报告题目：Accelerating materials research through machine learning
报告时间：2021年4月28日 上午 9：30-10：30
报告地点：线上：腾讯会议，会议号822 517 638，线下：南岭校区机械材料馆209报告厅
Machine learning has the potential to transform computational materials research by accelerating the calculation of material properties by orders of magnitude. I will present three examples of how this can be done at the atomic scale. In the first, I will demonstrate how machine learning, when combined with the cluster expansion approach, can be used to create highly accurate models of complex substitutional alloys. I will present several applications of this approach to problems in catalysis, including the prediction of the structures and properties of ternary alloy nanoparticles and the construction of novel catalytic activity maps of alloy phase diagrams. Such catalytic activity maps can be used to rapidly identify the synthesis conditions that are likely to produce highly stable and active catalysts, an important step towards rational catalysis design. In the second example, I will illustrate how an emerging class of machine-learned interatomic potentials known as "moment tensor potentials", trained on the fly, enables identification of candidate coating materials for solid state lithium ion batteries by accelerating molecular dynamics calculations by about seven orders of magnitude. This approach results in calculated activation energies for lithium-ion diffusion that are in much better agreement with experimental values than those calculated using only density functional theory. Using this method as part of a computational screen, we have identified novel candidate coating materials for use in all-solid-state lithium-ion batteries. In the final example, I will demonstrate how moment-tensor potentials, trained on the fly, can be used with genetic algorithms to dramatically accelerate the search for low-energy structures of small atomic clusters. This approach has enabled us to discover more than two dozen new low-energy structures for small aluminum clusters.
Tim Mueller is an Assistant Professor in the Department of Materials Science and Engineering at Johns Hopkins University. His research is on the design and discovery of new materials, with a focus on materials for energy storage and conversion. His group uses ab initio methods, informatics, and machine learning to predict the structure and properties of new materials before they are synthesized. Active areas of research include the development of machine learning algorithms to accelerate the atomic-scale calculation of materials properties, developing and applying advanced methods to predict the structures and properties of alloy catalysts, and the search for new materials for use in solid state batteries. Prior to joining Johns Hopkins, he co-founded Pellion Technologies, a battery company. He has an A.B. from Harvard University and a Ph.D. from MIT.