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20222026
most citedTowards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules

41 citations · 67 across the 7 of their papers we have counts for

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2 papers · 1 filter

physics.chem-ph2022★ 9 cited

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…

physics.chem-ph2022★ 41 cited

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…