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…
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…
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…
Static Subspace Approximation for Random Phase Approximation Correlation Energies: Implementation and Performance
Daniel Weinberg, Olivia A. Hull, Jacob M. Clary +3
Developing theoretical understanding of complex reactions and processes at interfaces requires using methods that go beyond semilocal density functional theory to accurately descri…
BEAST DB: Grand-Canonical Database of Electrocatalyst Properties
Cooper Tezak, Jacob Clary, Sophie Gerits +12
We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent…