Theoretical and computational methods for accelerated materials discovery

Predicting properties of materials and phase transformation using theoretical and computational multi-scale methods involving artificial intelligence and machine learning is important and highly rewarding. We investigate reliability of the relevant methods, apply them to caloric materials and high-entropy alloys, and demonstrate how theoretical guidance for experiment accelerates materials discovery.