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Thermal properties of single-layer MoS<sub>2</sub>–WS<sub>2</sub> alloys enabled by machine-learned interatomic potentials

Juan M. Marmolejo‐Tejada, Martín A. Mosquera

2022Chemical Communications11 citationsDOI

Abstract

and their alloys, and demonstrates a synergy of theoretical techniques that is anticipated to play an important role in the field. From a high-performance computing perspective, these yield very convenient inter-atomic (or inter-molecular in other contexts) potentials that are useful to predict the response of quantum materials to thermal perturbations, or other driving forces. We show that our trained MTP functions successfully describe vibrational properties of the systems, and their thermal conductivities. The trained potential displays consistent agreement with DFT calculations, as well as the Stillinger-Weber (SW) potential. We also find that the thermal conductivity of the 2D alloys is little affected by sulfur vacancies. This is a behavior that may aid the fine-tuning of material's thermal properties for heat management and energy storage and conversion applications.

Topics & Concepts

Thermal conductivityDensity functional theoryThermalInteratomic potentialMaterials scienceWork (physics)Field (mathematics)Thermal conductionLayer (electronics)QuantumMolecular dynamicsNanotechnologyComputational chemistryChemistryThermodynamicsPhysicsQuantum mechanicsPure mathematicsComposite materialMathematicsThermal properties of materialsMachine Learning in Materials Science2D Materials and Applications