The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning
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Authors
Yin, Hang
Sun, Zhehao
Wang, Zhuo
Tang, Dawei
Pang, Cheng Heng
Yu, Xuefeng
Barnard, Amanda
Zhao, Haitao
Yin, Zongyou
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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.
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Cell Reports Physical Science
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Open Access
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CC BY-NC-ND license
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