报告题目：Modeling the interatomic potential by deep learning
摘要：In silico design of molecules requires an accurate description of the interatomic potential. In the context of molecular simulation, one usually faces the dilemma that the first principle potential energies are accurate but computationally expensive, while the empirical force fields are efficient but of limited accuracy. In this talk, we try to solve this dilemma by using recently developed deep learning and active learning algorithms. We discuss the topic in two aspects: model construction and data generation. In terms of model construction, we introduce the Deep Potential scheme based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with first principle data. We show that the proposed scheme provides an efficient and accurate protocol for a variety of systems, including bulk materials and molecules, and, in particular, for some challenging systems like a high-entropy alloy system. In terms of data generation, we present a new active learning approach named Deep Potential Generator (DP-GEN), which is an iterative procedure including exploration, labeling, and training steps. By the example system of Al-Mg alloys, we demonstrate that DP-GEN can generate uniformly accurate potential energy models with a minimum number of labeled data.