263 citations · 519 across the 10 of their papers we have counts for
12 papers
Machine-learning potential for phonon transport in AlN with defects in multiple charge states
Ying Dou, Koji Shimizu, Jesús Carrete +2
Understanding phonon transport properties in defect-laden AlN is important for their device applications. Here, we construct a machine-learning potential to describe phonon transpo…
Neural-network-enabled molecular dynamics study of HfO phase transitions
Sebastian Bichelmaier, Jesús Carrete, Georg K. H. Madsen
The advances of machine-learned force fields have opened up molecular dynamics (MD) simulations for compounds for which ab-initio MD is too resource-intensive and phenomena for whi…
Neural-Network Force Field Backed Nested Sampling: Study of the Silicon p-T Phase Diagram
N. Unglert, J. Carrete, L. B. Pártay +1
Nested sampling is a promising method for calculating phase diagrams of materials, however, the computational cost limits its applicability if ab-initio accuracy is required. In th…
Deep Ensembles vs. Committees for Uncertainty Estimation in Neural-Network Force Fields: Comparison and Application to Active Learning
Jesús Carrete, Hadrián Montes-Campos, Ralf Wanzenböck +2
A reliable uncertainty estimator is a key ingredient in the successful use of machine-learning force fields for predictive calculations. Important considerations are correlation wi…
Predicting the lattice thermal conductivity of solids by solving the Boltzmann transport equation: AFLOW - AAPL an automated, accurate and effcient framework
Jose J. Plata, Demet Usanmaz, Pinku Nath +7
One of the most accurate approaches for calculating lattice thermal conductivity, , is solving the Boltzmann transport equation starting from third-order anharmonic force cons…
Optimizing phonon scattering by nanoprecipitates in lead chalcogenides
Xiaolong Yang, Jesús Carrete, Zhao Wang
We calculate the thermal conductivity of PbTe and PbS with seven different types of nanoprecipitates using an ab-initio-based Boltzmann transport approach. We find that precipitate…