报告题目 (Title)： 自动生成神经网络势能面：增强自组织增量神经网络深度势能方法
报告人 (Speaker)：朱通 副教授（华东师范大学&上海纽约大学）
报告时间 (Time)：2021年11月25日(周四) 19:00
报告地点 (Place)： 线上腾讯：https://meeting.tencent.com/dm/gfdipmPOes1R
会议ID：645 631 478
邀请人(Inviter)：李永乐 副教授
主办部门：理学院物理系
摘要：
In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical mechanic methods. However, the development of NNPES is highly non-trivial. In particular, it has been troubling to construct a dataset that is as small as possible yet can cover the target chemical space. In this work, an ESOINN-DP method is developed, which has the enhanced self-organizing incremental neural-network (ESOINN) and a newly proposed error indicator at its core. With ESOINN-DP, one can construct the NNPES with little human intervention, and this method ensures that the constructed reference dataset covers the target chemical space with minimum redundancy. The performance of the ESOINN-DP method has been well validated by developing neural network potential energy surfaces for water clusters and by de-redundancy of a sub-data set of the ANI-1 database. We believe that the ESOINN-DP method provides a novelty idea for the construction of NNPES and especially, the reference datasets, and it can be used for MD simulations of various gas-phase and condensed-phase chemical systems.
