Point defect formation at finite temperatures with machine learning force fields
Irea Mosquera‐Lois, Johan Klarbring, Aron Walsh
Abstract
by two orders of magnitude - and can thus significantly affect the predicted properties. Overall, our study underscores the importance of finite-temperature effects and the potential of MLFFs to model defect dynamics at both synthesis and device operating temperatures.
Topics & Concepts
Point (geometry)Computer scienceTheoretical physicsStatistical physicsPhysicsClassical mechanicsMaterials scienceMathematicsGeometryMachine Learning in Materials Science