题目：Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
报告人：Arthur Mar教授
单位：加拿大阿尔伯塔大学
时间：2019-5-17 10:00AM
地点：纳米楼一楼报告厅
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个人简介：
Dr. Arthur Mar received a Ph.D. from Northwestern University in 1992 under the supervision of James I. Ibers. He worked as an NSERC Postdoctoral Fellow in the laboratory of Yves Piffard and Jean Rouxel at the Institut des Matériaux de Nantes in 1993–1994. He is currently a full Professor in the Department of Chemistry at the University of Alberta. He is considered to be one of the leading experts in the field of inorganic solid state chemistry, having established a internationally recognized research program encompassing the synthesis, characterization, and applications of intermetallic compounds and Zintl phases, with an aim to understand structure-property relationships. In recent years, he has been at the forefront of applying machine-learning approaches to materials discovery. He has published over 208 articles and given over 92 invited presentations. He has served on the editorial boards of Chemistry of Materials, Journal of Solid State Chemistry, and Acta Crystallographica. He has received the Faculty of Science Research Award and many teaching awards at the University of Alberta.
报告摘要：
Traditional approaches to search for new solid state materials can involve systematic investigations (e.g., phase diagrams), serendipitous discoveries, or, for limited classes of compounds, rational strategies for manipulating building blocks. Answering the call of the Materials Genome Initiative,1 launched in 2011, to “discover, develop, and deploy new materials twice as fast,” we are applying high-throughput machine-learning methods to predict the structures of new compounds and optimize properties of materials. An ambitious goal is to classify structures of intermetallics, including unknown ones, solely on the basis of their compositions; these encompass binary AB compounds, ternary ABC compounds, Heusler and half-Heusler phases. In collaboration with Citrine Informatics,2 machine-learning approaches have also been used to search for unconventional candidates for thermoelectric materials.