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 • 4 Oct 2023 • Peter Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio, Anton Gorenko, Michael M. Henry, Frank Hu, Jing Huang, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João PGLM Rodrigues, Jaime Rodriguez-Guerra, Andrew C. Simmonett, Sukrit Singh, Jason Swails, Philip Turner, Yuanqing Wang, Ivy Zhang, John D. Chodera, Gianni de Fabritiis, Thomas E. Markland
Machine learning plays an important and growing role in molecular simulation.
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.
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 • NeurIPS 2021 • Jonas Köhler, Andreas Krämer, Frank Noé
In this work, we introduce a class of smooth mixture transformations working on both compact intervals and hypertori.
2 code implementations • 24 Jun 2021 • Mario Christopher Bedrunka, Dominik Wilde, Martin Kliemank, Dirk Reith, Holger Foysi, Andreas Krämer
Lettuce enables GPU accelerated calculations with minimal source code, facilitates rapid prototyping of LBM models, and enables integrating LBM simulations with PyTorch's deep learning and automatic differentiation facility.
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.
no code implementations • 11 Jan 2021 • Dominik Wilde, Andreas Krämer, Mario Bedrunka, Dirk Reith, Holger Foysi
Off-lattice Boltzmann methods increase the flexibility and applicability of lattice Boltzmann methods by decoupling the discretizations of time, space, and particle velocities.
Computational Physics Fluid Dynamics
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.
no code implementations • 10 Dec 2020 • Dominik Wilde, Andreas Krämer, Dirk Reith, Holger Foysi
Turbulent compressible flows are traditionally simulated using explicit time integrators applied to discretized versions of the Navier-Stokes equations.
Computational Physics Statistical Mechanics Fluid Dynamics
1 code implementation • 12 Nov 2020 • Andreas Krämer
Cell type-specific gene expression patterns are represented as memory states of a Hopfield neural network model.
no code implementations • 14 Oct 2020 • Andreas Krämer, Jonas Köhler, Frank Noé
Many types of neural network layers rely on matrix properties such as invertibility or orthogonality.
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.