Variational Monte Carlo on a Budget — Fine-tuning pre-trained Neural Wavefunctions

Michael Scherbela, Leon Gerard, Philipp Grohs

Keywords: Computational Physics, Machine Learning for Science, Quantum Monte Carlo, Fermionic Neural Networks

TL;DR: We obtain high-accuracy solutions to the Schrödinger equation cheaply, by fine-tuning a wavefunction pre-trained on many molecules.

Abstract:

Obtaining accurate solutions to the Schrödinger equation is the key challenge in computational quantum chemistry.
Deep-learning-based Variational Monte Carlo (DL-VMC) has recently outperformed conventional approaches in terms of accuracy, but only at large computational cost.
Whereas in many domains models are trained once and subsequently applied for inference, accurate DL-VMC so far requires a full optimization for every new problem instance, consuming thousands of GPUhs even for small molecules.
We instead propose a DL-VMC model which has been pre-trained using self-supervised wavefunction optimization on a large and chemically diverse set of molecules.
Applying this model to new molecules without any optimization, yields wavefunctions and absolute energies that outperform established methods such as CCSD(T)-2Z.
To obtain accurate relative energies, only few fine-tuning steps of this base model are required.
We accomplish this with a fully end-to-end machine-learned model, consisting of an improved geometry embedding architecture and an existing SE(3)-equivariant model to represent molecular orbitals.
Combining this architecture with continuous sampling of geometries, we improve zero-shot accuracy by two orders of magnitude compared to the state of the art.
We extensively evaluate the accuracy, scalability and limitations of our base model on a wide variety of test systems.

Submission Number: 11339