Coarse graining molecular dynamics with graph neural networks

Machine Learning for Molecular Simulation

(2020) Frank Noé et al.   Annual Review of Physical Chemistry

Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach

(2020) Jiang Wang et al.   JOURNAL OF CHEMICAL PHYSICS

Array programming with NumPy

(2020) Charles R. Harris et al.   NATURE

Variational selection of features for molecular kinetics

(2019) Martin K. Scherer et al.   JOURNAL OF CHEMICAL PHYSICS

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

(2019) Jiang Wang et al.   ACS Central Science

Coarse-graining molecular systems by spectral matching

(2019) Feliks Nüske et al.   JOURNAL OF CHEMICAL PHYSICS

Coarse-graining auto-encoders for molecular dynamics

(2019) Wujie Wang et al.   npj Computational Materials

Quantifying Configuration-Sampling Error in Langevin Simulations of Complex Molecular Systems

(2018) Josh Fass et al.   Entropy

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

(2018) Tristan Bereau et al.   JOURNAL OF CHEMICAL PHYSICS

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

(2018) Thuong T. Nguyen et al.   JOURNAL OF CHEMICAL PHYSICS

Less is more: Sampling chemical space with active learning

(2018) Justin S. Smith et al.   JOURNAL OF CHEMICAL PHYSICS

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

(2018) Giulio Imbalzano et al.   JOURNAL OF CHEMICAL PHYSICS

DeePCG: Constructing coarse-grained models via deep neural networks

(2018) Linfeng Zhang et al.   JOURNAL OF CHEMICAL PHYSICS

Markov State Models: From an Art to a Science

(2018) Brooke E. Husic et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

(2018) Andrea Grisafi et al.   PHYSICAL REVIEW LETTERS

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Automated design of collective variables using supervised machine learning

(2018) Mohammad M. Sultan et al.   JOURNAL OF CHEMICAL PHYSICS

Accuracy, Transferability, and Efficiency of Coarse-Grained Models of Molecular Liquids

(2018) M. G. Guenza et al.   JOURNAL OF PHYSICAL CHEMISTRY B

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

(2018) K. T. Schütt et al.   Journal of Chemical Theory and Computation

Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network

(2018) Hao Wang et al.   Journal of Chemical Theory and Computation

PotentialNet for Molecular Property Prediction

(2018) Evan N. Feinberg et al.   ACS Central Science

Modeling the mechanism of CLN025 beta-hairpin formation

(2017) Keri A. McKiernan et al.   JOURNAL OF CHEMICAL PHYSICS

A Data-Driven Perspective on the Hierarchical Assembly of Molecular Structures

(2017) Lorenzo Boninsegna et al.   Journal of Chemical Theory and Computation

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

(2017) S. T. John et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling

(2017) Nuria Plattner et al.   Nature Chemistry

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

(2017) Peter Eastman et al.   PLoS Computational Biology

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Coarse-Grained Protein Models and Their Applications

(2016) Sebastian Kmiecik et al.   CHEMICAL REVIEWS

Optimized parameter selection reveals trends in Markov state models for protein folding

(2016) Brooke E. Husic et al.   JOURNAL OF CHEMICAL PHYSICS

Molecular graph convolutions: moving beyond fingerprints

(2016) Steven Kearnes et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories

(2015) Robert T. McGibbon et al.   BIOPHYSICAL JOURNAL

PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models

(2015) Martin K. Scherer et al.   Journal of Chemical Theory and Computation

Perspective: Coarse-grained models for biomolecular systems

(2013) W. G. Noid   JOURNAL OF CHEMICAL PHYSICS

Identification of slow molecular order parameters for Markov model construction

(2013) Guillermo Pérez-Hernández et al.   JOURNAL OF CHEMICAL PHYSICS

Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9

(2013) Christian R. Schwantes et al.   Journal of Chemical Theory and Computation

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

To milliseconds and beyond: challenges in the simulation of protein folding

(2012) Thomas J Lane et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

AWSEM-MD: Protein Structure Prediction Using Coarse-Grained Physical Potentials and Bioinformatically Based Local Structure Biasing

(2012) Aram Davtyan et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Simple few-state models reveal hidden complexity in protein folding

(2012) K. A. Beauchamp et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Coarse-graining entropy, forces, and structures

(2011) Joseph F. Rudzinski et al.   JOURNAL OF CHEMICAL PHYSICS

Markov models of molecular kinetics: Generation and validation

(2011) Jan-Hendrik Prinz et al.   JOURNAL OF CHEMICAL PHYSICS

First-principle approach to rescale the dynamics of simulated coarse-grained macromolecular liquids

(2011) I. Lyubimov et al.   PHYSICAL REVIEW E

How Fast-Folding Proteins Fold

(2011) K. Lindorff-Larsen et al.   SCIENCE

High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing

(2010) I. Buch et al.   Journal of Chemical Information and Modeling

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Macromolecular Modeling with Rosetta

(2008) Rhiju Das et al.   Annual Review of Biochemistry

Anton, a special-purpose machine for molecular dynamics simulation

(2008) David E. Shaw et al.   COMMUNICATIONS OF THE ACM

The multiscale coarse-graining method. II. Numerical implementation for coarse-grained molecular models

(2008) W. G. Noid et al.   JOURNAL OF CHEMICAL PHYSICS

The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models

(2008) W. G. Noid et al.   JOURNAL OF CHEMICAL PHYSICS

The relative entropy is fundamental to multiscale and inverse thermodynamic problems

(2008) M. Scott Shell   JOURNAL OF CHEMICAL PHYSICS

The MARTINI Coarse-Grained Force Field: Extension to Proteins

(2008) Luca Monticelli et al.   Journal of Chemical Theory and Computation

Coarse Master Equations for Peptide Folding Dynamics†

(2008) Nicolae-Viorel Buchete et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Crystal Structure of a Ten-Amino Acid Protein

(2008) Shinya Honda et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY