5 papers
Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
Nima Karimitari, Jacob Clary, Derek Vigil-Fowler +3
Once trained, machine-learned interatomic potentials (MLIPs) provide a fast and accurate way to study catalytic reaction pathways, but their performance strongly depends on the tra…
Role of Wadsley Defects and Cation Disorder to Enhance MoNb12O33 Diffusion
CJ Sturgill, Manish Kumar, Nima Karimitari +8
Wadsley-Roth (WR) niobates have emerged as high-rate anode materials that can combine rapid ionic diffusion with good electronic conductivity. WR compounds have been defect-enhance…
Combined Experimental and Computational Analysis of Lithium Diffusion in Isostructural Pair VNb9O25 and VTa9O25
Manish Kumar, Md Abdullah Al Muhit, CJ Sturgill +6
Wadsley-Roth crystal structures are an attractive class of materials for batteries because lithium diffusion is facilitated by the ReO3-like block structure with electron transport…
Development of machine-learned interatomic potentials to predict structure, transport, and reactivity in platinum-based fuel cells
Kamron Fazel, Sam Brown, Jacob Clary +5
Machine-learned interatomic potentials (MLIPs) have rapidly progressed in accuracy, speed, and data efficiency in recent years. However, training robust MLIPs in multicomponent sys…
Bridging electronic and classical density-functional theory using universal machine-learned functional approximations
Michelle M. Kelley, Joshua Quinton, Kamron Fazel +3
The accuracy of density-functional theory (DFT) is determined by the quality of the approximate functionals, such as exchange-correlation in electronic DFT and the excess functiona…