Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing
Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing
复制标题
基于机器学习的2D材料点缺陷设计，用于量子和神经形态信息处理
DOI：
10.1021/acsnano.0c05267
复制
发表时间：
2020-10-27
期刊：
影响因子：
17.1
通讯作者：
Shenoy, Vivek B.
中科院分区：
文献类型：
--
作者：
Frey, Nathan C.;Akinwande, Deji;Shenoy, Vivek B.
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.
