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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.

Speaker: Pratyush Tiwary

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