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09/11/2023
Reaction coordinate flows for model reduction of molecular kinetics
In this work, we introduce a flow based machine learning approach, calle...
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06/26/2023
Equivariant flow matching
Normalizing flows are a class of deep generative models that are especia...
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06/08/2023
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
Advances in deep learning have greatly improved structure prediction of ...
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02/14/2023
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics
Machine-learned coarse-grained (CG) models have the potential for simula...
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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...
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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...
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12/14/2022
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
A generalized understanding of protein dynamics is an unsolved scientifi...
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08/26/2022
Ab-initio quantum chemistry with neural-network wavefunctions
Machine learning and specifically deep-learning methods have outperforme...
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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...
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03/17/2022
Electronic excited states in deep variational Monte Carlo
Obtaining accurate ground and low-lying excited states of electronic sys...
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02/20/2022
Equivariant Graph Attention Networks for Molecular Property Prediction
Learning and reasoning about 3D molecular structures with varying size i...
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02/15/2022
Unsupervised Learning of Group Invariant and Equivariant Representations
Equivariant neural networks, whose hidden features transform according t...
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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...
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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...
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06/14/2021
Machine Learning Implicit Solvation for Molecular Dynamics
Accurate modeling of the solvent environment for biological molecules is...
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04/20/2021
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
Recently, there has been great success in applying deep neural networks ...
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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...
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03/30/2021
Parameterized Hypercomplex Graph Neural Networks for Graph Classification
Despite recent advances in representation learning in hypercomplex (HC) ...
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01/05/2021
Auto-Encoding Molecular Conformations
In this work we introduce an Autoencoder for molecular conformations. Ou...
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12/22/2020
TorchMD: A deep learning framework for molecular simulations
Molecular dynamics simulations provide a mechanistic description of mole...
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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...
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08/19/2020
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties
Equivariant neural networks (ENNs) are graph neural networks embedded in...
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07/22/2020
Coarse Graining Molecular Dynamics with Graph Neural Networks
Coarse graining enables the investigation of molecular dynamics for larg...
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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...
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11/22/2019
Machine learning for protein folding and dynamics
Many aspects of the study of protein folding and dynamics have been affe...
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11/07/2019
Machine learning for molecular simulation
Machine learning (ML) is transforming all areas of science. The complex ...
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10/07/2019
Generating valid Euclidean distance matrices
Generating point clouds, e.g., molecular structures, in arbitrary rotati...
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10/02/2019
Equivariant Flows: sampling configurations for multi-body systems with symmetric energies
Flows are exact-likelihood generative neural networks that transform sam...
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09/16/2019
Deep neural network solution of the electronic Schrƶdinger equation
The electronic Schrƶdinger equation describes fundamental properties of ...
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12/18/2018
Machine Learning for Molecular Dynamics on Long Timescales
Molecular Dynamics (MD) simulation is widely used to analyze the propert...
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12/04/2018
Machine Learning of coarse-grained Molecular Dynamics Force Fields
Atomistic or ab-initio molecular dynamics simulations are widely used to...
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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...
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11/28/2018
Variational Selection of Features for Molecular Kinetics
The modeling of atomistic biomolecular simulations using kinetic models ...
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05/19/2018
Deep Generative Markov State Models
We propose a deep generative Markov State Model (DeepGenMSM) learning fr...
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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 ...
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10/16/2017
VAMPnets: Deep learning of molecular kinetics
Here we develop a deep learning framework for molecular kinetics from mo...
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07/14/2017
Variational approach for learning Markov processes from time series data
Inference, prediction and control of complex dynamical systems from time...
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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...
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09/04/2016