Title: From Atoms to Mechanisms: Artificial Intelligence Augmented Chemistry for Molecular Simulations and Beyond
Abstract: The ability to rapidly learn from high-dimensional data to make reliable predictions about the future is crucial in many contexts. This could be a fly avoiding predators, or the retina processing terabytes of data guiding complex human actions. Modern day artificial intelligence (AI) aims to mimic this fidelity and has been successful in many domains of life. It is tempting to ask if AI could also be used to understand and predict the emergent mechanisms of complex molecules with millions of atoms. In this colloquium I will show that certain flavors of AI can indeed help us understand generic molecular and chemical dynamics and also predict it even in situations with arbitrary long memories. However this requires close integration of AI with old and new ideas in statistical mechanics. I will talk about such methods developed by my group using different flavors of generative AI such as information bottleneck, recurrent neural networks and denoising probabilistic models. I will demonstrate the methods on different problems, where we predict mechanisms at timescales much longer than milliseconds while keeping all-atom/femtosecond resolution. These include ligand dissociation from flexible protein/RNA and crystal nucleation with competing polymorphs. I will conclude with an outlook for future challenges and opportunities.