# Transferable Deep-Learning Quantum Monte Carlo

Molecular photochemistry needs entire ground- and excited-state potential energy surfaces, yet solving the electronic Schrödinger equation point by point is expensive, and cheap single-reference methods fail at conical intersections. Transferable deep quantum Monte Carlo optimizes one neural-network wavefunction jointly across many molecular geometries and electronic states, sharing parameters between states and ordering them dynamically by per-geometry energy. On ethylene, the carbon dimer, the methylenimmonium cation and a 96-electron rubredoxin active-site model, it reproduces tailored quantum-chemistry references while cutting cost by up to about two orders of magnitude relative to single-point calculations.

Molecular photochemistry needs entire ground- and excited-state potential energy surfaces, yet solving the electronic Schrödinger equation point by point is expensive, and cheap single-reference methods fail at conical intersections. Transferable deep quantum Monte Carlo optimizes one neural-network wavefunction jointly across many molecular geometries and electronic states, sharing parameters between states and ordering them dynamically by per-geometry energy. On ethylene, the carbon dimer, the methylenimmonium cation and a 96-electron rubredoxin active-site model, it reproduces tailored quantum-chemistry references while cutting cost by up to about two orders of magnitude relative to single-point calculations.