Abstract
Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. We introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1899-1925 |
| Number of pages | 27 |
| Journal | Chemical Society Reviews |
| Volume | 51 |
| Issue number | 6 |
| DOIs | |
| State | Published - Mar 5 2022 |
Funding
J. H. Chen acknowledges the financial support from the US National Science Foundation (NSF) Scalable Nanomanufacturing Program (NSF CMMI-1727846 and CMMI-2039268) and the NSF Future Manufacturing Program (NSF CMMI-2037026). This work is also supported by the Laboratory Directed Research and Development (LDRD) Program (LDRD 2020-0171) from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. L. Wang and M. K. Y. Chan acknowledge funding from the National Science Foundation MRSEC program under grant number DMR-1720139. Use of the Center for Nanoscale Materials, an Office of Science user facility, was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.
ASJC Scopus subject areas
- General Chemistry