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Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence
Stefan Blücher, Klaus-Robert Müller, Stefan Chmiela
Kernel machines have sustained continuous progress in the field of quantum chemistry. In particular, they have proven to be successful in the low-data regime of force field reconst…
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…
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
Niklas Frederik Schmitz, Klaus-Robert Müller, Stefan Chmiela
Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive. Here, machine learning (ML) models can help to be data…