Accessing the thermal conductivities of Sb<sub>2</sub>Te<sub>3</sub> and Bi<sub>2</sub>Te<sub>3</sub>/Sb<sub>2</sub>Te<sub>3</sub> superlattices by molecular dynamics simulations with a deep neural network potential
Pan Zhang, Mi Qin, Zhenhua Zhang, Dan Jin, Yong Liu, Ziyu Wang, Zhihong Lu, Jing Shi, Rui Xiong
Abstract
superlattices with different periods are accurately predicted using non-equilibrium molecular dynamics (NEMD) simulations together with an NNP, which serves as a good example to explore the thermal transport physics of superlattices using a deep neural network potential.
Topics & Concepts
AnharmonicityPhononSuperlatticeBoltzmann equationThermal conductivityCondensed matter physicsThermoelectric materialsThermoelectric effectMolecular dynamicsMaterials sciencePhonon scatteringLattice (music)ScatteringThermalPhysicsThermodynamicsOpticsQuantum mechanicsAcousticsThermal properties of materialsAdvanced Thermoelectric Materials and DevicesMachine Learning in Materials Science