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Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution

Bohayra Mortazavi, Evgeny V. Podryabinkin, Ivan S. Novikov, Timon Rabczuk, Xiaoying Zhuang, Alexander V. Shapeev

2020Computer Physics Communications215 citationsDOIOpen Access PDF

Topics & Concepts

AnharmonicityThermal conductivityInteratomic potentialDensity functional theoryMolecular dynamicsBoltzmann equationBottleneckAb initioEmbedded atom modelStatistical physicsMaterials scienceComputer scienceThermodynamicsPhysicsComputational chemistryChemistryCondensed matter physicsQuantum mechanicsEmbedded systemThermal properties of materialsMachine Learning in Materials ScienceAdvanced Thermoelectric Materials and Devices
Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution | Litcius