1 citations · 1 across the 7 of their papers we have counts for
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MLIPAudit: A benchmarking tool for Machine Learned Interatomic Potentials
Leon Wehrhan, Lucien Walewski, Marie Bluntzer +4
Machine-learned interatomic potentials (MLIPs) promise to significantly advance atomistic simulations by delivering quantum-level accuracy for large molecular systems at a fraction…
Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Christoph Brunken, Olivier Peltre, Heloise Chomet +11
Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of…
Universally applicable and tunable graph-based coarse-graining for Machine learning force fields
Christoph Brunken, Sebastien Boyer, Mustafa Omar +7
Coarse-grained (CG) force field methods for molecular systems are a crucial tool to simulate large biological macromolecules and are therefore essential for characterisations of bi…