A Match Made in Heaven? Integrated Theory, Simulations and AI for Enabling Molecular Discovery
Speaker: Dr. Pratyush Tiwary, UMCP
Abstract: The universality of thermodynamics and statistical mechanics has led to a language comprehensible to chemists, physicists, materials scientists, geologists & others, enabling countless scientific discoveries in diverse fields. In the last decade, a new arguably common language that everyone seems to speak but no one quite understands, has emerged with the advent of artificial intelligence (AI). It is natural to ask if AI can be integrated with the various theoretical and simulation methods rooted in thermodynamics and statistical mechanics for discoveries that none of these could achieve individually. It is also natural to ask if chemists, who are not fundamentally trained in AI, should trust any of the results obtained using AI or even worse, theory or computer simulations that were guided by AI. In this colloquium I will show how such an integration of disciplines can be attained, creating trustable, robust AI frameworks for use by chemists and physical scientists. I will talk about such methods developed by my group using and extending different flavors of AI [1-3]. I will demonstrate the methods on different problems from proteins, RNA, crystal nucleation [4-5], where we predict mechanisms at timescales much longer than milliseconds while keeping all-atom/femtosecond resolution. I will conclude with an outlook for future challenges and opportunities, envisioning a new sub-discipline of “Artificial Chemical Intelligence” where chemistry (both theory and simulations) move hand-in-hand with AI to enable smart molecular discovery.
[1] Wang, Ribeiro, Tiwary. Nature Comm 10, 3573 (2019).
[2] Tsai, Kuo, Tiwary. Nature Comm 11, 5115 (2020).
[3] Wang, Herron, Tiwary. Proc Natl Acad Sci 119, e2203656119 (2022).
[4] Wang, Parmar, Schneekloth, Tiwary. ACS Central Science 8, 741 (2022).
[5] Shekhar, Smith, Seeliger, Tiwary. Angewandte Chemie 61, e202200983 (2022).
Special Seminar