no code implementations • 27 Oct 2023 • Nicholas E. Charron, Felix Musil, Andrea Guljas, Yaoyi Chen, Klara Bonneau, Aldo S. Pasos-Trejo, Jacopo Venturin, Daria Gusew, Iryna Zaporozhets, Andreas Krämer, Clark Templeton, Atharva Kelkar, Aleksander E. P. Durumeric, Simon Olsson, Adrià Pérez, Maciej Majewski, Brooke E. Husic, Ankit Patel, Gianni de Fabritiis, Frank Noé, Cecilia Clementi
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost.
no code implementations • 14 Feb 2023 • Andreas Krämer, Aleksander P. Durumeric, Nicholas E. Charron, Yaoyi Chen, Cecilia Clementi, Frank Noé
A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average.
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.
no code implementations • 14 Jun 2021 • Yaoyi Chen, Andreas Krämer, Nicholas E. Charron, Brooke E. Husic, Cecilia Clementi, Frank Noé
Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data.
1 code implementation • 22 Jul 2020 • Brooke E. Husic, Nicholas E. Charron, Dominik Lemm, Jiang Wang, Adrià Pérez, Maciej Majewski, Andreas Krämer, Yaoyi Chen, Simon Olsson, Gianni de Fabritiis, Frank Noé, Cecilia Clementi
5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space.
no code implementations • 4 Dec 2018 • Jiang Wang, Simon Olsson, Christoph Wehmeyer, Adria Perez, Nicholas E. Charron, Gianni de Fabritiis, Frank Noe, Cecilia Clementi
We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface.