【嘉宾介绍】

       王涵于2011年获得北京大学理学博士学位，之后前往柏林自由大学进行博士后研究。2014年加入北京应用物理与计算数学研究所，现任副研究员。王涵的主要研究兴趣为分子模拟中的多尺度建模与计算方法，包括深度学习在分子建模中的应用。王涵在Physical Review Letters, Physical Review X, Journal of Chemical Theory and Computation等SCI索引期刊上发表第一作者或通讯作者文章三十余篇。2019年入选北京市青年人才托举工程，并获得中国数学会计算数学分会第五届青年创新奖。

【报告简介】

An accurate description of the interatomic potential energy surface (PES) is one of the central problems in molecular simulations. For a long time, one has to choose between the first principle PESs that are accurate but computationally expensive and the empirical PESs (force fields) that are efficient but of limited accuracy. We discuss the solution to this dilemma in two aspects: PES construction and data generation. In terms of PES construction, we introduce the Deep Potential (DP) method, which faithfully represents the first principle PES by a symmetry-preserving deep neural network. In terms of data generation, we present a new concurrent learning scheme named Deep Potential Generator (DP-GEN). This approach automatically generates the most compact training dataset that enables the training of DP with uniform accuracy. By contrast to the empirical PESs, the DP-GEN opens the opportunity of continuously improving the quality of DP by exploring the chemical and configurational space of the system. After a few examples of DP and DP-GEN, we introduce the open-source implementations of DP named DeePMD-kit, and a recent GPU optimization of DeePMD-kit for the world's fastest supercomputer, which makes possible nanosecond simulation of 100 million atoms with ab initio accuracy in a day.

【报告时间】

2020年06月19日

19:00 - 21:00

【会议信息】

会议平台:ZOOM

会议号:928 733 84224

会议密码:327382

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“智能计算与应用”同济大学数学中心