239 citations · 676 across the 20 of their papers we have counts for
4 papers · 1 filter
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
Antonio Mirarchi, Raul P. Pelaez, Guillem Simeon +1
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological process…
Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
Gianni De Fabritiis
Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. Here, I explore thei…
Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes
Guillem Simeon, Antonio Mirarchi, Raul P. Pelaez +2
Most state-of-the-art neural network potentials do not account for molecular attributes other than atomic numbers and positions, which limits its range of applicability by design.…
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
Raul P. Pelaez, Guillem Simeon, Raimondas Galvelis +6
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents subs…