tomo835g/Superconductors

Introduction

The data and the code for ''Deep-Learning Estimation of Band Gap with the Reading-Periodic-Table Method and Periodic Convolution Layer'' by Tomohiko Konno, Journal of the Physical Society of Japan (2020)

The paper is open access, and Arxiv version is found here.

Condition

The data and the codes can be used under the condition that you cite the following two papers. Also see Licence.

@article{Konno2018DeepLM,
  title={Deep Learning Model for Finding New Superconductors},
  author={Tomohiko Konno and H. Kurokawa and F. Nabeshima and Y. Sakishita and Ryo Ogawa and I. Hosako and A. Maeda},
  journal={ArXiv},
  year={2018},
  volume={abs/1812.01995},
  }

@article{doi:10.7566/JPSJ.89.124006,
author = {Konno ,Tomohiko},
title = {Deep-Learning Estimation of Band Gap with the Reading-Periodic-Table Method and Periodic Convolution Layer},
journal = {Journal of the Physical Society of Japan},
volume = {89},number = {12},pages = {124006},year = {2020},
doi = {10.7566/JPSJ.89.124006},
URL = { https://doi.org/10.7566/JPSJ.89.124006},
eprint = {https://doi.org/10.7566/JPSJ.89.124006},
}

Code

1. The code that transforms chemical formula like H₂O into reading periodic table type data format. chemical_formula_to_reading_periodic_table.py

An example

test_formula = 'H2He5'
reading_periodic_table = TransformReadingPeriodicTable(formula=test_formula)
reading_periodic_table_form_data = reading_periodic_table.formula_to_periodic_table()
print(reading_periodic_table_form_data)
>> must print 4*7*32 data (rpt).
formula_dict_form=reading_periodic_table.from_periodic_table_form_to_dict_form(reading_periodic_table_form_data)
print(formula_dict_form)
>> must print {'H':2,'He':5}

2. The code for model (Pytorch) network_band_gap_estimation.py It also requires periodic_shift_conv2D.py

Due to the scarce human resource, we can only provide above codes.

Data

1. The data used for band gap binary classification. band_gap_data_binary_used.csv The list of materials with band gap existence; 0 for no band gap, and 1 for band gap.

2. The data used for band gap value regression. band_gap_data_reg_used.csv The list of materials and band gap values.

Requirements

torch, pymatgen