6 papers
Temperature-dependent Raman spectra of 2H-MoS2 from Machine Learning-driven statistical sampling
Samuel Longo, Aloïs Castellano, Matthieu J. Verstraete
Molybdenum sulfides are in the spotlight of materials science thanks to their interesting properties for applications in optoelectronics, nanocomposites, lubricants, and catalysis.…
Thermal Conductivity Of Monolayer Hexagonal Boron Nitride: Four-Phonon Scattering And Quantum Sampling Effects
José Pedro Alvarinhas Batista, Matthieu J. Verstraete, Aloïs Castellano
Monolayer hexagonal boron nitride is a prototypical planar 2-dimensional system material and has been the subject of many investigations of its exceptional vibrational, spectroscop…
Fluctuation-dissipation and virtual processes in interacting phonon systems
Aloïs Castellano, J. P. Alvarinhas Batista, Matthieu J. Verstraete
Phonon-phonon interactions are fundamental to understanding a wide range of material properties, including thermal transport and vibrational spectra. In conventional perturbative a…
Machine learning assisted canonical sampling (MLACS)
Aloïs Castellano, Romuald Béjaud, Pauline Richard +8
The acceleration of material property calculations while maintaining ab initio accuracy (1 meV/atom) is one of the major challenges in computational physics. In this paper, we intr…
Mode-coupling formulation of heat transport in anharmonic materials
Aloïs Castellano, J. P. Alvarinhas Batista, Olle Hellman +1
The temperature-dependent phonons are a generalization of interatomic force constants varying in T, which as found widespread use in computing the thermal transport of materials. A…
Electron-mediated anharmonicity and its role in the Raman spectrum of graphene
Nina Girotto Erhardt, Aloïs Castellano, J. P. Alvarinhas Batista +4
The Raman active G mode in graphene exhibits strong coupling to electrons, yet the comprehensive treatment of this interaction in the calculation of its temperature-dependent Raman…