Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
Bohayra Mortazavi, Evgeny V. Podryabinkin, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, Alexander V. Shapeev
Abstract
We highlight that machine-learning interatomic potentials trained over short AIMD trajectories enable first-principles multiscale modeling, bridging DFT level accuracy to the continuum level and empowering the study of complex/novel nanostructures.
Topics & Concepts
Multiscale modelingMaterials scienceBridging (networking)Thermal conductivityInteratomic potentialHeterojunctionCondensed matter physicsLattice (music)ThermalMolecular dynamicsStatistical physicsConductivityElectrical resistivity and conductivityAtomic force microscopyThermal properties of materialsMachine Learning in Materials ScienceAdvanced Thermoelectric Materials and Devices