9 citations · 17 across the 6 of their papers we have counts for
6 papers
Grain boundary evolution in nanoparticles
Manoj Settem, Pranav Kumar, Ajeet K. Srivastav
Grain boundary evolution is a key dynamical process that enables structural rearrangements in nanoparticles and drives them towards low energy configurations. Grain boundaries can…
An Ontology for Machine Learning Interatomic Potentials
Daniel Hernández, Jong Hyun Jung, Yuji Ikeda +11
Machine learning interatomic potentials (MLIPs) approximate quantum-mechanical energies and forces---conventionally computed by density functional theory (DFT) or wave-function met…
Machine learning via artificial neural networks coupled with density functional theory and experiments for thermodynamic optimization of high-entropy alloys for hydrogen storage at room temperature
Shivam Dangwal, Pranav Kumar, Yuji Ikeda +2
High-entropy alloys (HEAs) have received considerable attention for hydrogen storage because of their compositional flexibility; however, designing HEAs with optimal thermodynamics…
Hydrogen diffusion in TiCrH Laves phases: A combined ab initio and machine-learning-potential study
Pranav Kumar, Fritz Körmann, Kaveh Edalati +2
The kinetics of hydrogen diffusion in C15 cubic and C14 hexagonal TiCrH (0 < <= 4) Laves-phase hydrogen storage alloys is investigated with density functional theory (D…
Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions
Yuji Ikeda, Axel Forslund, Pranav Kumar +4
Machine-learning interatomic potentials (MLIPs) enable large-scale atomistic simulations at moderate computational cost while retaining ab initio accuracy. MLIPs trained on coupled…
Machine Learning Potentials for Hydrogen Absorption in TiCr Laves Phases
Pranav Kumar, Fritz Körmann, Blazej Grabowski +1
The energetics of hydrogen absorption in C15 cubic and C14 hexagonal TiCrH Laves phases is investigated for with density functional theory (DFT) and machine l…