4 citations · 4 across the 3 of their papers we have counts for
5 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…
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…
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…
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…
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…