期刊
ACS NANO
卷 14, 期 10, 页码 13406-13417出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsnano.0c05267
关键词
machine learning; 2D materials; defects; DFT; quantum emission; resistive switching; neuromorphic computing
类别
资金
- Army Research Office [W911NF-16-10447]
- U.S. National Science Foundation [EFMA-542879, CMMI-1727717]
- Department of Defense through the National Defense Science & Engineering Graduate Fellowship program
- Presidential Early Career Award for Scientists and Engineers (PECASE) through the Army Research Office [W911NF-16-1-0277]
- NSF [ECCS-1809017, DMR-1905853, DMR1720530]
- U.S. Army Research Office [W911NF-19-1-0109]
Engineered point defects in two-dimensional (2D) materials offer an attractive platform for solid-state devices that exploit tailored optoelectronic, quantum emission, and resistive properties. Naturally occurring defects are also unavoidably important contributors to material properties and performance. The immense variety and complexity of possible defects make it challenging to experimentally control, probe, or understand atomic-scale defect-property relationships. Here, we develop an approach based on deep transfer learning, machine learning, and first-principles calculations to rapidly predict key properties of point defects in 2D materials. We use physics-informed featurization to generate a minimal description of defect structures and present a general picture of defects across materials systems. We identify over one hundred promising, unexplored dopant defect structures in layered metal chalcogenides, hexagonal nitrides, and metal halides. These defects are prime candidates for quantum emission, resistive switching, and neuromorphic computing.
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