r2SCAN+rVV10+U parameterization for 3d transition metal sulfides for thermochemistry

Transition metal sulfides play a critical role in energy storage, catalysis, and metallurgy. Here, we assess the predictive accuracy of density-functional theory with the r2SCAN functional for thermochemical properties of transition-metal sulfides. We find that a correction of the method's intrinsic self-interaction error can further improve the accuracy of phase diagrams compared with experimental observation. A systematic Hubbard-U parameterization across 22 transition metal sulfide systems resulted in a ∼70% reduction in the root mean square error of formation energies. r2SCAN+U predicts binary and ternary phase diagrams as well as Pourbaix diagrams in excellent agreement with experimental references and captures phase stability trends that are incorrectly described by uncorrected r2SCAN. These advancements establish a reliable and computationally efficient framework for modeling transition-metal sulfides with broad applicability to the design of energy storage materials, electrocatalysts, and sustainable metallurgical processes.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2025-07-16
DOI
https://doi.org/10.1063/5.0265659
Primary Topic
Machine Learning in Materials Science
Type
article
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article

r2SCAN+rVV10+U parameterization for 3d transition metal sulfides for thermochemistry

Alan C. West, Brian Donovan, Alexander Urban
The Journal of Chemical Physics
Machine Learning in Materials Science
article

r2SCAN+rVV10+U parameterization for 3d transition metal sulfides for thermochemistry

Alan C. West, Brian Donovan, Alexander Urban
article en

Abstract

Transition metal sulfides play a critical role in energy storage, catalysis, and metallurgy. Here, we assess the predictive accuracy of density-functional theory with the r2SCAN functional for thermochemical properties of transition-metal sulfides. We find that a correction of the method's intrinsic self-interaction error can further improve the accuracy of phase diagrams compared with experimental observation. A systematic Hubbard-U parameterization across 22 transition metal sulfide systems resulted in a ∼70% reduction in the root mean square error of formation energies. r2SCAN+U predicts binary and ternary phase diagrams as well as Pourbaix diagrams in excellent agreement with experimental references and captures phase stability trends that are incorrectly described by uncorrected r2SCAN. These advancements establish a reliable and computationally efficient framework for modeling transition-metal sulfides with broad applicability to the design of energy storage materials, electrocatalysts, and sustainable metallurgical processes.

The Journal of Chemical PhysicsVol. 163(3)
Columbia University (US)
Openalex Percentile: Top 88%
Machine Learning in Materials Science
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