Machine learning interatomic potentials in engineering perspective for developing cathode materials
Dohyeong Kwon, Duho Kim
Abstract
Machine learning interatomic potentials (MLIPs) predict thermodynamic phase stability and structural parameters like density functional theory (DFT) but are much faster, making them valuable for engineering applications.
Topics & Concepts
Density functional theoryStability (learning theory)Interatomic potentialPerspective (graphical)Phase (matter)Computer scienceStructural stabilityCathodeMaterials scienceStatistical physicsArtificial intelligenceMachine learningMolecular dynamicsComputational chemistryChemistryPhysicsPhysical chemistryEngineeringQuantum mechanicsStructural engineeringMachine Learning in Materials ScienceAdvancements in Battery MaterialsElectron and X-Ray Spectroscopy Techniques