Nonlinear reduced basis using mixture Wasserstein barycenters: application to an eigenvalue problem inspired from quantum chemistry

TL;DR

This paper introduces a nonlinear reduced basis method using mixture Wasserstein barycenters for parametric eigenvalue problems inspired by quantum chemistry, demonstrating improved approximation efficiency over traditional methods.

Contribution

It develops a novel nonlinear reduced basis approach based on Wasserstein barycenters for parametric eigenvalue problems in quantum chemistry applications.

Findings

Wasserstein distances lead to faster decay of solution set complexity.

The proposed method achieves good approximation accuracy.

Numerical results validate the effectiveness of the approach.

Abstract

The aim of this article is to propose a new reduced-order modelling approach for parametric eigenvalue problems arising in electronic structure calculations. Namely, we develop nonlinear reduced basis techniques for the approximation of parametric eigenvalue problems inspired from quantum chemistry applications. More precisely, we consider here a one-dimensional model which is a toy model for the computation of the electronic ground state wavefunction of a system of electrons within a molecule, solution to the many-body electronic Schr\"odinger equation, where the varying parameters are the positions of the nuclei in the molecule. We estimate the decay rate of the Kolmogorov n-width of the set of solutions for this parametric problem in several settings, including the standard L2-norm as well as with distances based on optimal transport. The fact that the latter decays much faster than in the traditional L2-norm setting motivates us to propose a practical nonlinear reduced basis method, which is based on an offline greedy algorithm, and an efficient stochastic energy minimization in the online phase. We finally provide numerical results illustrating the capabilities of the method and good approximation properties, both in the offline and the online phase.