DeePCG: Constructing coarse-grained models via deep neural networks
Published 2018 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
DeePCG: Constructing coarse-grained models via deep neural networks
Authors
Keywords
-
Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 149, Issue 3, Pages 034101
Publisher
AIP Publishing
Online
2018-07-17
DOI
10.1063/1.5027645
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
- (2018) Han Wang et al. COMPUTER PHYSICS COMMUNICATIONS
- Reinforced dynamics for enhanced sampling in large atomic and molecular systems
- (2018) Linfeng Zhang et al. JOURNAL OF CHEMICAL PHYSICS
- Multiscale Simulation of Protein Hydration Using the SWINGER Dynamical Clustering Algorithm
- (2018) Julija Zavadlav et al. Journal of Chemical Theory and Computation
- Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
- (2018) Linfeng Zhang et al. PHYSICAL REVIEW LETTERS
- Extending pressure-matching to inhomogeneous systems via local-density potentials
- (2017) Michael R. DeLyser et al. JOURNAL OF CHEMICAL PHYSICS
- Extending the range and physical accuracy of coarse-grained models: Order parameter dependent interactions
- (2017) Jacob W. Wagner et al. JOURNAL OF CHEMICAL PHYSICS
- Neural Network Based Prediction of Conformational Free Energies - A New Route toward Coarse-Grained Simulation Models
- (2017) Tobias Lemke et al. Journal of Chemical Theory and Computation
- Neural Network and Nearest Neighbor Algorithms for Enhancing Sampling of Molecular Dynamics
- (2017) Raimondas Galvelis et al. Journal of Chemical Theory and Computation
- On the Density Dependence of the Integral Equation Coarse-Graining Effective Potential
- (2017) Mohammadhasan Dinpajooh et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials
- (2017) S. T. John et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces
- (2017) Elia Schneider et al. PHYSICAL REVIEW LETTERS
- Quantum-chemical insights from deep tensor neural networks
- (2017) Kristof T. Schütt et al. Nature Communications
- Machine learning of accurate energy-conserving molecular force fields
- (2017) Stefan Chmiela et al. Science Advances
- A coarse-grain force field for RDX: Density dependent and energy conserving
- (2016) Joshua D. Moore et al. JOURNAL OF CHEMICAL PHYSICS
- Coarse-grained models using local-density potentials optimized with the relative entropy: Application to implicit solvation
- (2016) Tanmoy Sanyal et al. JOURNAL OF CHEMICAL PHYSICS
- Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression
- (2016) Letif Mones et al. Journal of Chemical Theory and Computation
- The geometry of generalized force matching and related information metrics in coarse-graining of molecular systems
- (2015) Evangelia Kalligiannaki et al. JOURNAL OF CHEMICAL PHYSICS
- Exact dynamical coarse-graining without time-scale separation
- (2014) Jianfeng Lu et al. JOURNAL OF CHEMICAL PHYSICS
- The individual and collective effects of exact exchange and dispersion interactions on the ab initio structure of liquid water
- (2014) Robert A. DiStasio et al. JOURNAL OF CHEMICAL PHYSICS
- Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression
- (2014) Thomas Stecher et al. Journal of Chemical Theory and Computation
- The multiscale coarse-graining method. IX. A general method for construction of three body coarse-grained force fields
- (2012) Avisek Das et al. JOURNAL OF CHEMICAL PHYSICS
- Structural transformation in supercooled water controls the crystallization rate of ice
- (2011) Emily B. Moore et al. NATURE
- The multiscale coarse-graining method. VI. Implementation of three-body coarse-grained potentials
- (2010) Luca Larini et al. JOURNAL OF CHEMICAL PHYSICS
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Mori–Zwanzig formalism as a practical computational tool
- (2009) Carmen Hijón et al. FARADAY DISCUSSIONS
- Static and Dynamical Properties of Liquid Water from First Principles by a Novel Car−Parrinello-like Approach
- (2009) Thomas D. Kühne et al. Journal of Chemical Theory and Computation
- Formation of micelles in aqueous solutions of a room temperature ionic liquid: a study using coarse grained molecular dynamics
- (2009) B.L. Bhargava et al. MOLECULAR PHYSICS
- Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data
- (2009) Alexandre Tkatchenko et al. PHYSICAL REVIEW LETTERS
- 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
- Accurate determination of crystal structures based on averaged local bond order parameters
- (2008) Wolfgang Lechner et al. JOURNAL OF CHEMICAL PHYSICS
- Efficient and Direct Generation of Multidimensional Free Energy Surfaces via Adiabatic Dynamics without Coordinate Transformations
- (2008) Jerry B. Abrams et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Water Modeled As an Intermediate Element between Carbon and Silicon†
- (2008) Valeria Molinero et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Coarse-grained molecular modeling of non-ionic surfactant self-assembly
- (2008) Wataru Shinoda et al. Soft Matter
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started