In-Person Meeting Location:

Storey Innovation Center 1400

Live Meeting Link for Virtual Audience

Talk Abstract: Artificial intelligence and deep learning are increasingly transforming all scientific disciplines with their superior capability to learn to detect patterns from large amount of data and to learn predictive models from data without relying upon theory or deep mechanistic understanding, with their capability to build generative models for inverse design of materials and molecules and with the models to generate synthetic data. In this talk, we present our research focusing on using deep learning and machine learning to discover and model the patterns in and relationships of structures and functions in materials and molecules and how to exploit such learned dark/implicit knowledge in deep learning based generative design of novel materials, graph neural network based materials property prediction, and deep learning based crystal structure prediction of inorganic materials. Considering that the number of inorganic materials discovered so far (~250,000) by humanity is only a tiny portion of the almost infinite chemical design space, our AI based data-driven computational materials discovery has the potential to transform the conventional trial-and-error approaches in materials discovery.

Speaker's Bio: Dr. Jianjun Hu is currently a Full Professor of computer science at the Department of Computer Science and Engineering, University of South Carolina, Columbia SC. He was associate professor from 2013 to 2022 and assistant professor from 2007 to 2013 at the same department. Dr. Hu received his B.S. and M.S. degrees of Mechanical Engineering in 1995 and 1998 respectively from Wuhan University of Technology, China. He received the Ph.D. degree of Computer Science in 2004 from Michigan State University in the area of machine learning and evolutionary computation, under the supervision of Professor Erik Goodman. He then worked as Postdoctoral Fellow at Purdue University with Prof. Daisuke Kihara and University of Southern California with Prof. Xianghong Zhou from 2004 to 2007 in the area of bioinformatics. Dr. Hu’s main research has focused on machine learning, deep learning, evolutionary computation and their applications in materials informatics, bioinformatics, engineering design, and intelligent manufacturing. His works have been published in PNAS, Advanced Science, Nature npj Computational Materials, Patterns (Cell Press), Evolutionary Computation Journal, Journal of physical chemistry, Scientific Report, and so on with a total of more than 200 journal and conference papers (H-index 35 with > 4200 citations). Currently, his main research is focused on utilizing deep learning to discover the relationship of structures and functions in materials, molecules, and proteins and exploit the learned implicit knowledge for generative design of transformative new materials, drugs, and proteins. His work and research lab info can be found online at: http://mleg.cse.sc.edu/publication