5 papers
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
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…
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…
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Peter Eastman, Raimondas Galvelis, Raúl P. Peláez +22
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use…