Ab initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo

The accurate quantum chemical calculation of excited states is a challenging task, often requiring computationally demanding methods. When entire ground and excited potential energy surfaces (PESs) are desired, for instance, to predict the interaction of light excitation and structural changes, one is often forced to use cheaper computational methods at the cost of reduced accuracy. Here we introduce a method for the geometrically transferable optimization of neural network wavefunctions that leverages weight sharing and a dynamical ordering of electronic states. Our method enables the efficient prediction of ground- and excited-state PESs and their intersections at the highest accuracy, demonstrating up to two orders of magnitude cost reduction compared with single-point calculations. We validate our approach on four challenging excited-state PESs, namely, ethylene, the carbon dimer, the methylenimmonium cation and a rubredoxin active site model containing 96 electrons, illustrating the potential of transferable deep-learning quantum Monte Carlo as a practical framework for studying electronic excitations in molecules.