4 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…
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…
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…