Frey Nathan C, Akinwande Deji, Jariwala Deep, Shenoy Vivek B
Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.
Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States.
ACS Nano. 2020 Oct 27;14(10):13406-13417. doi: 10.1021/acsnano.0c05267. Epub 2020 Sep 16.
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.
二维（2D）材料中的工程化点缺陷为利用定制的光电、量子发射和电阻特性的固态器件提供了一个有吸引力的平台。自然存在的缺陷也是材料特性和性能不可避免的重要贡献因素。可能缺陷的种类繁多且复杂，使得在实验上控制、探测或理解原子尺度的缺陷-特性关系具有挑战性。在这里，我们开发了一种基于深度迁移学习、机器学习和第一性原理计算的方法，以快速预测二维材料中点缺陷的关键特性。我们使用物理信息特征化来生成缺陷结构的最小描述，并展示跨材料系统的缺陷总体情况。我们在层状金属硫族化物、六方氮化物和金属卤化物中识别出一百多种有前景的、未被探索的掺杂剂缺陷结构。这些缺陷是量子发射、电阻开关和神经形态计算的主要候选对象。
