Computational discovery of new 2D materials using deep learning generative models

TL;DR

This paper introduces a deep learning generative model combined with a classification approach to discover thousands of new 2D material compositions, with some confirmed as stable structures via DFT calculations, advancing materials discovery.

Contribution

It presents a novel deep learning framework for generating and validating new 2D materials, integrating composition generation, structure prediction, and stability confirmation.

Findings

Discovered 267,489 potential new 2D materials

Confirmed 12 new 2D/layered materials via DFT

Demonstrated effectiveness of generative models in materials discovery

Abstract

Two dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties. While several thousand 2D materials have been screened in existing materials databases, discovering new 2D materials remains to be challenging. Herein we propose a deep learning generative model for composition generation combined with random forest based 2D materials classifier to discover new hypothetical 2D materials. Furthermore, a template based element substitution structure prediction approach is developed to predict the crystal structures of a subset of the newly predicted hypothetical formulas, which allows us to confirm their structure stability using DFT calculations. So far, we have discovered 267,489 new potential 2D materials compositions and confirmed twelve 2D/layered materials by DFT formation energy calculation. Our results show that generative machine learning models provide an effective way to explore the vast chemical design space for new 2D materials discovery.