報告摘要：

　　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.

【報告人簡介】程俊教授，2002、2005年分别獲得上海交通大學化學化工學院學士和碩士學位。2008年在英國貝爾法斯特女王大學獲得博士學位。之後在劍橋大學化學系從事博士後研究，2010年獲得劍橋大學Emmanuel學院Junior Research Fellow職位開始獨立研究工作。2013年獲英國阿伯丁大學永久教職。2015年獲國家高層次青年人才項目，全職回廈門大學擔任閩江特聘教授。主要研究方向為表界面模拟方法發展、計算電化學、理論催化等。在Nature Materials, Nature Catalysis, Nature Commun., Science Advance, Phys. Rev. Lett.等學術期刊發表論文90餘篇。2019年獲得中國電化學青年獎。現擔任《電化學》副主編、《ACS Catalysis》青年編委、AIP期刊Chem. Phys. Rev.編委顧問、《中國化學快報》青年編委、國際電化學出版委員會委員等。