Researchers from Yunnan Normal University Report New Studies and Findings in the Area of Machine Learning (Combined Machine Learning and High-throughput Calcula tions Predict Heyd-scuseriaernzerhof Band Gap of 2d Materials and Potential ... )

云南师范大学的研究人员报告了机器学习领域的新研究和发现(结合机器学习和高通量计算预测了二维材料的heyd-scuseriaernzerhof带隙和潜在 ... )

Robotics & Machine Learning Daily News2024，Issue(Jun.17) ：40-41.

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Researchers from Yunnan Normal University Report New Studies and Findings in the Area of Machine Learning (Combined Machine Learning and High-throughput Calcula tions Predict Heyd-scuseriaernzerhof Band Gap of 2d Materials and Potential ... )

云南师范大学的研究人员报告了机器学习领域的新研究和发现(结合机器学习和高通量计算预测了二维材料的heyd-scuseriaernzerhof带隙和潜在 ... )

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摘要

由一名新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-一项关于机器学习的新研究现在已经可用。根据来自中国昆明的新闻报道，NewsRx记者称，"我们提出了一种新的焦油驱动方法，旨在利用全面的C2DB数据库E预测二维(2D)材料的Heyd-Scuseria-Ernzerhof(HSE)带隙。这种创新方法结合机器学习和密度泛函理论(DFT)计算来预测HSE带隙，导带最小值(C BM)。2176种二维材料的价带最大值为(VBM)。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting originating from Kunming, People’s Repub lic of China, by NewsRx correspondents, research stated, “We present a novel tar get-driven methodology devised to predict the Heyd-Scuseria-Ernzerhof (HSE) band gap of two-dimensional (2D) materials leveraging the comprehensive C2DB databas e. This innovative approach integrates machine learning and density functional t heory (DFT) calculations to predict the HSE band gap, conduction band minimum (C BM), and valence band maximum (VBM) of 2176 types of 2D materials.”

Key words

Kunming/People’s Republic of China/Asia/Cyborgs/Emerging Technologies/Machine Learning/Yunnan Normal University

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出版年

2024

Robotics & Machine Learning Daily News

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