206 citations · 771 across the 22 of their papers we have counts for
23 papers
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…
Agnostic calculation of atomic free energies with the descriptor density of states
Thomas D Swinburne, Clovis Lapointe, Mihai-Cosmin Marinica
We present a new method to evaluate vibrational free energies of atomic systems without a priori specification of an interatomic potential. Our model-agnostic approach leverages de…
Activation entropy of dislocation glide in body-centered cubic metals from atomistic simulations
Arnaud Allera, Thomas D. Swinburne, Alexandra M. Goryaeva +5
The activation entropy of dislocation glide, a key process controlling the strength of many metals, is often assumed to be constant or linked to enthalpy through the empirical Meye…
Calculation of crystal defects induced in CaWO by 100 eV displacement cascades using a linear Machine Learning interatomic potential
Gabrielle Soum-Sidikov, Jean-Paul Crocombette, Mihai-Cosmin Marinica +3
We determine the energy stored in the crystal defects induced by \,eV nuclear recoils in low-threshold CaWO cryogenic detectors. A Machine Learning inter…
Robust crystal structure identification at extreme conditions using a density-independent spectral descriptor and supervised learning
Paul Lafourcade, Jean-Bernard Maillet, Christophe Denoual +4
The increased time- and length-scale of classical molecular dynamics simulations have led to raw data flows surpassing storage capacities, necessitating on-the-fly integration of s…
Neighbors Map: an Efficient Atomic Descriptor for Structural Analysis
Arnaud Allera, Alexandra M. Goryaeva, Paul Lafourcade +2
Accurate structural analysis is essential to gain physical knowledge and understanding of atomic-scale processes in materials from atomistic simulations. However, traditional analy…
Synergistic coupling in ab initio-machine learning simulations of dislocations
Petr Grigorev, Alexandra M. Goryaeva, Mihai-Cosmin Marinica +2
Ab initio simulations of dislocations are essential to build quantitative models of material strength, but the required system sizes are often at or beyond the limit of existing me…
Quantum-accurate magneto-elastic predictions with classical spin-lattice dynamics
Svetoslav Nikolov, Mitchell A. Wood, Attila Cangi +5
A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-e…
Solute diffusion by self-interstitial defects and radiation-induced segregation in ferritic Fe-X (X=Cr, Cu, Mn, Ni, P, Si) dilute alloys
Luca Messina, Thomas Schuler, Maylise Nastar +2
This work investigates solute transport due to self-interstitial defects and radiation induced segregation tendencies in dilute ferritic alloys, by computing the transport coeffici…
Relaxation volumes of microscopic and mesoscopic irradiation-induced defects in tungsten
Daniel R Mason, Duc Nguyen-Manh, Mihai-Cosmin Marinica +2
The low energy structures of irradiation-induced defects have been studied in detail, as these determine the available modes by which a defect can diffuse or relax. As a result, th…
Smart energy models for atomistic simulations using a DFT-driven multifidelity approach
Luca Messina, Alessio Quaglino, Alexandra Goryaeva +5
The reliability of atomistic simulations depends on the quality of the underlying energy models providing the source of physical information, for instance for the calculation of mi…
Using Bayes formula to estimate rates of rare events in transition path sampling simulations
Pierre Terrier, Mihai-Cosmin Marinica, Manuel Athènes
Transition path sampling is a method for estimating the rates of rare events in molecular systems based on the gradual transformation of a path distribution containing a small frac…
Point defect modeling in materials: coupling ab initio and elasticity approaches
Céline Varvenne, Fabien Bruneval, Mihai-Cosmin Marinica +1
Modeling point defects at an atomic scale requires careful treatment of the long-range atomic relaxations. This elastic field can strongly affect point defect properties calculated…
Free energy calculations from adaptive molecular dynamics simulations with adiabatic reweighting
Lingling Cao, Gabriel Stoltz, Tony Lelièvre +2
We propose an adiabatic reweighting algorithm for computing the free energy along an external parameter from adaptive molecular dynamics simulations. The adaptive bias is estimated…
Estimating time-correlation functions by sampling and unbiasing dynamically activated events
Manuel Athènes, Mihai-Cosmin Marinica, Thomas Jourdan
Transition path sampling is a rare-event method that estimates state-to-state timecorrelation functions in many-body systems from samples of short trajectories. In this framework,…
Assessment of interatomic potentials for atomistic analysis of static and dynamic properties of screw dislocations in W
David Cereceda, J. Manuel Perlado, Alexander Stukowski +4
Screw dislocations in bcc metals display non-planar cores at zero temperature which result in high lattice friction and thermally activated strain rate behavior. In bcc W, electron…
Free energy reconstruction from steered dynamics without post-processing
Manuel Athènes, Mihai-Cosmin Marinica
Various methods achieving importance sampling in ensembles of nonequilibrium trajectories enable to estimate free energy differences and, by maximum-likelihood post-processing, to…
Some improvements of the ART method for finding transition pathways on potential energy surfaces
E. Cancès, F. Legoll, M. -C. Marinica +2
The Activation-Relaxation Technique nouveau (ARTn) is an eigenvector following method for systematic search of saddle points and transition pathways on a given potential energy sur…
Diffusion rates of Cu adatoms on Cu(111) in the presence of an adisland nucleated at FCC or HCP sites
Mihai-Cosmin Marinica, Cyrille Barreteau, Daniel Spanjaard +1
The surface diffusion of Cu adatoms in the presence of an adisland at FCC or HCP sites on Cu(111) is studied using the EAM potential derived by Mishin {\it et al.} [Phys. Rev. B {\…