宁波东方理工大学

报告摘要：

The interface between metal oxides and aqueous electrolytes plays a crucial role in a wide range of applications, including photo- and electrocatalytic water splitting, solar energy conversion, and anti-corrosion coatings. However, accurate simulation and understanding of such interfaces remain a long-standing challenge due to their inherent complexity. In this talk, I will present our recent progress on using machine learning to simulate and analyze oxide–electrolyte interfaces. On the simulation side, we employ the Deep Potential Long-Range (DPLR) method to enable large-scale molecular dynamics simulations with ab initio accuracy. I will highlight the importance of explicitly incorporating long-range electrostatics and discuss how to correctly introduce external electric fields under periodic boundary conditions. On the analysis side, I will introduce our machine-learning-based approach to calculate electrostatic potential and our design of machine-learning collective variables, which enable a microscopic understanding of chemical reactions at the interface. These developments allow us to obtain molecular-scale insights into the structure of the electrical double layer and uncover a novel mechanism by which external electric fields modulate interfacial chemical reactions.

报告人简介：

张春一，宁波东方理工大学（暂名）理学部助理教授、博导。2019博士毕业于北京大学，曾在美国普林斯顿大学（导师：Annabella Selloni、Roberto Car）和美国天普大学（导师：Xifan Wu、Michael Klein）从事博士后研究。2025年加入东方理工。主要研究方向为计算凝聚态物理、水科学、界面科学等。近年来，在Nature Communications、Physical Review Letters、Physical Review B、The Journal of Physical Chemistry B 等期刊发表多篇一作论文，研究成果曾被Science杂志作为专题新闻文章报道。

邀请人：张萃 副研究员（8609）

联系人 ：王立芬 副研究员（9963）