68 citations · 142 across the 5 of their papers we have counts for
5 papers
MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
Dávid Péter Kovács, J. Harry Moore, Nicholas J. Browning +8
Classical empirical force fields have dominated biomolecular simulation for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dy…
Fast evaluation of spherical harmonics with sphericart
Filippo Bigi, Guillaume Fraux, Nicholas J. Browning +1
Spherical harmonics provide a smooth, orthogonal, and symmetry-adapted basis to expand functions on a sphere, and they are used routinely in physical and theoretical chemistry as w…
GPU-Accelerated Approximate Kernel Method for Quantum Machine Learning
Nicholas J. Browning, Felix A. Faber, O. Anatole von Lilienfeld
Conventional kernel-based machine learning models for ab initio potential energy surfaces, while accurate and convenient in small data regimes, suffer immense computational cost as…
Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation
Jan Weinreich, Nicholas J. Browning, O. Anatole von Lilienfeld
Free energies govern the behavior of soft and liquid matter, and improving their predictions could have a large impact on the development of drugs, electrolytes or homogeneous cata…
Genetic optimization of training sets for improved machine learning models of molecular properties
Nicholas J. Browning, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld +1
The training of molecular models of quantum mechanical properties based on statistical machine learning requires large datasets which exemplify the map from chemical structure to m…