Data-driven Approach to Parameterize SCAN+U for an Accurate Description of 3d Transition Metal Oxide Thermochemistry

Abstract

Semi-local DFT methods exhibit significant errors for the phase diagrams of transition-metal oxides that are caused by an incorrect description of molecular oxygen and the large self-interaction error in materials with strongly localized electronic orbitals. Empirical and semiempirical corrections based on the DFT+U method can reduce these errors, but the parameterization and validation of the correction terms remains an on-going challenge. We develop a systematic methodology to determine the parameters and to statistically assess the results by considering thermochemical data across a set of transition metal compounds. We consider three interconnected levels of correction terms: (1) a constant oxygen binding correction, (2) Hubbard-U correction, and (3) DFT/DFT+U compatibility correction. The parameterization is expressed as a unified optimization problem. We demonstrate this approach for 3d transition metal oxides, considering a target set of binary and ternary oxides. With a total of 37 measured formation enthalpies taken from the literature, the dataset is augmented by the reaction energies of 1,710 unique reactions that were derived from the formation energies by systematic enumeration. To ensure a balanced dataset across the available data, the reactions were grouped by their similarity using clustering and suitably weighted. The parameterization is validated using leave-one-out cross-validation (CV), a standard technique for the validation of statistical models. We apply the methodology to the SCAN density functional. Based on the CV score, the error of binary (ternary) oxide formation energies is reduced by 40% (75%) to 0.10 (0.03) eV/atom. The method and tools demonstrated here can be applied to other classes of materials or to parameterize the corrections to optimize DFT+U performance for other target physical properties.

49 pages, 8 figures, 1 table, 6 SI figure, 5 SI tables

References in corpus (4)

• Extensive Benchmarking of DFT+U Calculations for Predicting Band Gaps
• Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning
• Computing total energies in complex materials using charge self-consistent DFT+DMFT
• Lattice and Electronic properties of VO${}_2$ with the SCAN(+$U$) approach

Cited by in corpus (9)

• Accurate electronic properties and intercalation voltages of olivine-type Li-ion cathode materials from extended Hubbard functionals
• Performance of the r${}^2$SCAN functional in transition metal oxides
• Phase transitions of LaMnO${}_3$ and SrRuO${}_3$ from DFT + U based machine learning force fields simulations
• Modeling intercalation chemistry with multi-redox reactions by sparse lattice models in disordered rocksalt cathodes
• First-principles Hubbard parameters with automated and reproducible workflows
• Accuracy of metaGGA functionals in describing transition metal fluorides
• Teaching oxidation states to neural networks
• Reducing Self-Interaction Error in Transition-Metal Oxides with Different Exact-Exchange Fractions for Energy and Density
• Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation