5 papers
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…
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
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…