3 papers
physics.chem-ph2025
Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Christoph Brunken, Olivier Peltre, Heloise Chomet +11
Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of…
physics.chem-ph2025
Universally applicable and tunable graph-based coarse-graining for Machine learning force fields
Christoph Brunken, Sebastien Boyer, Mustafa Omar +7
Coarse-grained (CG) force field methods for molecular systems are a crucial tool to simulate large biological macromolecules and are therefore essential for characterisations of bi…
cs.DS2022
Unit Perturbations in Budgeted Spanning Tree Problems
Hassene Aissi, Solal Attias, Da Qi Chen +1
The minimum spanning tree of a graph is a well-studied structure that is the basis of countless graph theoretic and optimization problem. We study the minimum spanning tree (MST) p…