Discovery of 2D materials using Transformer Network based Generative Design

TL;DR

This paper introduces a Transformer-based generative pipeline for discovering new 2D materials, successfully identifying four stable candidates verified by DFT calculations, advancing materials design methods.

Contribution

The study presents a novel Transformer-based generative approach for large-scale 2D materials discovery, integrating neural language models with DFT validation.

Findings

Four new stable 2D materials identified: NiCl₄, IrSBr, CuBr₃, CoBrCl

Generative models effectively produce viable 2D compositions

Pipeline demonstrates potential for discovering functional materials

Abstract

Two-dimensional (2D) materials have wide applications in superconductors, quantum, and topological materials. However, their rational design is not well established, and currently less than 6,000 experimentally synthesized 2D materials have been reported. Recently, deep learning, data-mining, and density functional theory (DFT)-based high-throughput calculations are widely performed to discover potential new materials for diverse applications. Here we propose a generative material design pipeline, namely material transformer generator(MTG), for large-scale discovery of hypothetical 2D materials. We train two 2D materials composition generators using self-learning neural language models based on Transformers with and without transfer learning. The models are then used to generate a large number of candidate 2D compositions, which are fed to known 2D materials templates for crystal structure prediction. Next, we performed DFT computations to study their thermodynamic stability based on energy-above-hull and formation energy. We report four new DFT-verified stable 2D materials with zero e-above-hull energies, including NiCl$_4$, IrSBr, CuBr$_3$, and CoBrCl. Our work thus demonstrates the potential of our MTG generative materials design pipeline in the discovery of novel 2D materials and other functional materials.