Dept. of Chemistry & Biochemistry, University of Maryland
Wednesday, April 19, 2023, 3:00-4:00 PM ET (1:00-2:00 PM MT)
A video of this talk will be made available to NIST staff in the Math channel on NISTube, which is accessible from the NIST internal home page. It will be taken down from NISTube after 12 months at which point it can be requested by emailing the ACMD Seminar Chair.
Abstract: The universality of thermodynamics and statistical mechanics has led to a language comprehensible to chemists, physicists & others, enabling countless scientific discoveries in diverse fields. In the last decade, a new arguably common language that everyone seems to speak but at least no chemist fully 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 in chemistry for new discoveries. At the same this raises many open questions, including: (1) should chemists, who are not fundamentally trained in AI, trust any of the results obtained using AI, (2) can AI paradigms developed for non-molecular systems with massive training data can directly be applied to chemistry with all its quirks, richness, known/unknown laws, and often poor/limited data? In this seminar I will show how such an integration of disciplines can be attained, creating trustable, robust AI frameworks for use by chemists. I will demonstrate such methods on different problems involving protein kinases, riboswitches and crystal polymorph nucleation, 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 moves hand-in-hand with AI to enable smart molecular discovery, and is not just yet another domain for application of AI.
Bio: Pratyush Tiwary is an Associate Professor at the University of Maryland, College Park. There he leads a lab specializing in the intersection of molecular simulations, statistical mechanics, and machine learning for solving practical problems of human health and energy relevance. He has been recognized through awards such as the Sloan Research Fellowship in Chemistry, NSF CAREER award, NIH Maximizing Investigators’ Research Award and the student nominated Excellence in Teaching award from University of Maryland. He is a member of the Editorial Board of the journal Proteins and the Scientific Advisory Board of Schrodinger, and also serves as an Associate Editor for the Journal of Chemical Theory and Computation. Before starting at Maryland in 2017, Tiwary received his degrees from IIT-BHU and Caltech, and completed postdoctoral work at ETH Zurich and Columbia University. For more details visit his website https://go.umd.edu/tiwarylab
Host: Yi-Kai Liu
Note: This talk will be recorded to provide access to NIST staff and associates who could not be present to the time of the seminar. The recording will be made available in the Math channel on NISTube, which is accessible only on the NIST internal network. This recording could be released to the public through a Freedom of Information Act (FOIA) request. Do not discuss or visually present any sensitive (CUI/PII/BII) material. Ensure that no inappropriate material or any minors are contained within the background of any recording. (To facilitate this, we request that cameras of attendees are muted except when asking questions.)
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