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

Area of Science:

• Materials Science
• Computational Chemistry
• Solid State Physics

Background:

• Transition metal sulfides are vital for energy storage, catalysis, and metallurgy.
• Accurate computational modeling is crucial for designing new materials with desired properties.

Purpose of the Study:

• To evaluate the predictive accuracy of density-functional theory (DFT) using the r2SCAN functional for thermochemical properties of transition metal sulfides.
• To improve the accuracy of DFT predictions by addressing the self-interaction error inherent in the r2SCAN functional.

Main Methods:

• Applied DFT with the r2SCAN functional to model thermochemical properties of transition metal sulfides.
• Systematically parameterized the Hubbard U correction (r2SCAN+U) across 22 transition metal sulfide systems.
• Compared predicted phase diagrams and formation energies with experimental data.

Main Results:

• The r2SCAN+U method significantly improved the prediction of formation energies, reducing root mean square error by approximately 70%.
• r2SCAN+U accurately predicted binary and ternary phase diagrams and Pourbaix diagrams, showing excellent agreement with experimental references.
• The corrected method captured phase stability trends missed by the uncorrected r2SCAN functional.

Conclusions:

• DFT with r2SCAN, particularly when corrected for self-interaction error using the Hubbard U parameterization, provides a reliable and computationally efficient framework for modeling transition metal sulfides.
• This improved modeling capability has broad applicability for the rational design of advanced energy storage materials, electrocatalysts, and sustainable metallurgical processes.