54 citations · 299 across the 19 of their papers we have counts for
20 papers
Benchmarking machine-learning interatomic potentials using classical force fields
Johannes Sandberg, Geneviève Dusson, Julien Lam
We propose a rational comparison between several well-known machine-learning interatomic potentials (linear atomic cluster expansion, the Behler-Parrinello high-dimensional neural…
Competing nucleation pathways in nanocrystal formation
Carlos R. Salazar, Akshay Krishna Ammothum Kandy, Jean Furstoss +3
Despite numerous efforts from numerical approaches to complement experimental measurements, several fundamental challenges have still hindered one's ability to truly provide an ato…
Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations
Quentin Gromoff, Patrizio Benzo, Wissam A. Saidi +6
While nanoalloys are of paramount scientific and practical interests, the main processes leading to their formation are still poorly understood. Key structural features in the allo…
Harvesting nucleating structures in nanoparticle crystallization: The example of gold, silver and iron
Arthur France-Lanord, Sarath Menon, Julien Lam
The thermodynamics and kinetics of crystallization of nanoparticles, as opposed to bulk phases, may be influenced by surface and size effects. We investigate the importance of such…
A critical comparison of general-purpose collective variables for crystal nucleation
Julien Lam, Fabio Pietrucci
The nucleation of crystals is a prominent phenomenon in science and technology that still lacks a full atomic-scale understanding. Much work has been devoted to identifying order p…
Non-classical nucleation of zinc oxide from a physically-motivated machine-learning approach
Gaétan Laurens, Jacek Goniakowski, Julien Lam
Observing non-classical nucleation pathways remains challenging in simulations of complex materials with technological interests. This is because it requires very accurate force fi…