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RLVacDiffSim

Reinforcement Learning driven simulation of vacancy diffusion

Create conda envrionment

conda update conda
pip install --upgrade pip
cd <rlsim_direcotry>
conda env create -f environment.yml
conda activate rlsim-env

Install pytroch, torch_geometric right cuda version

  • below example is for pytorch version 2.2.0 with cuda version 12.1
conda install pytorch=2.2.0 torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.2.0+cu121.html
pip install torch_geometric

Install the package

pip install -e .
pip install -e ".[dev]" # For developer version

Install MACE, RGNN

  • MACE: pip install mace-torch
  • RGNN: install from github page

Usage

We provide scripts in command line interface (CLI). Trained models and initial poscars (256 atoms with mono vacancy) are saved in figshare [TBD]

Exampeles are as follows:

  • Generate dataset for pre-trained reaction encodings
rlsim-gen_pretrain_data -c '/path/to/config'
  • Train a model for deep reinforcement learning (DRL)
rlsim rl-train -c '/path/to/context_bandit/config' # Contextual Bendit
rlsim rl-train -c '/path/to/dqn/config' # Deep Q Network training
  • Deploy DRL
rlsim rl-deploy -c '/path/to/tks/config' # Transition kinetics simulation
rlsim rl-deploy -c '/path/to/dqn/config' # Lower-energy state sampling
  • Generate dataset for time estimator
rlsim-gen_time_dataset -f '/path/to/poscars` -c '/path/to/config' -s 30 -n 100
  • Train a time estimator
rlsim time-train -c '/path/to/time/config'
  • Estimate time using the time estimator
rlsim-estimate_time -m t_net_binary '/path/to/model' -v 1 256 -t 300 -i '/path/to/trajectory -n 10 -s '/path/to/save_dir' -d cuda

examples of configurations are saved in conifgs

Citation

@misc{chun2024learningmeanpassagetime,
      title={Learning Mean First Passage Time: Chemical Short-Range Order and Kinetics of Diffusive Relaxation}, 
      author={Hoje Chun and Hao Tang and Rafael Gomez-Bombarelli and Ju Li},
      year={2024},
      eprint={2411.17839},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci},
      url={https://arxiv.org/abs/2411.17839}, 
}