About
Human society of this century is facing several fundamental challenges including global climate change, energy crisis, and public health crisis such as cancers and COVID-19. Common to their solutions are the discovery of novel materials, molecules, proteins, and drugs. Designing these functional atomic structures is challenging due to the astonishing complexity of the interatomic interactions, sophisticated physical/chemical/geometric constraints and patterns to form stable structures, and how the structures relate to their functions. Like most other engineering design activities, currently, the mainstream paradigm of material design is the rational design approach, which emphasizes a causal understanding of the structure-function relationship and depends on heuristic expert knowledge and explicit design rules. However, the traditional material design paradigm is facing increasing challenges in designing extraordinary functional materials that can effectively meet our needs: it usually leads to sub-optimal solutions in the huge chemical design space due to their limited search capability; it is difficult to handle huge amount of implicit knowledge and constraints, and cannot exploit such rules for efficient design space exploration; it needs too many explicit design rules; it is difficult to design highly constrained structures such as the periodic inorganic crystals.

In this talk, I will introduce the transformative shift from rational materials design to the data-driven deep generative material design paradigm, in which known materials data are fed to the deep generative models to learn explicit and implicit knowledge of atomic structures and then exploit them for efficient structure generation. This is inspired by the deep learning based Artificial Intelligence Generated Content (AIGC) technologies that have been accelerating in generating authentic images, videos, texts, music, and human voices. Our work shows that designing images and texts shares many characteristics with the task of designing proteins, molecules, and materials, in which building blocks of different levels are assembled together to form specific stable or meaningful structures that satisfy diverse grammatical, physical, chemical or geometric constraints. While Nature has used the physical apparatus of DNA as the information carrier of synthesis rules for protein synthesis and biochemistry through evolution, deep neural networks can also be exploited similarly to achieve Nature's way of material design by learning the designing rules from known materials or from computational simulations. Just as a female frog can give birth to a frog without knowing how a frog is grown from a zygote through a developmental process, we show that our deep generative materials design works in a similar design-without-understanding process.

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Bio: Dr. Jianjun Hu is professor of computer science at the University of South Carolina. He directs the Machine Learning and Evolution Laboratory (http://mleg.cse.sc.edu). Dr. Hu received his Ph.D. of computer science in 2004 from Michigan State University in the areas of machine learning and evolutionary computation and then conducted postdoc studies in bioinformatics at Purdue University and University of Southern California from 2004 to 2007. His current research interests include AI for science, machine learning, deep learning, evolutionary algorithms, and their application in material informatics, bioinformatics, health informatics, and automated design synthesis with a total of more than 200 papers. Dr. Hu is the winner of the National Science Foundation Career Award. His research has been funded by NSF, DOE, NIH. He can be reached at jianjunh@cse.sc.edu

Details: https://www.linkedin.com/events/7162579233444192256/about/