no code implementations • 2 Sep 2024 • Lixue Cheng, P. Bernát Szabó, Zeno Schätzle, Derk Kooi, Jonas Köhler, Klaas J. H. Giesbertz, Frank Noé, Jan Hermann, Paola Gori-Giorgi, Adam Foster

Variational ab-initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function.

no code implementations • 27 Aug 2024 • Paolo Andrea Erdman, Robert Czupryniak, Bibek Bhandari, Andrew N. Jordan, Frank Noé, Jens Eisert, Giacomo Guarnieri

Considering weak or projective quantum measurements, we explore different regimes based on the ordering between the thermalization, the measurement, and the unitary feedback timescales, finding different and highly non-intuitive, yet interpretable, strategies.

no code implementations • 20 Jun 2024 • Leon Klein, Frank Noé

The generation of equilibrium samples of molecular systems has been a long-standing problem in statistical physics.

1 code implementation • 18 Jun 2024 • Maximilian Schebek, Michele Invernizzi, Frank Noé, Jutta Rogal

The accurate prediction of phase diagrams is of central importance for both the fundamental understanding of materials as well as for technological applications in material sciences.

no code implementations • 23 May 2024 • Julian Cremer, Tuan Le, Frank Noé, Djork-Arné Clevert, Kristof T. Schütt

The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design.

2 code implementations • 8 Jan 2024 • Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola

Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around.

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.

2 code implementations • 8 Oct 2023 • Jason Yim, Andrew Campbell, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Regina Barzilay, Tommi Jaakkola, Frank Noé

We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching.

no code implementations • 29 Sep 2023 • Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof Schütt

To further strengthen the applicability of diffusion models to limited training data, we investigate the transferability of EQGAT-diff trained on the large PubChem3D dataset with implicit hydrogen atoms to target different data distributions.

no code implementations • 11 Sep 2023 • Hao Wu, Frank Noé

In this work, we introduce a flow based machine learning approach, called reaction coordinate (RC) flow, for discovery of low-dimensional kinetic models of molecular systems.

no code implementations • 8 Jun 2023 • Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu

In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems.

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 • NeurIPS 2023 • Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka

Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated with timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$).

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.

1 code implementation • 26 Jan 2023 • Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé

Normalizing flows (NF) are a class of powerful generative models that have gained popularity in recent years due to their ability to model complex distributions with high flexibility and expressiveness.

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 • 30 Sep 2022 • Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande, Frank Noé

Nanodiamonds have a wide range of applications including catalysis, sensing, tribology and biomedicine.

no code implementations • 26 Aug 2022 • Jan Hermann, James Spencer, Kenny Choo, Antonio Mezzacapo, W. M. C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé

Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also play an increasingly important role in scientific discovery.

1 code implementation • 10 Apr 2022 • Paolo Andrea Erdman, Frank Noé

We introduce a general model-free framework based on Reinforcement Learning to identify out-of-equilibrium thermodynamic cycles that are Pareto optimal trade-offs between power and efficiency for quantum heat engines and refrigerators.

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 • 17 Mar 2022 • Mike Entwistle, Zeno Schätzle, Paolo A. Erdman, Jan Hermann, Frank Noé

Obtaining accurate ground and low-lying excited states of electronic systems is crucial in a multitude of important applications.

no code implementations • 20 Feb 2022 • Tuan Le, Frank Noé, Djork-Arné Clevert

Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in drug discovery.

no code implementations • 15 Feb 2022 • Robin Winter, Marco Bertolini, Tuan Le, Frank Noé, Djork-Arné Clevert

In this work, we extend group invariant and equivariant representation learning to the field of unsupervised deep learning.

1 code implementation • 28 Oct 2021 • Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, Nathan Kutz, Steven L. Brunton, Frank Noé

Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics.

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.

1 code implementation • 30 Aug 2021 • Paolo Andrea Erdman, Frank Noé

The optimal control of open quantum systems is a challenging task but has a key role in improving existing quantum information processing technologies.

1 code implementation • 4 Aug 2021 • Andreas Mardt, Frank Noé

Recent advances in deep learning frameworks have established valuable tools for analyzing the long-timescale behavior of complex systems such as proteins.

no code implementations • 11 Jul 2021 • Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen, Mads-Peter V. Christiansen, Frank Noé, Bjørk Hammer

Chemical space is routinely explored by machine learning methods to discover interesting molecules, before time-consuming experimental synthesizing is attempted.

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 • NeurIPS 2021 • Robin Winter, Frank Noé, Djork-Arné Clevert

In this work we address this issue by proposing a permutation-invariant variational autoencoder for graph structured data.

no code implementations • 31 Mar 2021 • Stefan Klus, Patrick Gelß, Feliks Nüske, Frank Noé

We derive symmetric and antisymmetric kernels by symmetrizing and antisymmetrizing conventional kernels and analyze their properties.

1 code implementation • 30 Mar 2021 • Tuan Le, Marco Bertolini, Frank Noé, Djork-Arné Clevert

Despite recent advances in representation learning in hypercomplex (HC) space, this subject is still vastly unexplored in the context of graphs.

Ranked #16 on Graph Property Prediction on ogbg-molpcba

no code implementations • 5 Jan 2021 • Robin Winter, Frank Noé, Djork-Arné Clevert

In this work we introduce an Autoencoder for molecular conformations.

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 • 11 Oct 2020 • Zeno Schätzle, Jan Hermann, Frank Noé

Variational quantum Monte Carlo (QMC) is an ab-initio method for solving the electronic Schr\"odinger equation that is exact in principle, but limited by the flexibility of the available ansatzes in practice.

2 code implementations • Nature Chemistry 2020 • Jan Hermann, Zeno Schätzle, Frank Noé

The electronic Schrödinger equation can only be solved analytically for the hydrogen atom, and the numerically exact full configuration-interaction method is exponentially expensive in the number of electrons.

1 code implementation • 19 Aug 2020 • Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé

Equivariant neural networks (ENNs) are graph neural networks embedded in $\mathbb{R}^3$ and are well suited for predicting molecular properties.

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 • 3 Jun 2020 • Jonas Köhler, Leon Klein, Frank Noé

We provide a theoretical sufficient criterion showing that the distribution generated by \textit{equivariant} normalizing flows is invariant with respect to these symmetries by design.

no code implementations • 29 May 2020 • Margarita Kostré, Christof Schütte, Frank Noé, Mauricio J. del Razo

In this work, we develop modeling and numerical schemes for particle-based reaction-diffusion in an open setting, where the reservoirs are mediated by reaction-diffusion PDEs.

Quantitative Methods Chemical Physics Computational Physics 92C40, 92C45, 60J70, 60Gxx, 70Lxx

2 code implementations • NeurIPS 2020 • Hao Wu, Jonas Köhler, Frank Noé

The sampling of probability distributions specified up to a normalization constant is an important problem in both machine learning and statistical mechanics.

1 code implementation • 16 Dec 2019 • Andreas Mardt, Luca Pasquali, Frank Noé, Hao Wu

Here we develop theory and methods for deep learning Markov and Koopman models that can bear such physical constraints.

Computational Physics

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.

1 code implementation • 7 Oct 2019 • Moritz Hoffmann, Frank Noé

Generating point clouds, e. g., molecular structures, in arbitrary rotations, translations, and enumerations remains a challenging task.

no code implementations • 2 Oct 2019 • Jonas Köhler, Leon Klein, Frank Noé

Flows are exact-likelihood generative neural networks that transform samples from a simple prior distribution to the samples of the probability distribution of interest.

1 code implementation • 16 Sep 2019 • Jan Hermann, Zeno Schätzle, Frank Noé

The electronic Schr\"odinger equation describes fundamental properties of molecules and materials, but can only be solved analytically for the hydrogen atom.

1 code implementation • 6 Sep 2019 • Mohsen Sadeghi, Frank Noé

The great challenge with biological membrane systems is the wide range of scales involved, from nanometers and picoseconds for individual lipids, to the micrometers and beyond millisecond for cellular signalling processes.

Computational Physics Biological Physics Fluid Dynamics

no code implementations • 16 Apr 2019 • Stefan Klus, Brooke E. Husic, Mattes Mollenhauer, Frank Noé

In particular, we show that kernel canonical correlation analysis (CCA) can be interpreted in terms of kernel transfer operators and that it can be obtained by optimizing the variational approach for Markov processes (VAMP) score.

no code implementations • 18 Dec 2018 • Frank Noé

Molecular Dynamics (MD) simulation is widely used to analyze the properties of molecules and materials.

2 code implementations • 4 Dec 2018 • Frank Noé, Simon Olsson, Jonas Köhler, Hao Wu

Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge.

1 code implementation • 29 Nov 2018 • Giovanni Pinamonti, Fabian Paul, Frank Noé, Alex Rodriguez, Giovanni Bussi

We here use molecular dynamics simulations and Markov state models to characterize the kinetics of RNA fraying and its sequence and direction dependence.

Computational Physics Statistical Mechanics Biological Physics Chemical Physics Biomolecules

no code implementations • 28 Nov 2018 • Martin K. Scherer, Brooke E. Husic, Moritz Hoffmann, Fabian Paul, Hao Wu, Frank Noé

The modeling of atomistic biomolecular simulations using kinetic models such as Markov state models (MSMs) has had many notable algorithmic advances in recent years.

no code implementations • 30 Oct 2017 • Christoph Wehmeyer, Frank Noé

Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data.

1 code implementation • 21 Oct 2017 • Luigi Sbailò, Frank Noé

The algorithm is shown to be more efficient than brute-force Brownian dynamics simulations up to a molar concentration of $10^{3}\mu M$ and is up to an order of magnitude more efficient compared with previous MD-GFRD schemes.

Chemical Physics Computational Physics

1 code implementation • 16 Oct 2017 • Andreas Mardt, Luca Pasquali, Hao Wu, Frank Noé

There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations.

no code implementations • 14 Jul 2017 • Hao Wu, Frank Noé

This leads to the definition of a family of score functions called VAMP-r which can be calculated from data, and can be employed to optimize a Markovian model.

no code implementations • 20 Oct 2016 • Hao Wu, Feliks Nüske, Fabian Paul, Stefan Klus, Peter Koltai, Frank Noé

Recently, a powerful generalization of MSMs has been introduced, the variational approach (VA) of molecular kinetics and its special case the time-lagged independent component analysis (TICA), which allow us to approximate slow collective variables and molecular kinetics by linear combinations of smooth basis functions or order parameters.

no code implementations • NeurIPS 2016 • Hao Wu, Frank Noé

Observable operator models (OOMs) and related models are one of the most important and powerful tools for modeling and analyzing stochastic systems.

no code implementations • 6 Aug 2011 • John D. Chodera, Phillip Elms, Frank Noé, Bettina Keller, Christian M. Kaiser, Aaron Ewall-Wice, Susan Marqusee, Carlos Bustamante, Nina Singhal Hinrichs

Single-molecule force spectroscopy has proven to be a powerful tool for studying the kinetic behavior of biomolecules.

Statistical Mechanics Biological Physics Biomolecules

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