6 papers
Accurate and Transferable Intermolecular Potential Based on Machine-Learned Molecular Electron Density
Dahvyd Wing, Mihail Bogojeski, Szabolcs Goger +2
Machine-learned force fields (MLFFs) contain many learnable parameters and therefore require large training datasets. This poses a challenge for developing highly accurate, general…
A Transferable Model of Molecular Exchange-Repulsion Interaction from Anisotropic Valence Density Overlap
Dahvyd Wing, Alexandre Tkatchenko
Pauli exchange-repulsion is the dominant short-range intermolecular interaction and it is an essential component of molecular force fields. Current approaches to modeling Pauli rep…
Quantum-Accurate Conformational Stabilities and Vibrational Dynamics in Molecules and Proteins with Machine-Learned Force Fields
Sergio Suárez-Dou, Miguel Gallegos, Kyunghoon Han +3
Biomolecular thermodynamics and spectroscopy depend on relative conformer energies, local curvatures, and collective dipole fluctuations on the potential-energy surface. Convention…
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…
aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields
Tobias Henkes, Shubham Sharma, Alexandre Tkatchenko +2
Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable referenc…