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Bosonic Quantum Solvation Enabled by Machine Learning

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My talk will focus on our recent advances that allow us to perform highly accurate and converged path integral simulations of flexible molecules including their reactions in bosonic solvents at 1 Kelvin or less. Our approach is based on using machine learning potentials to describe the many-body interactions at the level of coupled cluster electronic structure theory. Selected applications will be used to explore to what extent Bose-Einstein statistics of the liquid environment such as manifestations of local superfluidity or supersolidity are critical to understand phenomena as probed by the embedded molecular species.

This talk is part of the Theory of Condensed Matter series.

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