Putting error bars on density functional theory

Predicting the error in density functional theory (DFT) calculations due to the choice of exchange-correlation (XC) functional is crucial to the success of DFT, but currently, there are limited options to estimate this a priori. This is particularly important for high-throughput screening of new materials. In this work, the structure and elastic properties of binary and ternary oxides are computed using four XC functionals: LDA, PBE-GGA, PBEsol, and vdW-DF with C09 exchange. To analyze the systemic errors inherent to each XC functional, we employed materials informatics methods to predict the expected errors. The predicted errors were also used to better the DFT-predicted lattice parameters. Our results emphasize the link between the computed errors and the electron density and hybridization errors of a functional. In essence, these results provide "error bars" for choosing a functional for the creation of high-accuracy, high-throughput datasets as well as avenues for the development of XC functionals with enhanced performance, thereby enabling the accelerated discovery and design of new materials.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2024-08-30
DOI
https://doi.org/10.1038/s41598-024-69194-w
Citations
26
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
3.05

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Putting error bars on density functional theory

Noah Meyer, Valentino R. Cooper, Irmak Sargin, Simuck F. Yuk et al.
26 citations
Scientific Reports
Machine Learning in Materials Science
3.05
article

Putting error bars on density functional theory

Noah Meyer, Valentino R. Cooper, Irmak Sargin, Simuck F. Yuk, Jaron T. Krogel, Scott P. Beckman
article en
26 citations

Abstract

Predicting the error in density functional theory (DFT) calculations due to the choice of exchange-correlation (XC) functional is crucial to the success of DFT, but currently, there are limited options to estimate this a priori. This is particularly important for high-throughput screening of new materials. In this work, the structure and elastic properties of binary and ternary oxides are computed using four XC functionals: LDA, PBE-GGA, PBEsol, and vdW-DF with C09 exchange. To analyze the systemic errors inherent to each XC functional, we employed materials informatics methods to predict the expected errors. The predicted errors were also used to better the DFT-predicted lattice parameters. Our results emphasize the link between the computed errors and the electron density and hybridization errors of a functional. In essence, these results provide "error bars" for choosing a functional for the creation of high-accuracy, high-throughput datasets as well as avenues for the development of XC functionals with enhanced performance, thereby enabling the accelerated discovery and design of new materials.

Scientific ReportsVol. 14(1)
Oak Ridge National Laboratory (US), United States Military Academy (US), Middle East Technical University (TR), Washington State University (US), Stanford University (US)
U.S. Department of Energy, National Energy Research Scientific Computing Center, Office of Science, Oak Ridge Institute for Science and Education, Basic Energy Sciences, Oak Ridge National Laboratory
Industry, innovation and infrastructure
Openalex Percentile: Top 5%
Machine Learning in Materials Science
3.05
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.

Putting error bars on density functional theory — Noah Meyer, Valentino R. Cooper, et al. · Scientific Reports (2024) | TGRS Research Map | TGRS