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

ArXi:2603.25381v1 Announce Type: cross 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 Schr\"odinger equation jointly across molecular geometries at consistently high accuracy, yet its stochastic nature renders direct exploration of molecular configuration space nontrivial.