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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…
Electron-induced non-monotonic pressure dependence of the lattice thermal conductivity of θ-TaN
Ashis Kundu, Yani Chen, Xiaolong Yang +5
Recent theoretical and experimental research suggests that -TaN is a semimetal with high thermal conductivity (), primarily due to the contribution of phonons ($κ_\texttt{ph}…
How to verify the precision of density-functional-theory implementations via reproducible and universal workflows
Emanuele Bosoni, Louis Beal, Marnik Bercx +42
In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter phys…
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…