The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning

Date

2021

Authors

Yin, Hang
Sun, Zhehao
Wang, Zhuo
Tang, Dawei
Pang, Cheng Heng
Yu, Xuefeng
Barnard, Amanda
Zhao, Haitao
Yin, Zongyou

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Abstract

Machine learning (ML) has experienced rapid development in recent years and been widely applied to assist studies in various research areas. Two-dimensional (2D) materials, due to their unique chemical and physical properties, have been receiving increasing attention since the isolation of graphene. The combination of ML and 2D materials science has significantly accelerated the development of new functional 2D materials, and a timely review may inspire further ML-assisted 2D materials development. In this review, we provide a horizontal and vertical summary of the recent advances at the intersection of the fields of ML and 2D materials, discussing ML-assisted 2D materials preparation (design, discovery, and synthesis of 2D materials), atomistic structure analysis (structure identification and formation mechanism), and properties prediction (electronic properties, thermodynamic properties, mechanical properties, and other properties) and revealing their connections. Finally, we highlight current research challenges and provide insight into future research opportunities.

Description

Keywords

Citation

Source

Cell Reports Physical Science

Type

Journal article

Book Title

Entity type

Access Statement

Open Access

License Rights

CC BY-NC-ND license

DOI

10.1016/j.xcrp.2021.100482

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