Free energy predictions for crystal stability and synthesisability
Kasper Tolborg, Johan Klarbring, Alex M. Ganose, Aron Walsh
Abstract
Prediction of crystal stability and synthesisability is crucial for accelerated materials design. We discuss modern free energy methods for building more accurate models and data-driven approaches suitable for screening large chemical spaces.
Topics & Concepts
Stability (learning theory)Energy (signal processing)Crystal structure predictionCrystal (programming language)Chemical stabilityComputer scienceMaterials scienceStatistical physicsCrystal structurePhysicsThermodynamicsChemistryCrystallographyMachine learningMathematicsStatisticsProgramming languageMachine Learning in Materials ScienceX-ray Diffraction in CrystallographyCatalysis and Oxidation Reactions