b'Frank No\xc3\xa9'

research

∙ 09/11/2023

Reaction coordinate flows for model reduction of molecular kinetics

In this work, we introduce a flow based machine learning approach, calle...

0 Hao Wu, et al. ∙

research

∙ 06/26/2023

Equivariant flow matching

Normalizing flows are a class of deep generative models that are especia...

0 Leon Klein, et al. ∙

research

∙ 06/08/2023

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

Advances in deep learning have greatly improved structure prediction of ...

0 Shuxin Zheng, et al. ∙

research

∙ 02/14/2023

Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics

Machine-learned coarse-grained (CG) models have the potential for simula...

0 Andreas Krämer, et al. ∙

research

∙ 02/02/2023

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

Molecular dynamics (MD) simulation is a widely used technique to simulat...

0 Leon Klein, et al. ∙

research

∙ 02/01/2023

Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics

Coarse-grained (CG) molecular dynamics enables the study of biological p...

0 Marloes Arts, et al. ∙

research

∙ 12/14/2022

Machine Learning Coarse-Grained Potentials of Protein Thermodynamics

A generalized understanding of protein dynamics is an unsolved scientifi...

1 Maciej Majewski, et al. ∙

research

∙ 08/26/2022

Ab-initio quantum chemistry with neural-network wavefunctions

Machine learning and specifically deep-learning methods have outperforme...

18 Jan Hermann, et al. ∙

research

∙ 04/10/2022

Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning

The optimal control of non-equilibrium open quantum systems is a challen...

0 Paolo Andrea Erdman, et al. ∙

research

∙ 03/17/2022

Electronic excited states in deep variational Monte Carlo

Obtaining accurate ground and low-lying excited states of electronic sys...

0 Mike Entwistle, et al. ∙

research

∙ 02/20/2022

Equivariant Graph Attention Networks for Molecular Property Prediction

Learning and reasoning about 3D molecular structures with varying size i...

6 Tuan Le, et al. ∙

research

∙ 02/15/2022

Unsupervised Learning of Group Invariant and Equivariant Representations

Equivariant neural networks, whose hidden features transform according t...

9 Robin Winter, et al. ∙

research

∙ 10/28/2021

Deeptime: a Python library for machine learning dynamical models from time series data

Generation and analysis of time-series data is relevant to many quantita...

323 Moritz Hoffmann, et al. ∙

research

∙ 08/30/2021

Identifying optimal cycles in quantum thermal machines with reinforcement-learning

The optimal control of open quantum systems is a challenging task but ha...

0 Paolo Andrea Erdman, et al. ∙

research

∙ 06/14/2021

Machine Learning Implicit Solvation for Molecular Dynamics

Accurate modeling of the solvent environment for biological molecules is...

0 Yaoyi Chen, et al. ∙

research

∙ 04/20/2021

Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning

Recently, there has been great success in applying deep neural networks ...

0 Robin Winter, et al. ∙

research

∙ 03/31/2021

Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry

We derive symmetric and antisymmetric kernels by symmetrizing and antisy...

0 Stefan Klus, et al. ∙

research

∙ 03/30/2021

Parameterized Hypercomplex Graph Neural Networks for Graph Classification

Despite recent advances in representation learning in hypercomplex (HC) ...

0 Tuan Le, et al. ∙

research

∙ 01/05/2021

Auto-Encoding Molecular Conformations

In this work we introduce an Autoencoder for molecular conformations. Ou...

0 Robin Winter, et al. ∙

research

∙ 12/22/2020

TorchMD: A deep learning framework for molecular simulations

Molecular dynamics simulations provide a mechanistic description of mole...

0 Stefan Doerr, et al. ∙

research

∙ 10/11/2020

Convergence to the fixed-node limit in deep variational Monte Carlo

Variational quantum Monte Carlo (QMC) is an ab-initio method for solving...

7 Zeno Schätzle, et al. ∙

research

∙ 08/19/2020

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties

Equivariant neural networks (ENNs) are graph neural networks embedded in...

5 Benjamin Kurt Miller, et al. ∙

research

∙ 07/22/2020

Coarse Graining Molecular Dynamics with Graph Neural Networks

Coarse graining enables the investigation of molecular dynamics for larg...

14 Brooke E. Husic, et al. ∙

research

∙ 05/04/2020

Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach

Gradient-domain machine learning (GDML) is an accurate and efficient app...

62 Jiang Wang, et al. ∙

research

∙ 11/22/2019

Machine learning for protein folding and dynamics

Many aspects of the study of protein folding and dynamics have been affe...

0 Frank Noé, et al. ∙

research

∙ 11/07/2019

Machine learning for molecular simulation

Machine learning (ML) is transforming all areas of science. The complex ...

0 Frank Noé, et al. ∙

research

∙ 10/07/2019

Generating valid Euclidean distance matrices

Generating point clouds, e.g., molecular structures, in arbitrary rotati...

1 Moritz Hoffmann, et al. ∙

research

∙ 10/02/2019

Equivariant Flows: sampling configurations for multi-body systems with symmetric energies

Flows are exact-likelihood generative neural networks that transform sam...

0 Jonas Köhler, et al. ∙

research

∙ 09/16/2019

Deep neural network solution of the electronic Schrödinger equation

The electronic Schrödinger equation describes fundamental properties of ...

90 Zeno Schätzle, et al. ∙

research

∙ 12/18/2018

Machine Learning for Molecular Dynamics on Long Timescales

Molecular Dynamics (MD) simulation is widely used to analyze the propert...

0 Frank Noé, et al. ∙

research

∙ 12/04/2018

Machine Learning of coarse-grained Molecular Dynamics Force Fields

Atomistic or ab-initio molecular dynamics simulations are widely used to...

0 Jiang Wang, et al. ∙

research

∙ 12/04/2018

Boltzmann Generators - Sampling Equilibrium States of Many-Body Systems with Deep Learning

Computing equilibrium states in condensed-matter many-body systems, such...

10 Frank Noé, et al. ∙

research

∙ 11/28/2018

Variational Selection of Features for Molecular Kinetics

The modeling of atomistic biomolecular simulations using kinetic models ...

6 Martin K. Scherer, et al. ∙

research

∙ 05/19/2018

Deep Generative Markov State Models

We propose a deep generative Markov State Model (DeepGenMSM) learning fr...

0 Hao Wu, et al. ∙

research

∙ 10/30/2017

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

Inspired by the success of deep learning techniques in the physical and ...

0 Christoph Wehmeyer, et al. ∙

research

∙ 10/16/2017

VAMPnets: Deep learning of molecular kinetics

Here we develop a deep learning framework for molecular kinetics from mo...

0 Andreas Mardt, et al. ∙

research

∙ 07/14/2017

Variational approach for learning Markov processes from time series data

Inference, prediction and control of complex dynamical systems from time...

0 Hao Wu, et al. ∙

research

∙ 10/20/2016

Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations

Markov state models (MSMs) and Master equation models are popular approa...

0 Hao Wu, et al. ∙

research

∙ 09/04/2016

Spectral learning of dynamic systems from nonequilibrium data

Observable operator models (OOMs) and related models are one of the most...

0 Hao Wu, et al. ∙

Frank Noé

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