1 citations · 2 across the 2 of their papers we have counts for
3 papers
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…
Unified Framework for Matchgate Classical Shadows
Valentin Heyraud, Héloise Chomet, Jules Tilly
Estimating quantum fermionic properties is a computationally difficult yet crucial task for the study of electronic systems. Recent developments have begun to address this challeng…