README.md

TherML: closed loop materials discovery of thermoelectric using error correction learning

Table of Contents

• How to cite
• Installation
• Data
• Structure of the repository
• Usage
• License

How to install:

• First install the prerequisite libraries:
  • PyTorch, ASE, Pymatgen, TPOT, Scikit-learn, Matminer
• Clone this library locally as: git clone https://github.com/hitarth64/therml
• Optional installs (refer to optionals for more information):
  • ROOST from https://github.com/CompRhys/roost
  • CrabNet from https://github.com/anthony-wang/CrabNet

Data:

• We use two sets of data in this study:
  • Thermoelectric property database from MRL @ UCSB
  • Closed-loop experimental dataset

Structure of the repository:

• therml directory:
  • Contains files, modules and data required for training accurate ML model to perform error-correction learning and rank materials
  • Please refer to the in-directory README file for more information
• therml/saved_models directory:
  • Contains the checkpoint of our model with highest cross-validation score
• therml/prior_models directory:
  • All the prior-models trained using Magpie, Roost and CrabNet (refer to manuscript for definition of prior-models)
  • Please refer to the in-directory README file for more information

Usage:

• You can perform the inference using:

  • python inference.py
  • Modify the inquiry dataloader within inference.py to rank new material candidates

• You can perform error-correction learning using:

  • python hpo_dense.py
  • Enables you to perform hyperparameter search for the error-correction model.
  • It is setup by default, to train and cross-validate on all the data collected until the last round (which is what we did)

• If you encounter any problem, feel free to start a discussion in the Issues

How to cite:

@article{https://doi.org/10.1002/adma.202302575,
author = {Choubisa, Hitarth and Haque, Md Azimul and Zhu, Tong and Zeng, Lewei and Vafaie, Maral and Baran, Derya and Sargent, Edward H},
title = {Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials},
journal = {Advanced Materials},
volume = {n/a},
number = {n/a},
pages = {2302575},
doi = {https://doi.org/10.1002/adma.202302575},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202302575},

License:

TherML is released under the MIT License.