Untangling Sourcesof Error in the Density-FunctionalMany-Body Expansion

The many-body expansion provides a framework for data-driven applications of electronic structure theory, including parametrization of classical force fields and machine learning. However, we demonstrate that its use significantly amplifies quadrature grid errors when modern density-functional approximations are employed. Standard grids that work well in conventional density-functional calculations result in runaway error accumulation when used with the many-body expansion. At the same time, delocalization error is also exacerbated, leading to exaggerated estimates of nonadditive <i>n</i>-body interactions. This is illustrated for anion–water clusters using the SCAN, r<sup>2</sup>SCAN, ωB97X-V and ωB97M-V functionals. By employing dense quadrature grids, the inherent self-interaction error is exposed, which can then be mitigated using a variety of other strategies.

Publication Details

Journal
Figshare
Published
2025-03-08
DOI
https://doi.org/10.1021/acs.jpclett.4c03619.s001
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Untangling Sourcesof Error in the Density-FunctionalMany-Body Expansion

Figshare
Machine Learning in Materials Science
article

Untangling Sourcesof Error in the Density-FunctionalMany-Body Expansion

article en

Abstract

The many-body expansion provides a framework for data-driven applications of electronic structure theory, including parametrization of classical force fields and machine learning. However, we demonstrate that its use significantly amplifies quadrature grid errors when modern density-functional approximations are employed. Standard grids that work well in conventional density-functional calculations result in runaway error accumulation when used with the many-body expansion. At the same time, delocalization error is also exacerbated, leading to exaggerated estimates of nonadditive <i>n</i>-body interactions. This is illustrated for anion–water clusters using the SCAN, r<sup>2</sup>SCAN, ωB97X-V and ωB97M-V functionals. By employing dense quadrature grids, the inherent self-interaction error is exposed, which can then be mitigated using a variety of other strategies.

Figshare
Openalex Percentile: Top 64%
Machine Learning in Materials Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.