Unveiling the complex structure-property correlation of defects in 2D materials based on high throughput datasets

TL;DR

This paper introduces a new platform and datasets for applying machine learning to understand and design defect properties in 2D materials, aiding materials engineering.

Contribution

It develops a comprehensive database of defect properties in 2D materials and demonstrates its potential for machine learning-driven materials design.

Findings

Provides datasets of defects in 2D materials calculated with DFT.

Offers data-driven insights into defect behavior in 2D materials.

Lays groundwork for machine learning models in materials design.

Abstract

Modification of physical properties of materials and design of materials with on-demand characteristics is at the heart of modern technology. Rare application relies on pure materials--most devices and technologies require careful design of materials properties through alloying, creating heterostructures of composites or controllable introduction of defects. At the same time, such designer materials are notoriously difficult for modelling. Thus, it is very tempting to apply machine learning methods for such systems. Unfortunately, there is only a handful of machine learning-friendly material databases available these days. We develop a platform for easy implementation of machine learning techniques to materials design and populate it with datasets on pristine and defected materials. Here we describe datasets of defects in represented 2D materials such as MoS2, WSe2, hBN, GaSe, InSe, and black phosphorous, calculated using DFT. Our study provides a data-driven physical understanding of complex behaviors of defect properties in 2D materials, holding promise for a guide to the development of efficient machine learning models. In addition, with the increasing enrollment of datasets, our database could provide a platform for designing of materials with predetermined properties.