Deep generative modelling of canonical ensemble with differentiable thermal properties

arXiv:2404.18404v1 Announce Type: cross
Abstract: We propose a variational modelling method with differentiable temperature for canonical ensembles. Using a deep generative model, the free energy is estimated and minimized simultaneously in a continuous temperature range. At optimal, this generative model is a Boltzmann distribution with temperature dependence. The training process requires no dataset, and works with arbitrary explicit density generative models. We applied our method to study the phase transitions (PT) in the Ising and XY models, and showed that …

abstract arxiv boltzmann canonical cond-mat.stat-mech continuous cs.lg differentiable distribution energy ensemble free generative modelling process training type