Machine-learning interatomic potentials (GAP, MACE) combined with lattice dynamics and NEMD predict thermal boundary resistance at silicon grain boundaries that is sensitive to interfacial roughness and differs substantially from classical potential predictions at high roughness.
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The first-order structural phase transition in Nb3Cl8 is entropy-driven, with the alpha phase stabilized by softer phonons and larger paramagnetic spin entropy while the beta phase is favored by interlayer dimerization that hardens phonons and quenches spin entropy via singlets.
citing papers explorer
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Phonon-Mediated Thermal Transport in Nanocrystalline Silicon Using Machine-Learning Interatomic Potentials
Machine-learning interatomic potentials (GAP, MACE) combined with lattice dynamics and NEMD predict thermal boundary resistance at silicon grain boundaries that is sensitive to interfacial roughness and differs substantially from classical potential predictions at high roughness.
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Entropy-Driven Structural Phase Transition in Nb$_3$Cl$_8$ via Density Functional Theory and an Effective Model
The first-order structural phase transition in Nb3Cl8 is entropy-driven, with the alpha phase stabilized by softer phonons and larger paramagnetic spin entropy while the beta phase is favored by interlayer dimerization that hardens phonons and quenches spin entropy via singlets.