Blazej Grabowski

activity

20212026

most citedMachine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: Prediction of density functional theory and experimental data

89 citations · 182 across the 17 of their papers we have counts for

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Showing 2024Show all

3 papers · 1 filter

cond-mat.mtrl-sci2024★ 13 cited

Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li $_{6}$ PS $_{5}$ Cl using machine-learning interatomic potentials

Yongliang Ou, Yuji Ikeda, Lena Scholz +3

Li $_{6}$ PS $_{5}$ Cl is a promising candidate for the solid electrolyte in all-solid-state Li-ion batteries. In applications, this material is in a polycrystalline state with grain bound…

cond-mat.mtrl-sci2024★ 2 cited

Origin of the yield stress anomaly in L12 intermetallics unveiled with physically-informed machine-learning potentials

Xiang Xu, Xi Zhang, Erik Bitzek +2

The yield stress anomaly of L12 intermetallics such as Ni3Al or Ni3Ga is controlled by the so-called Kear-Wilsdorf lock (KWL), of which the formation and unlocking are governed by…

cond-mat.mtrl-sci2024★ 89 cited

Machine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: Prediction of density functional theory and experimental data

Shivam Dangwal, Yuji Ikeda, Blazej Grabowski +1

Safe and high-density storage of hydrogen, for a clean-fuel economy, can be realized by hydride-forming materials, but these materials should be able to store hydrogen at room temp…

Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6​PS5​Cl using machine-learning interatomic potentials

Origin of the yield stress anomaly in L12 intermetallics unveiled with physically-informed machine-learning potentials

Machine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: Prediction of density functional theory and experimental data

Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li $_{6}$ PS $_{5}$ Cl using machine-learning interatomic potentials