Path integral Liouville dynamics (PILD) has offered an approximate quantum dynamics approach to simultaneously satisfy the two critical fundamental criteria: conservation of the quantum Boltzmann distribution for the thermal equilibrium system and being exact for any thermal correlation functions (of even nonlinear operators, i.e., nonlinear functions of position or momentum operators) in the classical and harmonic limits. Our recent study demonstrates that it is possible to significantly accelerate the PILD computation by employing recently developed deep learning techniques to evaluate the thermal mass matrix and effective force. Using information entropy techniques can further improve the accuracy of the real time dynamics result.

Path integral Liouville dynamics with deep learning and information entropy

256th National Meeting (2018)

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