Interpolating many-body wave functions for accelerated molecular dynamics on the near-exact electronic surface

TL;DR

This paper introduces a novel interpolation scheme for many-electron wave functions that enables accurate, scalable molecular dynamics simulations on near-exact electronic surfaces, bridging electronic structure and machine learning methods.

Contribution

The authors develop a practical interpolation approach for correlated many-electron states that achieves near-exact potential energy surfaces with mean-field computational scaling, using minimal training data.

Findings

Achieves convergence to near-exact potential energy surfaces

Enables high-throughput molecular dynamics with validated wave functions

Outperforms traditional machine-learning potentials in accuracy

Abstract

While there have been many developments in computational probes of both strongly-correlated molecular systems and machine-learning accelerated molecular dynamics, there remains a significant gap in capabilities in simulating accurate non-local electronic structure over timescales on which atoms move. We develop an approach to bridge these fields with a practical interpolation scheme for the correlated many-electron state through the space of atomic configurations, whilst avoiding the exponential complexity of these underlying electronic states. With a small number of accurate correlated wave functions as a training set, we demonstrate provable convergence to near-exact potential energy surfaces for subsequent dynamics with propagation of a valid many-body wave function and inference of its variational energy whilst retaining a mean-field computational scaling. This represents a profoundly different paradigm to the direct interpolation of potential energy surfaces in established machine-learning approaches. We combine this with modern electronic structure approaches to systematically resolve molecular dynamics trajectories and converge thermodynamic quantities with a high-throughput of several million interpolated wave functions with explicit validation of their accuracy from only a few numerically exact quantum chemical calculations. We also highlight the comparison to traditional machine-learned potentials or dynamics on mean-field surfaces.