未来电池讲坛：Computation and machine learning to accelerate materials discovery
主 讲 人：汪硕，麻省理工博士后研究员
邀 请 人：溥渊未来技术学院未来电池研究中心
时 间：2023年12月13日14:00
地 点：上海交通大学包玉刚图书馆东翼楼202会议室
讲坛摘要：
Computation has emerged as a transformative tool in materials science, reshaping our understanding, prediction, and design of materials. Utilizing computational simulations, researchers can uncover intricate atomic-level details, predict material responses in diverse conditions, and efficiently screen extensive databases for potential candidates. The integration of machine learning has ushered in a new era, acting as a catalyst by combining algorithms and data analysis to enhance the precision and efficiency of calculations. In this work, I elucidate the application of computation in evaluating the electrochemical and interface stability of various anion chemistries in all-solid-state batteries, establishing a comprehensive design principle for inorganic solid-state electrolytes. Employing a synergistic approach that combines first-principles calculations, molecular dynamics simulations, and machine learning, we introduce a novel tool, Density of Atomistic States (DOAS), to assess and quantify frustration, such as the disordering of the mobile ion-sublattice, in superionic conductors. DOAS provides fundamental insights into ionic diffusion in solids. The integration of these insights into high-throughput screening has led to the discovery of numerous novel Li-ion and Na-ion conductors. Through close collaboration with experimental groups, these findings have significantly expedited the identification of world-leading lithium/sodium-ion solid-state electrolyte materials, bridging the gap between atomic-level understanding and practical applications in energy storage and beyond.
主讲人简介：
汪硕，现为麻省理工学院材料系博士后（导师: Prof. Yang Shao-Horn与Prof. Jeffrey C. Grossman），2020-2022期间为马里兰大学材料系博士后（导师:莫一非教授），博士毕业于北京大学工学院材料系(导师:孙强教授），研究方向为运用第一性原理计算、分子动力学、机器学习加速设计新能源材料，包括二维材料、电极材料、催化材料和固态电解质材料等。目前在Nat. Com, J. Am. Chem. Soc., Angew.Chem., PNAS, Energy Environ. Sci., Sci. Adv, Adv. Energy Mater., Adv. Funct. Mater., ACS Energy Letter, JACS Au, Chem. Mat., J. Mater. Chem. A，J. Phys. Chem.Lett. 等杂志发表文章近五十余篇，目前H因子为24，总引用超过2700次。
