Seminar

Accelerating simulation and design of energy-related materials via machine learning

Speaker

Dr. Yunwei Zhang

Cavendish Laboratory

University of Cambridge

UK

Date & Time

Venue

Via Zoom

Abstract:

Computational materials science is undergoing a second revolution empowered by machine learning (ML). ML methods do not completely reply on the theoretical understanding of the problem but take a data-driven approach to solve these problems. ML methods can describe and predict the notorious properties of materials, especially for those that can only be determined experimentally.

In this talk, I will present our works in applying ML to identify the degradation patterns of Li-ion batteries (Nat. Comms 11 (1), 1-6 (2020)) and design new high-temperature superconductors. I will show that combining advanced experimental technique and physical theory with ML can help us to understand the physical laws between materials features and properties from a new perspective.

Research Areas:

Advanced Materials

Contact for

Information

Dr. Y. Chen

+(852) 3917 7095

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