# Adaptive algorithm for electronic structure calculations using reduction of Gaussian mixtures

## TL;DR

This paper introduces an adaptive algorithm for electronic structure calculations that reduces Gaussian mixtures efficiently, avoiding basis errors and requiring fewer terms than traditional multiresolution methods.

## Contribution

The novel adaptive method selects the optimal Gaussian mixture terms during iteration, eliminating the need for multiresolution bases and improving efficiency.

## Key findings

- Successfully applied to Hartree-Fock equations for diatomic molecules
- Achieves comparable accuracy with fewer Gaussian terms
- Demonstrates effective adaptivity without multiresolution bases

## Abstract

We present a new adaptive method for electronic structure calculations based on novel fast algorithms for reduction of multivariate mixtures. In our calculations, spatial orbitals are maintained as Gaussian mixtures whose terms are selected in the process of solving equations. Using a fixed basis leads to the so-called "basis error" since orbitals may not lie entirely within the linear span of the basis. To avoid such an error, multiresolution bases are used in adaptive algorithms so that basis functions are selected from a fixed collection of functions, large enough as to approximate solutions within any user-selected accuracy. Our new method achieves adaptivity without using a multiresolution basis. Instead, as a part of an iteration to solve nonlinear equations, our algorithm selects the "best" subset of linearly independent terms of a Gaussian mixture from a collection that is much larger than any possible basis since the locations and shapes of the Gaussian terms are not fixed in advance. Approximating an orbital within a given accuracy, our algorithm yields significantly fewer terms than methods using multiresolution bases. We demonstrate our approach by solving the Hartree-Fock equations for two diatomic molecules, HeH+ and LiH, matching the accuracy previously obtained using multiwavelet bases.