报告摘要：

　　It is known that energy materials undergo dynamic structural changes at in-situ/in-operando conditions. Yet, the majority of computational studies only consider the static structures of energy materials. When the materials are submerged in liquid solution, dynamic solvation effects are usually ignored, or treated with dielectric continuum models, often lacking validation. The situations are about to change. Thanks to the latest development of in-situ experimental techniques and state-of-the-art computational methods, materials dynamics has recently drawn more and more attentions in many research areas. In this talk, I will present our recent progress on modeling dynamic catalysis and electrochemistry using Ab Initio Molecular Dynamics (AIMD). When statistical sampling is getting too expensive, we develop efficient simulation protocols of Artificial Intelligence accelerated Ab Initio Molecular Dynamics (AI²MD), enabled by the powerful Deep Potentials [6].

　　References

　　[1] J. Cheng*, J. VandeVondele*, Phys. Rev. Lett. 2016, 116, 086402.

　　[2] J.-B. Le, M. Iannuzzi, A. Cuesta, J. Cheng*, Phys. Rev. Lett. 2017, 119, 016801.

　　[3] C.-Y. Li, J.-B. Le, J.-F. Li*, J. Cheng*, Z.-Q Tian, et al. Nature Mater. 2019, 18, 697.

　　[4] J.-J. Sun, J. Cheng*, Nature Commun. 2019, 10, 5400.

　　[5] J.-B. Le, Q.-Y. Fan, J.-Q. Li, J. Cheng*, Sci. Adv. 2020, 6, eabb1219.

　　[6] H. Wang, L. Zhang, J. Han, W. E, Computer Physics Communications 2018, 228, 178.

【报告人简介】程俊教授，2002、2005年分别获得上海交通大学化学化工学院学士和硕士学位。2008年在英国贝尔法斯特女王大学获得博士学位。之后在剑桥大学化学系从事博士后研究，2010年获得剑桥大学Emmanuel学院Junior Research Fellow职位开始独立研究工作。2013年获英国阿伯丁大学永久教职。2015年获国家高层次青年人才项目，全职回厦门大学担任闽江特聘教授。主要研究方向为表界面模拟方法发展、计算电化学、理论催化等。在Nature Materials, Nature Catalysis, Nature Commun., Science Advance, Phys. Rev. Lett.等学术期刊发表论文90余篇。2019年获得中国电化学青年奖。现担任《电化学》副主编、《ACS Catalysis》青年编委、AIP期刊Chem. Phys. Rev.编委顾问、《中国化学快报》青年编委、国际电化学出版委员会委员等。