High-throughput computation of novel ternary B–C–N structures and carbon allotropes with electronic-level insights into superhard materials from machine learning
High-throughput computation of novel ternary B–C–N structures and carbon allotropes with electronic-level insights into superhard materials from machine learning
复制标题
新型三元B-C-N结构和碳同素异形体的高通量计算，以及机器学习对超硬材料的电子水平洞察
DOI：
10.1039/d1ta07553e
复制
发表时间：
2021
影响因子：
11.9
通讯作者：
Mohammed Al-Fahdi;T. Ouyang;Ming Hu
中科院分区：
文献类型：
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作者：
Mohammed Al-Fahdi;T. Ouyang;Ming Hu
通过高通量计算筛选并提出了具有超高硬度的新型碳同素异形体和三元B-C-N结构。机器学习提供了对超硬材料的电子级见解。
Novel carbon allotropes and ternary B–C–N structures with ultrahigh hardness were screened and proposed by high-throughput computation. Electronic-level insights into superhard materials were provided from machine learning.