(Self-Consistent Determination of Long-Range Electrostatics in Neural Network Potentials)
报告人（单位）
高昂 研究员（北京邮电大学）
报告时间
2022年3月12日 10:00
主办方
兰州大学兰州理论物理中心（筹）
直播二维码
报告人介绍
Ang Gao is a research fellow at Beijing University of Posts and Telecommunications. He received his B.A in physics from Tsinghua University and PhD in chemical physics from University of Maryland, College Park. He worked as a postdoctoral research at MIT on biophysics. His main research interest is to combine statistical mechanics and molecular dynamics simulation to understand the dynamics of chemical and biological systems.
报告摘要
Machine learning has the potential to revolutionize the field of molecular simulation through the development of efficient and accurate models of interatomic interactions. In particular, neural network models can describe interactions at the level of accuracy of quantum mechanics-based calculations, but with a fraction of the cost, enabling the simulation of large systems over long timescales with ab initio accuracy. However, implicit in the construction of neural network potentials is an assumption of locality, wherein atomic arrangements on the scale of about a nanometer are used to learn interatomic interactions. Because of this assumption, the resulting neural network models cannot describe long-range interactions that play critical roles in dielectric screening and chemical reactivity. To address this issue, we introduce the self-consistent field neural network (SCFNN) model — a general approach for learning the long-range response of molecular systems in neural network potentials. The SCFNN model relies on a physically meaningful separation of the interatomic interactions into short- and long-range components, with a separate network to handle each component. We demonstrate the success of the SCFNN approach in modeling the dielectric properties of liquid water, and show that the SCFNN model accurately predicts long-range polarization correlations and the response of water to applied electrostatic fields.