41 citations · 67 across the 7 of their papers we have counts for
6 papers · 1 filter
How Atoms Interact Within Molecules
Adil Kabylda, Malte Esders, Matteo Gori +3
Fundamental understanding of interatomic forces in molecules must emerge from quantum mechanics, yet widely used empirical force fields rely on simplified mechanistic approximation…
MBD-ML: Many-body dispersion from machine learning for molecules and materials
Evgeny Moerman, Adil Kabylda, Almaz Khabibrakhmanov +1
Van der Waals (vdW) interactions are essential for describing molecules and materials, from drug design and catalysis to battery applications. These omnipresent interactions must a…
QCell: Comprehensive Quantum-Mechanical Dataset Spanning Diverse Biomolecular Fragments
Adil Kabylda, Sergio Suárez-Dou, Nils Davoine +2
Recent advances in machine learning force fields (MLFFs) are revolutionizing molecular simulations by bridging the gap between quantum-mechanical (QM) accuracy and the computationa…
Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
Malte Esders, Thomas Schnake, Jonas Lederer +4
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a te…
Accurate global machine learning force fields for molecules with hundreds of atoms
Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke +4
Global machine learning force fields (MLFFs), that have the capacity to capture collective many-atom interactions in molecular systems, currently only scale up to a few dozen atoms…
Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules
Adil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela +2
Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fract…