1 citations · 2 across the 7 of their papers we have counts for
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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…
Unsupervised topological learning approach of crystal nucleation
Sébastien Becker, Emilie Devijver, Rémi Molinier +1
Nucleation phenomena commonly observed in our every day life are of fundamental, technological and societal importance in many areas, but some of their most intimate mechanisms rem…
Unsupervised topological learning for identification of atomic structures
Sébastien Becker, Emilie Devijver, Rémi Molinier +1
We propose an unsupervised learning methodology with descriptors based on Topological Data Analysis (TDA) concepts to describe the local structural properties of materials at the a…