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.
no code implementations • 1 Feb 2023 • Marloes Arts, Victor Garcia Satorras, Chin-wei Huang, Daniel Zuegner, Marco Federici, Cecilia Clementi, Frank Noé, Robert Pinsler, Rianne van den Berg
Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution.
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.
1 code implementation • 21 Mar 2022 • Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, Frank Noé
Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations.
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.
2 code implementations • 22 Dec 2020 • Stefan Doerr, Maciej Majewsk, Adrià Pérez, Andreas Krämer, Cecilia Clementi, Frank Noe, Toni Giorgino, Gianni de Fabritiis
Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials.
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 May 2020 • Jiang Wang, Stefan Chmiela, Klaus-Robert Müller, Frank Noè, Cecilia Clementi
Using ensemble learning and stratified sampling, we propose a 2-layer training scheme that enables GDML to learn an effective coarse-grained model.
no code implementations • 22 Nov 2019 • Frank Noé, Gianni De Fabritiis, Cecilia Clementi
Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning.
no code implementations • 7 Nov 2019 • Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi
Machine learning (ML) is transforming all areas of science.
no code implementations • 23 Sep 2019 • Stefan Klus, Feliks Nüske, Sebastian Peitz, Jan-Hendrik Niemann, Cecilia Clementi, Christof Schütte
We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition).
1 code implementation • 12 Aug 2019 • Feliks Nüske, Patrick Gelß, Stefan Klus, Cecilia Clementi
Recent years have seen rapid advances in the data-driven analysis of dynamical systems based on Koopman operator theory and related approaches.
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.
1 code implementation • 6 Dec 2017 • Lorenzo Boninsegna, Feliks Nüske, Cecilia Clementi
With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets.
Ranked #1 on Denoising on Darmstadt Noise Dataset