工作流程
计算机科学
半导体
理论(学习稳定性)
任务(项目管理)
空格(标点符号)
相空间
从头算
机器学习
计算科学
人工智能
材料科学
系统工程
工程类
化学
物理
光电子学
有机化学
数据库
热力学
操作系统
作者
Yu-Xin Guo,Yong-Bin Zhuang,Jueli Shi,Jun Cheng
摘要
Semiconductor alloy materials are highly versatile due to their adjustable properties; however, exploring their structural space is a challenging task that affects the control of their properties. Traditional methods rely on ad hoc design based on the understanding of known chemistry and crystallography, which have limitations in computational efficiency and search space. In this work, we present ChecMatE (Chemical Material Explorer), a software package that automatically generates machine learning potentials (MLPs) and uses global search algorithms to screen semiconductor alloy materials. Taking advantage of MLPs, ChecMatE enables a more efficient and cost-effective exploration of the structural space of materials and predicts their energy and relative stability with ab initio accuracy. We demonstrate the efficacy of ChecMatE through a case study of the InxGa1−xN system, where it accelerates structural exploration at reduced costs. Our automatic framework offers a promising solution to the challenging task of exploring the structural space of semiconductor alloy materials.
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