[2603.25381] Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo

Physics > Chemical Physics arXiv:2603.25381 (physics) [Submitted on 26 Mar 2026] Title:Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo Authors:P. Bernát Szabó, Zeno Schätzle, Frank Noé View a PDF of the paper titled Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo, by P. Bern\'at Szab\'o and 2 other authors View PDF Abstract:A faithful description of chemical processes requires exploring extended regions of the molecular potential energy surface (PES), which remains challenging for strongly correlated systems. Transferable deep-learning variational Monte Carlo (VMC) offers a promising route by efficiently solving the electronic Schrödinger equation jointly across molecular geometries at consistently high accuracy, yet its stochastic nature renders direct exploration of molecular configuration space nontrivial. Here, we present a framework for highly accurate ab initio exploration of PESs that combines transferable deep-learning VMC with a cost-effective estimation of energies, forces, and Hessians. By continuously sampling nuclear configurations during VMC optimization of electronic wave functions, we obtain transferable descriptions that achieve zero-shot chemical accuracy within chemically relevant distributions of molecular geometries. Throughout the subsequent characterization of molecular configuration space, the PES is evaluated only sp...