Sampling-free Inference for Ab-Initio Potential Energy Surface Networks
This work addresses the computational bottleneck in ab-initio quantum chemistry simulations for researchers, enabling high-resolution energy surface modeling for larger systems that were previously infeasible.
The paper tackles the slow inference problem in neural wave function models for molecular energy surfaces by proposing the PlaNet framework, which uses a surrogate model to accelerate energy evaluation from hours to milliseconds while preserving accuracy, achieving a 7-order magnitude speedup for larger molecules like ethanol and reducing energy errors by up to 74% with PESNet++.
Recently, it has been shown that neural networks not only approximate the ground-state wave functions of a single molecular system well but can also generalize to multiple geometries. While such generalization significantly speeds up training, each energy evaluation still requires Monte Carlo integration which limits the evaluation to a few geometries. In this work, we address the inference shortcomings by proposing the Potential learning from ab-initio Networks (PlaNet) framework, in which we simultaneously train a surrogate model in addition to the neural wave function. At inference time, the surrogate avoids expensive Monte-Carlo integration by directly estimating the energy, accelerating the process from hours to milliseconds. In this way, we can accurately model high-resolution multi-dimensional energy surfaces for larger systems that previously were unobtainable via neural wave functions. Finally, we explore an additional inductive bias by introducing physically-motivated restricted neural wave function models. We implement such a function with several additional improvements in the new PESNet++ model. In our experimental evaluation, PlaNet accelerates inference by 7 orders of magnitude for larger molecules like ethanol while preserving accuracy. Compared to previous energy surface networks, PESNet++ reduces energy errors by up to 74%.