4 papers · 1 filter
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…
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…
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 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…