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Universal machine learning potential accelerates atomistic modeling of materials
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作者 Zhongheng Fu Dawei Zhang 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2023年第8期1-2,I0002,共3页
With the rapid development of computer techniques,atomistic modeling is playing an increasingly important role in understanding the structure-activity relationship of materials.Molecular dynamics (MD) is a computation... With the rapid development of computer techniques,atomistic modeling is playing an increasingly important role in understanding the structure-activity relationship of materials.Molecular dynamics (MD) is a computational simulation approach to predicting the structural evolution of an atomic system over time,widely used to understand physical and chemical phenomena including phase transition,diffusion,crystallization,and reaction [1]. 展开更多
关键词 MACHINELEARNING Atomisticmodeling Neural networkpotential Solid-statematerials
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