High-throughput computation of novel ternary B–C–N structures and carbon allotropes with electronic-level insights into superhard materials from machine learning-Reference-Cited by-同舟云学术

High-throughput computation of novel ternary B–C–N structures and carbon allotropes with electronic-level insights into superhard materials from machine learning

Published:2021 Issue:48 Volume:9 Page:27596-27614
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Al-Fahdi Mohammed¹^ORCID,Ouyang Tao²^ORCID,Hu Ming¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA

2. Hunan Key Laboratory for Micro-Nano Energy Materials and Device, School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, Hunan, China

Abstract

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.

Funder

Office of Experimental Program to Stimulate Competitive Research

National Science Foundation

Scientific Research Foundation of Hunan Provincial Education Department

Natural Science Foundation of Hunan Province

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/TA/D1TA07553E

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