3 papers
physics.chem-ph2026
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…
physics.chem-ph2026
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…
cond-mat.mtrl-sci2025
Advancing Quantum Many-Body GW Calculations on Exascale Supercomputing Platforms
Benran Zhang, Daniel Weinberg, Chih-En Hsu +9
Advanced ab initio materials simulations face growing challenges as increasing systems and phenomena complexity requires higher accuracy, driving up computational demands. Quantum…