TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations

1 code implementation • 27 Feb 2024 • Raul P. Pelaez, Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter Eastman, Stefan Doerr, Philipp Thölke, Thomas E. Markland, Gianni de Fabritiis

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge.

Computational Efficiency

Neuro-GPT: Towards A Foundation Model for EEG

1 code implementation • 7 Nov 2023 • Wenhui Cui, Woojae Jeong, Philipp Thölke, Takfarinas Medani, Karim Jerbi, Anand A. Joshi, Richard M. Leahy

To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model.

EEG Motor Imagery

Machine Learning Coarse-Grained Potentials of Protein Thermodynamics

2 code implementations • 14 Dec 2022 • Maciej Majewski, Adrià Pérez, Philipp Thölke, Stefan Doerr, Nicholas E. Charron, Toni Giorgino, Brooke E. Husic, Cecilia Clementi, Frank Noé, Gianni de Fabritiis

The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems.

TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

1 code implementation • 5 Feb 2022 • Philipp Thölke, Gianni de Fabritiis

The prediction of quantum mechanical properties is historically plagued by a trade-off between accuracy and speed.

Computational Efficiency

Equivariant Transformers for Neural Network based Molecular Potentials

no code implementations • ICLR 2022 • Philipp Thölke, Gianni de Fabritiis

The prediction of quantum mechanical properties is historically plagued by a trade-off between accuracy and speed.

Computational Efficiency