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Updated: Sep 13, 2025

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics
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使用通用机器学习原子间潜力的材料缺陷选

Ethan Berger1,2, Mohammad Bagheri3, Hannu-Pekka Komsa1

  • 1Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, P.O. Box 4500, Oulu, FIN-90014, Finland.

Small (Weinheim an der Bergstrasse, Germany)
|August 4, 2025
PubMed
概括

全球机器学习的原子间潜力加速了新材料的发现. 这项研究证明了它们在选有缺陷的材料和识别新的稳定化合物的准确性,大大推进了材料科学.

关键词:
两维材料是二维材料.一个基准的基准指标.缺陷 缺陷 缺陷 缺陷 缺陷机器学习 原子间潜力职位空缺 职位空缺 职位空缺

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