Deep Learning on Atomistic Physical Fields of Graphene for Strain and Defect Engineering

Abstract

Strain and defect engineering have profound applications in two‐dimensional materials, where it is important to determine the equilibrated atomistic structures with defect conditions under mechanical deformations for computational materials design. Nevertheless, how to efficiently predict relaxed atomistic structures and the associated physical fields on each atom or bond under evolving mechanical deformations remains as an essential challenge. To address this issue, a deep neural network architecture is designed to embed the state of applied strains into the defect‐engineered atomistic geometry, so that deformation‐coupled physical fields of interests on atoms or bonds can be predicted or derived over continuous state of deformations. For demonstration, the combination of applied tensile strains and shear strain on monolayer graphene with random distribution of Stone–Wales defects and vacancy defects is considered. The unique advantage of this framework is the development of strain‐embedding concept combined with generative adversarial network, which can be feasibly extended to other material and other conditions. The computational approach sheds light on boosting the efficiency of evaluating physical properties of 2D materials under complex strain and defect states.

Keywords

• 2D materials
• atomistic physical fields
• generative adversarial network
• strain and defect engineering
• strain embeddings