2.3k citations · 16.8k across the 81 of their papers we have counts for
8 papers · 1 filter
Optimized Quantum Drude Oscillators for Atomic and Molecular Response Properties
Szabolcs Góger, Almaz Khabibrakhmanov, Ornella Vaccarelli +2
The quantum Drude oscillator (QDO) is an efficient yet accurate coarse-grained approach that has been widely used to model electronic and optical response properties of atoms and m…
Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective
Alice E. A. Allen, Alexandre Tkatchenko
Learning from data has led to substantial advances in a multitude of disciplines, including text and multimedia search, speech recognition, and autonomous-vehicle navigation. Can m…
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…
The three-center two-positron bond
Jorge Charry, Félix Moncada, Matteo Barborini +4
Computational studies have shown that one or more positrons can stabilize two repelling atomic anions through the formation of two-center positronic bonds. In the present work, we…
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Oliver T. Unke, Martin Stöhr, Stefan Ganscha +8
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical ca…