3 papers
cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2024
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…
cond-mat.dis-nn2023
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…