Accuracy of density functional theory in predicting formation energies of ternary oxides from binary oxides and its implication on phase stability
Open accessAbstract
The evaluation of reaction energies between solids using density functional theory (DFT) is of practical importance in many technological fields and paramount in the study of the phase stability of known and predicted compounds. In this work, we present a comparison between reaction energies provided by experiments and computed by DFT in the generalized gradient approximation (GGA), using a Hubbard $U$ parameter for some transition metal elements (GGA+$U$). We use a data set of 135 reactions involving the formation of ternary oxides from binary oxides in a broad range of chemistries and crystal structures. We find that the computational errors can be modeled by a normal distribution with a mean close to zero and a standard deviation of 24 meV/atom. The significantly smaller error compared to the more commonly reported errors in the formation energies from the elements is related to the larger cancellation of errors in energies when reactions involve chemically similar compounds. This result is of importance for phase diagram computations for which the relevant reaction energies are often not from the elements but from chemically close phases (e.g., ternary oxides versus binary oxides). In addition, we discuss the distribution of computational errors among chemistries and show that the use of a Hubbard $U$ parameter is critical to the accuracy of reaction energies involving transition metals even when no major change in formal oxidation state is occurring.
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Standard GGA-level DFT functionals show, for 27 formation reactions involving silica, an RMSE of about 28 meV atom−1 with respect to experiment.
“To put these NNP errors in context, note that standard GGA-level DFT functionals show, for 27 formation reactions that involve silica, an RMSE of about 28 meV atom−1 with respect to experiment57.”
From A reactive neural network framework for water-loaded acidic zeolites (2024) · cites this paper as [57]
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Blöchl, P. E. (1994) · Physical Review B · doi:10.1103/physrevb.50.17953 · Cited by 92,732 · see citing works
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A critical examination of compound stability predictions from machine-learned formation energies
Bartel, C. J., Trewartha, A., Wang, Q., Dunn, A., Jain, A., & Ceder, G. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00362-y · Cited by 277 · see citing works
Self-citationA reactive neural network framework for water-loaded acidic zeolites
Erlebach, A., Šípka, M., Saha, I., Nachtigall, P., Heard, C. J., & Grajciar, L. (2024) · Nature Communications · doi:10.1038/s41467-024-48609-2 · Cited by 47 · see citing works
“To put these NNP errors in context, note that standard GGA-level DFT functionals show, for 27 formation reactions that involve silica, an RMSE of about 28 meV atom − 1 with respect to experiment 57 .”
Method
- Authors
- Hautier, G., Ong, S. P., Jain, A., Moore, C. J., & Ceder, G.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2012-04-30
- Volume
- 85
- Issue
- 15
- ISSN
- 1098-0121 (ISSN-L)
- 1550-235X
- OpenAlex
- W2035158988
- Cited by
- 506
- References
- 1
- Citations by year
- 2026: 34 · 2025: 38 · 2024: 51 · 2023: 43 · 2022: 46 · 2021: 44 · 2020: 57 · 2019: 28 · 2018: 40 · 2017: 35 · 2016: 30 · 2015: 24 · 2014: 15 · 2013: 16 · 2012: 5
- FWCI
- 9.93
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Physical Review B (publishedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- APS
- Funders
- National Science Foundation
- U.S. Department of Energy
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Earth and Planetary Sciences › Geophysics › High-pressure geophysics and materials
- Keywords
- density functional accuracy, reaction energy, formation energy, ternary oxides, binary oxides, phase stability, phase diagram, GGA+U, Hubbard U parameter, transition metal oxides, error cancellation
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