Deep-Learning Estimation of Band Gap with the Reading-Periodic-Table Method and Periodic Convolution Layer

TL;DR

This paper extends a deep learning approach that reads the periodic table to estimate material band gaps, introducing periodic convolution layers to better capture elemental periodicity and improve prediction accuracy.

Contribution

The paper introduces periodic convolution layers that treat the periodic table as a continuous, cyclic structure, enhancing deep learning models for materials property prediction.

Findings

The extended method accurately estimates band gaps without material descriptors.

Periodic convolution layers improve model performance by capturing periodicity.

Open code and data facilitate further research.

Abstract

We verified that the deep learning method named reading periodic table introduced by ref. Deep Learning Model for Finding New Superconductors, which utilizes deep learning to read the periodic table and the laws of the elements, is applicable not only for superconductors, for which the method was originally applied but also for other problems of materials by demonstrating band gap estimations. We then extended the method to learn the laws better by directly learning the cylindrical periodicity between the right- and left-most columns in the periodic table at the learning representation level, that is, by considering the left- and right-most columns to be adjacent to each other. Thus, while the original method handles the table as is, the extended method treats the periodic table as if its two edges are connected. This is achieved using novel layers named periodic convolution layers, which can handle inputs exhibiting periodicity and may be applied to other problems related to computer vision, time series, and so on for data that possess some periodicity. In the reading periodic table method, no material feature or descriptor is required as input. We demonstrated two types of deep learning estimation: methods to estimate the existence of a bandgap, and methods to estimate the value of the bandgap given when the existence of the bandgap in the materials is known. Finally, we discuss the limitations of the dataset and model evaluation method. We may be unable to distinguish good models based on the random train-test split scheme; thus, we must prepare an appropriate dataset where the training and test data are temporally separate. The code and data are open.