2 citations · 4 across the 8 of their papers we have counts for
10 papers
Predicting Spin-Crossover Behavior in Metal-Organic Frameworks from Limited and Noisy Data Using Quantile Active Learning
Ashna Jose, Emilie Devijver, Martin Uhrin +2
Spin-crossover (SCO) metal-organic frameworks (MOFs) hold great promise for sensing, spintronics, and gas-related applications, however, only a small number of SCO-active examples…
Reproducible container solutions for codes and workflows in materials science
Dylan Bissuel, Léo Orveillon, Benjamin Arrondeau +15
A computing solution combining the GNU Guix functional package manager with the Apptainer container system is presented. This approach provides fully declarative and reproducible s…
Exotic collective dynamics in molten Carbon
Taras Bryk, Giancarlo Ruocco, Jean-François Wax +1
Collective longitudinal and transverse propagating modes in molten Carbon at ~K and pressure range - GPa are reported from {\it ab initio} based as well as machine…
Crystal Nucleation in Eutectic Al-Si Alloys by Machine-Learned Molecular Dynamics
Quentin Bizot, Noel Jakse
Solidification control is crucial in manufacturing technologies, as it determines the microstructure and, consequently, the performance of the final product. Investigating the mech…
Impact of local structure on melt dynamics in Cu-Ti alloys: Insights from ab-initio molecular dynamics simulations
Lucas P. Kreuzer, Fan Yang, Andreas Mayer +1
First-principle based molecular-dynamics simulations have been performed for binary CuTi (x = 0.31, 0.50, and 0.76) alloys to investigate the relationship between local…
Diffusion in liquid metals is directed by competing collective modes
Franz Demmel, Noel Jakse
The self-diffusion process in a dense liquid is influenced by collective particle movements. Extensive molecular dynamics simulations for liquid aluminium and rubidium evidence a c…
Homogeneous Nucleation of Undercooled Al-Ni melts via a Machine-Learned Interaction Potential
Johannes Sandberg, Thomas Voigtmann, Emilie Devijver +1
Homogeneous nucleation processes are important for understanding solidification and the resulting microstructure of materials. Simulating this process requires accurately describin…
Feature Selection for High-Dimensional Neural Network Potentials with the Adaptive Group Lasso
Johannes Sandberg, Thomas Voigtmann, Emilie Devijver +1
Neural network potentials are a powerful tool for atomistic simulations, allowing to accurately reproduce \textit{ab initio} potential energy surfaces with computational performanc…
Structure-Dynamics Relationship in Al-Mg-Si Liquid Alloys
Alaa Fahs, Philippe Jarry, Noël Jakse
Enhancing properties and performances of aluminium alloys by a control of their solidification is pivotal in automotive and aerospace industries. The fundamental role of the struct…
Comment on "Universal effect of excitation dispersion on the heat capacity and gapped states in fluids"
T. Bryk, N. Jakse, I. Mryglod +2
We discuss the validity of recent results in [Phys. Rev. Lett. 125, 125501 (2020)] on an universal relation between the heat capacity and dispersions of collective excitations in l…