DFT‐D3 for Solid‐State Materials: A Data‐Driven Perspective on Accuracy

Abstract Dispersion interactions are essential for accurate modeling of materials, yet standard Density Functional Theory (DFT) lacks explicit treatment. Grimme's DFT‐D3 method adds empirical dispersion corrections and performs well for molecular systems, but its reliability in solids remains uncertain. Here, DFT‐D3, including zero‐damping and Becke–Johnson variants, is systematically assessed across more than 2,000 lithium‐, potassium‐, magnesium‐, zinc‐, copper‐, and manganese‐based solid‐state binary compounds using a homegrown automated workflow integrating VASP , pymatgen , and a custom D3 interface. It is found that D3 often introduces artificial secondary minima in potential energy surfaces, particularly in metallic and densely packed solids, leading to unphysical stabilization and significant energy errors. The successor scheme D3S is also examined, and although it reduces dispersion errors relative to D3, it still produces artificial stabilization in ionic and metallic systems, indicating that its molecular‐level improvements do not fully extend to solids. Comparisons with many‐body dispersion (MBD@rsSCS) calculations show that these artifacts arise from abrupt coordination‐number‐dependent variations in the coefficients. Since the errors lack consistent correlation with structural or chemical descriptors, they remain difficult to predict. Overall, DFT‐D3 is reliable for covalent frameworks and low‐metal‐content intercalation systems but requires caution in dense, metal‐rich materials to avoid misleading overbinding.

Authors

Institutions

Publication Details

Journal
Advanced Theory and Simulations
Published
2025-10-13
DOI
https://doi.org/10.1002/adts.202501072
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DFT‐D3 for Solid‐State Materials: A Data‐Driven Perspective on Accuracy

Vikas Singh Thakur, Swastika Banerjee, Partha Paul
1 citations
Advanced Theory and Simulations
Machine Learning in Materials Science
0.52
article

DFT‐D3 for Solid‐State Materials: A Data‐Driven Perspective on Accuracy

Vikas Singh Thakur, Swastika Banerjee, Partha Paul
article en
1 citations

Abstract

Abstract Dispersion interactions are essential for accurate modeling of materials, yet standard Density Functional Theory (DFT) lacks explicit treatment. Grimme's DFT‐D3 method adds empirical dispersion corrections and performs well for molecular systems, but its reliability in solids remains uncertain. Here, DFT‐D3, including zero‐damping and Becke–Johnson variants, is systematically assessed across more than 2,000 lithium‐, potassium‐, magnesium‐, zinc‐, copper‐, and manganese‐based solid‐state binary compounds using a homegrown automated workflow integrating VASP , pymatgen , and a custom D3 interface. It is found that D3 often introduces artificial secondary minima in potential energy surfaces, particularly in metallic and densely packed solids, leading to unphysical stabilization and significant energy errors. The successor scheme D3S is also examined, and although it reduces dispersion errors relative to D3, it still produces artificial stabilization in ionic and metallic systems, indicating that its molecular‐level improvements do not fully extend to solids. Comparisons with many‐body dispersion (MBD@rsSCS) calculations show that these artifacts arise from abrupt coordination‐number‐dependent variations in the coefficients. Since the errors lack consistent correlation with structural or chemical descriptors, they remain difficult to predict. Overall, DFT‐D3 is reliable for covalent frameworks and low‐metal‐content intercalation systems but requires caution in dense, metal‐rich materials to avoid misleading overbinding.

Advanced Theory and SimulationsVol. 9(1)
Indian Institute of Technology Roorkee (IN)
Openalex Percentile: Top 32%
Machine Learning in Materials Science
0.52
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.