Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics
Takeru Miyagawa, Namita Krishnan, Manuel Grumet, Christian Reverón Baecker, Waldemar Kaiser, David A. Egger
Abstract
Machine-learning molecular dynamics provides predictions of structural and anharmonic vibrational properties of solid-state ionic conductors with ab initio accuracy. This opens a path towards rapid design of novel battery materials.
Topics & Concepts
IonElectrical conductorChemical physicsSolid-stateMolecular dynamicsComputer scienceMaterials scienceNanotechnologyEngineering physicsPhysicsChemistryComputational chemistryComposite materialQuantum mechanicsMachine Learning in Materials ScienceAdvanced Memory and Neural ComputingAdvancements in Battery Materials