Refreshments are available starting at noon. The seminar will begin at 12:15 p.m.
Abstract
Artificial intelligence techniques for scientific discovery have gained increasing interest across the machine learning, physics, chemistry, materials, and biology communities. A central challenge in AI-driven scientific discovery is molecular learning and design, as molecules serve as fundamental building blocks and can be naturally represented through various modalities, including chemical formulas, molecular graphs, geometric conformations, knowledge graphs, and textual literature.
Shengchao Liu’s research focuses on leveraging multimodal information to develop a physics-inspired foundation model. To assess its effectiveness, he outlines and applies two key paradigms. The first involves employing physics-inspired AI models to accelerate established scientific discovery processes such as molecular dynamics simulations and molecule crystallization. The second paradigm integrates the reasoning and planning capabilities of generative AI models to explore novel approaches, including text-guided lead optimization, protein engineering, and material design. By bridging AI and physics in chemistry, biology, and materials science, Liu’s work offers a unique perspective on advancing AI-driven scientific discovery, ultimately supporting both science and scientists.
Speaker Biography
Shengchao Liu is a postdoctoral researcher at the University of California, Berkeley, working with Christian Borgs and Jennifer Chayes. Liu’s research focuses on representation learning, self-supervised pre-training, deep generative modeling, and physics-inspired machine learning with applications in scientific discovery. He has published in top venues such as the International Conference on Machine Learning (ICML), the International Conference on Learning Representations, the Conference and Workshop on Neural Information Processing Systems (NeurIPS), the International Conference on Artificial Intelligence and Statistics, Transactions on Machine Learning Research, the AAAI Conference on Artificial Intelligence, Nature Machine Intelligence, and the Journal of the American Chemical Society, and his work on protein engineering was a finalist for the ACM Gordon Bell Prize in 2024. Liu has co-organized AI for Science workshops at NeurIPS 2021, ICML 2022, and NeurIPS 2023 and led lecture tutorials on physics-inspired and scientific foundation models at AAAI 2025.