原子轨道
从头算
理论(学习稳定性)
材料科学
密度泛函理论
带隙
总能量
晶体结构预测
能量(信号处理)
价(化学)
晶体结构
计算机科学
结晶学
机器学习
计算化学
物理
化学
量子力学
光电子学
电子
心理治疗师
流离失所(心理学)
心理学
作者
Wei-Chih Chen,Yogesh K. Vohra,Cheng-Chien Chen
出处
期刊:ACS omega
[American Chemical Society]
日期:2022-06-09
卷期号:7 (24): 21035-21042
被引量:5
标识
DOI:10.1021/acsomega.2c01818
摘要
We searched for new superhard B-N-O compounds with an iterative machine learning (ML) procedure, where ML models are trained using sample crystal structures from an evolutionary algorithm. We first used cohesive energy to evaluate the thermodynamic stability of varying B x N y O z compositions and then gradually focused on compositional regions with high cohesive energy and high hardness. The results converged quickly after a few iterations. Our resulting ML models show that B x+2N x O3 compounds with x ≥ 3 (like B5N3O3, B6N4O3, etc.) are potentially superhard and thermodynamically favorable. Our meta-GGA density functional theory calculations indicate that these materials are also wide bandgap (≥4.4 eV) insulators, with the valence band maximum related to the p-orbitals of nitrogen atoms near vacant sites. This study demonstrates that an iterative method combining ML and ab initio simulations provides a powerful tool for discovering novel materials.
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