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 torch_geometric
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

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

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}, 
}