报告题目:Modeling the interatomic potential by deep learning

时间: 2020年11月19日上午 10:00-11:00

地点: 计算所环保园1号楼327会议室

报告人:王涵 研究员 北京应用物理与数学研究所

报告摘要:

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.

主讲人简介:

王涵,北京应用物理与计算数学研究所研究员。2006年获北京大学计算数学专业本科学位,2011年获北京大学计算数学博士学位。2007-2008,德国马克斯普朗克研究所访问学习。2011-2014年在德国自由大学(Freie Universität Berlin)从事博士后研究。2014-2018年在中国工程物理研究院高性能数值模拟软件中心担任研究科学家。2018年至今在北京应用物理与计算数学研究所担任研究科学家。  

王涵研究员长期从事分子动力学的数值分析和快速算法研究,以及多尺度模型和模拟研究,发表学术论文30余篇。王涵研究员及其团队研发的基于深度学习的分子动力学原子间相互作用势建模项目在专业领域内受到广泛的关注和认可,并成功发布了开源软件DeePMD-kit,对该领域产生了重要的影响。

By gxnzx