2019/12/25 by Dmitri Iouchtchenko, Neil Raymond, Iouchtchenko, Dmitri +5 · 1 citation
Chemistry · Physics and Astronomy · #Advanced Chemical Physics Studies #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Mass Spectrometry Techniques and Applications #Quantum, superfluid, helium dynamics
paper · pdf · doi:10.48550/arxiv.1912.11594
openalex publication_date 2019/12/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
It was recently shown that path integral Monte Carlo can be used to directly compute partition functions of Hamiltonians with vibronic coupling [J. Chem. Phys. 148, 194110 (2018)]. While the importance sampling Monte Carlo integration scheme was successful, it required many samples to reduce the stochastic error and suffered from the need to manually construct a sampling distribution for each system. We tackle these issues by using deterministic component selection for Gaussian mixture distributions (GMDs), introducing quasi-random numbers into the Monte Carlo sampling, and automatically optimizing the GMD parameters to obtain an improved sampling distribution. We demonstrate the effectiveness of our methods using vibronic model systems, but these methods are in principle widely applicable to general Monte Carlo sampling of GMDs.