Accurate prediction of grain boundary structures and energetics in CdTe: a machine-learning potential approach
Tatsuya Yokoi, Kosuke Adachi, Sayuri Iwase, Katsuyuki Matsunaga
Abstract
GBs as training datasets. Such improvement will offer a way to accurately predict atomic structures of general GBs within practical computational cost.
Topics & Concepts
EnergeticsTransferabilityDensity functional theoryArtificial neural networkRelaxation (psychology)Computer scienceInteratomic potentialGrain boundaryStability (learning theory)Range (aeronautics)Potential energyStatistical physicsArtificial intelligenceMachine learningMaterials scienceChemistryPhysicsMolecular dynamicsComputational chemistryAtomic physicsThermodynamicsCrystallographySocial psychologyMicrostructurePsychologyLogitComposite materialMachine Learning in Materials ScienceAdvanced Semiconductor Detectors and MaterialsChalcogenide Semiconductor Thin Films