Machine learning for accelerated prediction of lattice thermal conductivity at arbitrary temperature
Zihe Li, Mengke Li, Yufeng Luo, Haibin Cao, Huijun Liu, Ying Fang
Abstract
We propose a neural network model that allows ready and accurate prediction of the lattice thermal conductivities of crystalline materials at arbitrary temperature.
Topics & Concepts
Thermal conductivityMaterials scienceLattice (music)Artificial intelligenceThermalCondensed matter physicsStatistical physicsComputer scienceMachine learningThermodynamicsPhysicsComposite materialAcousticsMachine Learning in Materials Science