报告人： Prof. William A. Goddard, III (Director of Caltech-Soochow Multiscale nanoMaterials Genome Center (MnG); FUNSOM, Soochow University, Suzhou China; Charles and Mary Ferkel Professor of Chemistry, Materials Science, and Applied Physics; Director, Materials and Process Simulation Center (MSC), California Institute of Technology (139-74), Pasadena, CA 91125, UAS)

报告题目：Multiscale simulations of Materials from machine learning to design improved Electrocatalysts to a new paradigm for water that explains the anomalies in supercooled water

报告时间：10：00 am, Oct. 16 (Wednesday)

报告地点：909-B

Abstract: 

Advances in theory and methods of quantum mechanics (QM) are making it practical for first principles (de novo) predictions on complex materials.

Here we examine the mechanisms of complex electrocatalytic reactions using QM to predict the reaction mechanisms and kinetics of low index surfaces.

However, the best catalysts are often nanoparticles or nanowires of sizes 10nm and larger that may have 200K atoms and 10K surface sites, far too big for QM. This problem can be solved by using the ReaxFF reactive force field to grow the catalyst, while retaining QM accuracy for the reaction barriers. But with 10K surface sites, examining each one is impractical.To solve this problem, we show how to use machine learning to predict all 10K surface sites with QM accuracy.

Water plays an enormous role in phenomena from life to electrochemistry, but there remains puzzles such as the apparent supercooled critical point at ~228K. To explain such mysteries, we developed the RexPoN reactive force field that aims at retaining the accuracy of high-level ab initio QM (CCSDT) while remaining practical for 100,000 atoms. This leads to a new paradigm for the dynamics of water at the time scales of 100 femtoseconds, leading to the explanation of the puzzling properties of supercooled water.

联系人：程涛 教授

责任编辑：杨娟