未來電池講壇🧊🧜🏿♀️：Computation and machine learning to accelerate materials discovery
主 講 人：汪碩，麻省理工博士後研究員
邀 請 人🕠😏：意昂2体育未來電池研究中心
時 間：2023年12月13日14:00
地 點：意昂2体育平台包玉剛圖書館東翼樓202會議室
講壇摘要：
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.
主講人簡介：
汪碩🎃👼🏻，現為麻省理工學院材料系博士後（導師: Prof. Yang Shao-Horn與Prof. Jeffrey C. Grossman），2020-2022期間為馬裏蘭大學材料系博士後（導師:莫一非教授）👨🏿🦱，博士畢業於北京大學工學院材料系(導師:孫強教授），研究方向為運用第一性原理計算、分子動力學、機器學習加速設計新能源材料🫀，包括二維材料、電極材料、催化材料和固態電解質材料等。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. 等雜誌發表文章近五十余篇，目前H因子為24💁🏼，總引用超過2700次。
