讲座题目：Machine Learning Accelerated Material Discovery at High Pressure

时间：7月22日 上午10：00

地点：理学楼A301

报告人：姚延荪 教授

报告人简介：姚延荪，加拿大萨斯喀彻温大学物理系教授、物理与工程物理系系主任。他在北京理工大学获得学士和硕士学位，2008年在萨斯喀彻温大学获得博士学位，主要研究方向为理论凝聚态物理。随后，在渥太华的加拿大国家研究委员会完成博士后工作，并成为一名全职研究员，后于2012年回到美丽的萨斯喀彻温省。研究工作主要集中于高压和低温极端条件下新型材料结构与性能的理论研究。姚延荪目前是《Journal of Physics: Condensed Matter》编委会成员。发表论文110余篇，其中包括10篇Phys. Rev. Lett.，35篇Phys. Rev. B，共被引4300余次。

讲座内容：Predicting new phases and simulating corresponding phase transitions in solid materials requires an accurate description of the potential energy surface (PES). While density-functional theory (DFT)-based calculations can provide the required accuracy, they are computationally prohibitive for large systems and/or extended simulation times. In this talk,I will present a popular approach for predicting new structures and simulating reconstructive phase transitions, which integrates random structure search, metadynamics simulation and machine learning representation of high-dimensional PES. This machine learning accelerated method can reach an accuracy comparable to DFT-based calculations but with computational costs reduced by several orders of magnitude less and a nearly linear scaling with system size.I will demonstrate the dynamics simulation of pressure-induced phase transitions in gallium nitride and silicon.Using a half-million atom simulation box, our large-scale simulation reveals the phase transition in remarkable detail, showcasing a sequential change in the phase transition mechanism from collective modes to nucleation and growth. Additionally, I will illustrate the static simulation for structure prediction of sodium under high pressure, where we successfully identified a crystal structure corresponding to a long-elusive phase of sodium discovered experimentally in 2008.

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理学院

2024年7月18日