58 citations · 133 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
Guillem Simeon, Gianni de Fabritiis
The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research. We introduce TensorNet, an innovative O(3)-equ…
Geometric Deep Learning for the Assessment of Thrombosis Risk in the Left Atrial Appendage
Xabier Morales, Jordi Mill, Guillem Simeon +4
The assessment of left atrial appendage (LAA) thrombogenesis has experienced major advances with the adoption of patient-specific computational fluid dynamics (CFD) simulations. No…