89 citations · 182 across the 17 of their papers we have counts for
18 papers
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…
Temperature dependence of the Gibbs energies of formation of point defects in B2 MoTa from ab initio calculations
Xiang Xu, Fritz Körmann, Sergiy Divinski +2
Using B2 MoTa, the strongest B2 former among group V/VI refractory binaries, as a model system, we compute temperature-dependent Gibbs energies of formation of vacancies and antisi…
Hydrogen uptake and hydride formation in AlCoCrFeNi high-entropy alloys: First-principles, universal-potential, and experimental study
Fritz Körmann, Yuji Ikeda, Konstantin Glazyrin +9
Hydrogen uptake in complex multicomponent alloys, including high-entropy alloys (HEAs), governs both hydrogen storage capacity and resistance to hydrogen-induced degradation. We co…
Finely Tunable Thermal Expansion of NiTi by Stress-Induced Martensitic Transformation and Thermomechanical Training
Won Seok Choi, Won-Seok Ko, Yejun Park +6
Tailoring the thermal expansion of martensitic materials by crystallographic texture and anisotropic variation of lattice parameters is a promising route to a flexible design of th…
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…
Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte LiPSCl using machine-learning interatomic potentials
Yongliang Ou, Yuji Ikeda, Lena Scholz +3
LiPSCl 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…
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…
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…
Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten
Xi Zhang, Sergiy V. Divinski, Blazej Grabowski
We propose an efficient ab initio framework to compute the Gibbs energy of the transition state in vacancy-mediated diffusion including the relevant thermal excitations at density-…
Design of a V-Ti-Ni alloy with superelastic nano-precipitates
J. -L. Zhang, J. L. Cann, S. B. Maisel +8
Stress-induced martensitic transformations enable metastable alloys to exhibit enhanced strain hardening capacity, leading to improved formability and toughness. As is well-known f…