MeGen is an RL-based algorithm that generates low-energy 3D structures of metal clusters with biased structure generation towards low-energy structures. MeGen employs DART-predicted energy to evaluate the generated structures. Including DART reduces the number of compute-intensive optimizations and adds a bias in the model’s learning by rewarding low-energy generated structures. This makes MeGen more efficient than algorithms like basin hopping, random search, and genetic evolutions that do not have such a bias and require local structure optimization.

This repository allows to train reinforcement learning policies for designing gallium clusters directly in Cartesian coordinates. The agent builds gallium cluster of size "N+1" by taking Ga atoms from a given bag and placing them onto a 3D canvas. The 3D canvas has seed structure of gallium cluster of size "N".

The code of MeGen is adapted from MolGym. We thank the authors for making the code available on github. MolGym package allows to train reinforcement learning policies for designing molecules directly in Cartesian coordinates. So MolGym was developed for organic molecules. We modified the code to work with clusters. In our case we focus on gallium clusters.

MeGen uses DART model as a reward function. The snippet reward = CustomReward() calls the DART model to calculate reward.

MeGen - generation of gallium metal clusters using reinforcement learning
Rohit Modee, Ashwini Verma, Kavita Joshi, U. Deva Priyakumar
Machine Learning: Science and Technology, 2023.
https://iopscience.iop.org/article/10.1088/2632-2153/acdc03

DART: Deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers
Rohit Modee, Sheena Agarwal, Ashwini Verma, Kavita Joshi, U. Deva Priyakumar
Physical Chemistry Chemical Physics, 2021.
https://pubs.rsc.org/en/content/articlelanding/2021/cp/d1cp02956h

Dependencies:

• Python >= 3.7
• torch >= 1.5.1
• MolGym

Install required packages and library itself:

cd megen_molgym
pip install -r requirements.txt
pip install -e .

You can use this code to train and evaluate reinforcement learning agents for generation of low-energy 3D gallium clusters. data directory contains the training data. DART directory contains trained DART model.

To perform the gallium (Ga18) structure generation experiment, run the following

cd scripts
python3 run_megen.py

Hyper-parameters and other details can be found in the paper.

To generate learning curves, run the following command:

python3 plot.py --dir=results

Running this script will automatically generate a figure of the learning curve.

To write out the generated structures, run the following command:

python3 structures.py --dir=data --symbols=X,Ga

This should generate file named structures_eval.xyz which contains generated structures.

For post-processing and to write the structures in file structures_eval.xyz to POSCAR for duplicates removal and minimization using VASP use following command:

python3 read_gen_eval_stru.py

You can visualize the structures in the generated XYZ file using, for example, ASE-GUI.

If you use this code, please cite our papers:

@article{megen_modee,
author = {Modee, Rohit and Verma, Ashwini and Joshi, Kavita and Priyakumar, U. Deva},
doi = {10.1088/2632-2153/acdc03},
issn = {2632-2153},
journal = {Mach. Learn. Sci. Technol.},
month = {jun},
number = {2},
pages = {025032},
title = {{MeGen - generation of gallium metal clusters using reinforcement learning}},
url = {https://iopscience.iop.org/article/10.1088/2632-2153/acdc03},
volume = {4},
year = {2023}
}
@article{Modee2021,
author = {Modee, Rohit and Agarwal, Sheena and Verma, Ashwini and Joshi, Kavita and Priyakumar, U. Deva},
doi = {10.1039/d1cp02956h},
issn = {14639076},
journal = {Phys. Chem. Chem. Phys.},
number = {38},
pages = {21995--22003},
title = {{DART: Deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers}},
url = {https://xlink.rsc.org/?DOI=D1CP02956H},
volume = {23},
year = {2021}
}

MolGym papers:

@inproceedings{Simm2020Reinforcement,
  title = {Reinforcement Learning for Molecular Design Guided by Quantum Mechanics},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  author = {Simm, Gregor N. C. and Pinsler, Robert and {Hern{\'a}ndez-Lobato}, Jos{\'e} Miguel},
  editor = {III, Hal Daum{\'e} and Singh, Aarti},
  year = {2020},
  volume = {119},
  pages = {8959--8969},
  publisher = {{PMLR}},
  series = {Proceedings of Machine Learning Research}
  url = {https://proceedings.mlr.press/v119/simm20b.html}
}

@inproceedings{Simm2021SymmetryAware,
  title = {Symmetry-Aware Actor-Critic for 3D Molecular Design},
  author = {Gregor N. C. Simm and Robert Pinsler and G{\'a}bor Cs{\'a}nyi and Jos{\'e} Miguel Hern{\'a}ndez-Lobato},
  booktitle = {International Conference on Learning Representations},
  year = {2021},
  url = {https://openreview.net/forum?id=jEYKjPE1xYN}
}