Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

Summary: Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design....

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Bibliographic Details
Main Authors: Guanjie Wang, Changrui Wang, Xuanguang Zhang, Zefeng Li, Jian Zhou, Zhimei Sun
Format: Article
Language:English
Published: Elsevier 2024-05-01
Series:iScience
Subjects:
Chemistry
Computer science
Materials science
Physics
Online Access:http://www.sciencedirect.com/science/article/pii/S2589004224008952

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http://www.sciencedirect.com/science/article/pii/S2589004224008952

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