206 citations · 771 across the 22 of their papers we have counts for
5 papers · 1 filter
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Systematic and accurate anharmonic formation free energies of metastable defects via a constrained Bayesian Adaptive Biasing Force framework
Clovis Lapointe, Anruo Zhong, Manuel Athènes +1
Computing the formation free energies of metastable defects at finite temperature remains challenging due to anharmonicity, the multiplicity of basins and the frequent occurrence o…
Girsanov Reweighting for Uncertainty Propagation in Rare-Event Kinetics
Leonard Moracchini, Thomas Pigeon, Morgane Menz +3
Machine-learning interatomic potentials (MLIPs) have become a powerful tool for rare event sampling in molecular dynamics, offering near ab initio accuracy at a fraction of the com…
Revisiting quantum effects on dislocation glide in bcc metals from DFT calculations and machine-learning potentials
Arnaud Allera, Lisa Ventelon, Mihai-Cosmin Marinica +2
Quantum zero-point effects have been long proposed to explain a well-known discrepancy between the low-temperature flow stresses of body-centered cubic metals and corresponding ato…
Fully anharmonic calculations of the free energy of migration of point defects in UO2 and PuO2
Dillon G. Frost, Johann Bouchet, Mihai-Cosmin Marinica +3
Calculating diffusion rates of point defects in materials typically relies on the harmonic approximation to estimate migration free energies. However, anharmonic effects can have a…