Investigation of phase transition, mechanical behavior and lattice thermal conductivity of halogen perovskites using machine learning interatomic potentials
Yongbo Shi, Yuanyuan Chen, Haikuan Dong, Hao Wang, Ping Qian
Abstract
only undergoes a phase transition from α to β. Then, the key mechanical parameters, including Poisson's ratio, tensile strength, critical strain and bulk modulus, are predicted. The thermal conductivity is also investigated using the NEP-based MD simulations. At room temperature, they exhibit extremely low thermal conductivity. The predicted results are compared with the experimental results, and the rationality of ML potentials has been confirmed.
Topics & Concepts
Materials sciencePhase transitionTetragonal crystal systemThermal conductivityInteratomic potentialOrthorhombic crystal systemThermodynamicsCondensed matter physicsPhase (matter)ChemistryCrystal structureComputational chemistryMolecular dynamicsCrystallographyComposite materialPhysicsOrganic chemistryMachine Learning in Materials SciencePerovskite Materials and ApplicationsAdvanced Thermoelectric Materials and Devices