3 \
    --temperature 400 \
    --output-dir data/results/perovskite/frenkel_ladd/CsPbI3_delta_6x3x3_400K/0

# Perovskite alchemy calculation (alpha phase)
python -u perovskite_alchemy.py \
    --structure-file data/structures/CsPbI3_alpha.cif \
    --supercell 6 6 6 \
    --switch-pair Pb Sn \
    --temperature 400 \
    --output-dir data/results/perovskite/alchemy/CsPbI3_CsSnI3_alpha_400K/0

# Perovskite alchemy calculation (delta phase)
python -u perovskite_alchemy.py \
    --structure-file data/structures/CsPbI3_delta.cif \
    --supercell 6 3 3 \
    --switch-pair Pb Sn \
    --temperature 400 \
    --output-dir data/results/perovskite/alchemy/CsPbI3_CsSnI3_delta_400K/0

The result files are large and not included in the repository. If you want to reproduce the results without running the calculations, the result files are uploaded in the Zenodo repository. Please download the files and place them in the data/results directory.

The post-processing scripts for the free energy calculations are provided in the notebook directory.

notebook/
├── 3_vacancy_analysis.ipynb
└── 4_perovskite_analysis.ipynb

Citation

@misc{nam2024interpolation,
    title={Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials},
    author={Juno Nam and Rafael G{\'o}mez-Bombarelli},
    year={2024},
    eprint={2404.10746},
    archivePrefix={arXiv},
    primaryClass={cond-mat.mtrl-sci}
}