University of California Berkeley, Berkeley, California, United States
Promising materials in a lot of situations come with complicate composition and bond topology. The structural complexity makes such materials difficult to understand and manipulate. To rationale the design of materials with complex structure, data driven computational framework that combines first principal calculations, high throughput computation, data mining and machine learning has been established to answer two fundamental questions 1) How does the building block of complicate crystal structure --- atomic scale local structures influence materials properties? 2) How to design accessible chemical reaction pathway to synthesize promising materials in experiments? The capability of the current theoretical framework will be demonstrated by several case studies on materials for energy storage and conversion. Additionally, future opportunities and challenges on accelerating the discovery of new materials with structural complexity will be demonstrated.
Brief CV of Dr. Bin Ouyang:
Dr. Bin Ouyang is a theorist working on predictive synthesis of inorganic materials, mass/phonon/electron transport, and materials discovery through data mining and machine learning. He obtained PhD in Materials Engineering at McGill University in 2017. Currently, he is a postdoc scholar at University of California Berkeley working on developing theoretical and computational tools to understand energy storage/conversion materials. Up until now, he has authored 45 papers in diverse fields of material science including batteries, catalysis, metal alloys and nanomaterials. He is interested in developing theoretical and computational tools for accelerating the discovery and understanding of materials with complex atomic structure and chemical composition.
Host: Prof. Miao Liu, State Key Lab for Surface Physics
Contact: Liu Yang（杨柳）
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