The Source of SomeEmpirical Density Functionals vander Waals Forces

Density functional approximations became indispensable tools in many fields of chemistry due to their excellent cost-to-accuracy ratio. Still, consideration is required to select an appropriate approximation for each task. Highly parameterized Minnesota functionals are known for their excellent accuracy in reproducing thermochemical properties and, in particular, weak medium-range interactions. Here, we show that the latter ability of many Minnesota functionals comes from exploiting the basis set incompleteness. This finding shows how empirical functionals can trick their makers by learning to operate in a physics-defying way and likely explains the previously observed tendency of Minnesota functionals to distort electron densities. Thus, satisfaction of the Hellmann–Feynman theorem should be considered an important test and parameterization goal for the future generations of highly parameterized density functionals, including those based on neural networks.

Publication Details

Journal
Figshare
Published
2025-03-07
DOI
https://doi.org/10.1021/acs.jpca.4c07586.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

The Source of SomeEmpirical Density Functionals vander Waals Forces

Figshare
Machine Learning in Materials Science
article

The Source of SomeEmpirical Density Functionals vander Waals Forces

article en

Abstract

Density functional approximations became indispensable tools in many fields of chemistry due to their excellent cost-to-accuracy ratio. Still, consideration is required to select an appropriate approximation for each task. Highly parameterized Minnesota functionals are known for their excellent accuracy in reproducing thermochemical properties and, in particular, weak medium-range interactions. Here, we show that the latter ability of many Minnesota functionals comes from exploiting the basis set incompleteness. This finding shows how empirical functionals can trick their makers by learning to operate in a physics-defying way and likely explains the previously observed tendency of Minnesota functionals to distort electron densities. Thus, satisfaction of the Hellmann–Feynman theorem should be considered an important test and parameterization goal for the future generations of highly parameterized density functionals, including those based on neural networks.

Figshare
Openalex Percentile: Top 66%
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.