89 citations · 182 across the 17 of their papers we have counts for
8 papers · 1 filter
An experimentally validated end-to-end framework for operando modeling of intrinsically complex metallosilicates
Jong Hyun Jung, Tom Schächtel, Yongliang Ou +5
Structurally and chemically complex materials such as amorphous metallosilicates underpin major catalytic and separation technologies, yet their intrinsic complexity challenges rel…
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…
Microstructural Insights into Fast Ion Transport in Solid Electrolytes via Multiscale Modeling
Yongliang Ou, Lena Scholz, Sanath Keshav +7
Improving solid electrolytes is critical for high-performance all-solid-state batteries, yet the microstructural features that enable fast ion transport remain poorly understood. H…
Lattice distortions and non-sluggish diffusion in BCC refractory high entropy alloys
Jingfeng Zhang, Xiang Xu, Fritz Körmann +10
Refractory high-entropy alloys (RHEAs) have emerged as promising candidates for extreme high-temperature applications, for example, in next-generation turbines and nuclear reactors…
Accurate complex-stacking-fault Gibbs energy in Ni3Al at high temperatures
Xiang Xu, Xi Zhang, Andrei Ruban +2
To gain a deeper insight into the anomalous yield behavior of Ni3Al, it is essential to obtain temperature-dependent formation Gibbs energies of the relevant planar defects. Here,…
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…
Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs
Axel Forslund, Jong Hyun Jung, Yuji Ikeda +1
We propose a free-energy-perturbation approach accelerated by machine-learning potentials to efficiently compute transition temperatures and entropies for all rungs of Jacob's ladd…
A collapsed interface approach to resolve grain boundaries in finite element simulations of polycrystalline diffusion
Lena Scholz, Yongliang Ou, Blazej Grabowski +1
Atomic diffusion affects the properties of various engineering materials, which predominantly occur in the polycrystalline state. A rigorous description of polycrystalline diffusio…