A generalized deep learning approach for local structure identification in molecular simulations
暂无分享,去创建一个
Melissa C. Smith | M. C. Smith | Sapna Sarupria | Ryan S. DeFever | Colin Targonski | Steven W. Hall | Sapna Sarupria | Colin Targonski | Steven W Hall
[1] Matteo Salvalaglio,et al. DeepIce: A Deep Neural Network Approach To Identify Ice and Water Molecules , 2019, J. Chem. Inf. Model..
[2] Yuqing Qiu,et al. Ice Nucleation Efficiency of Hydroxylated Organic Surfaces Is Controlled by Their Structural Fluctuations and Mismatch to Ice. , 2017, Journal of the American Chemical Society.
[3] Wei Chen,et al. Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design. , 2018, The Journal of chemical physics.
[4] Andrew H. Nguyen,et al. Role of stacking disorder in ice nucleation , 2017, Nature.
[5] David Chandler,et al. Premelting, fluctuations, and coarse-graining of water-ice interfaces. , 2014, The Journal of chemical physics.
[6] Shekhar Garde,et al. Efficient method to characterize the context-dependent hydrophobicity of proteins. , 2014, The journal of physical chemistry. B.
[7] Nicholas Rego,et al. Hydrophobicity of proteins and nanostructured solutes is governed by topographical and chemical context , 2017, Proceedings of the National Academy of Sciences.
[8] Christoph Dellago,et al. Neural networks for local structure detection in polymorphic systems. , 2013, The Journal of chemical physics.
[9] Chris Benmore,et al. Machine learning coarse grained models for water , 2019, Nature Communications.
[10] P. Steinhardt,et al. Bond-orientational order in liquids and glasses , 1983 .
[11] Fabio Pietrucci,et al. Pre-critical fluctuations and what they disclose about heterogeneous crystal nucleation , 2017, Nature Communications.
[12] Sapna Sarupria,et al. The surface charge distribution affects the ice nucleating efficiency of silver iodide. , 2016, The Journal of chemical physics.
[13] Sapna Sarupria,et al. Adsorption of amino acids on graphene: assessment of current force fields. , 2019, Soft matter.
[14] Valeria Molinero,et al. Why Is Gyroid More Difficult to Nucleate from Disordered Liquids than Lamellar and Hexagonal Mesophases? , 2018, The journal of physical chemistry. B.
[15] Christoph Dellago,et al. Accurate determination of crystal structures based on averaged local bond order parameters. , 2008, The Journal of chemical physics.
[16] Christoph Dellago,et al. Reaction coordinates for the crystal nucleation of colloidal suspensions extracted from the reweighted path ensemble. , 2011, The Journal of chemical physics.
[17] Pratyush Tiwary,et al. Reweighted autoencoded variational Bayes for enhanced sampling (RAVE). , 2018, The Journal of chemical physics.
[18] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[19] Klaus-Robert Müller,et al. Machine learning of accurate energy-conserving molecular force fields , 2016, Science Advances.
[20] Jennifer M. Rieser,et al. Identifying structural flow defects in disordered solids using machine-learning methods. , 2014, Physical review letters.
[21] Ann B. Lee,et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[22] Liam C. Jacobson,et al. Order parameters for the multistep crystallization of clathrate hydrates. , 2011, The Journal of chemical physics.
[23] Amish J. Patel,et al. Characterizing Solvent Density Fluctuations in Dynamical Observation Volumes. , 2018, The journal of physical chemistry. B.
[24] Graeme Ackland,et al. Applications of local crystal structure measures in experiment and simulation , 2006 .
[25] Ryan S. DeFever,et al. Nucleation mechanism of clathrate hydrates of water-soluble guest molecules. , 2017, The Journal of chemical physics.
[26] Fernando A. Escobedo,et al. Developing Local Order Parameters for Order–Disorder Transitions From Particles to Block Copolymers: Methodological Framework , 2018, Macromolecules.
[27] Fernando A. Escobedo,et al. Developing Local Order Parameters for Order–Disorder Transitions From Particles to Block Copolymers: Application to Macromolecular Systems , 2018, Macromolecules.
[28] Aaron R Dinner,et al. Automatic method for identifying reaction coordinates in complex systems. , 2005, The journal of physical chemistry. B.
[29] Ting Xu,et al. Self-assembly and applications of anisotropic nanomaterials: A review , 2015 .
[30] T Kretz,et al. Machine-learning approach for local classification of crystalline structures in multiphase systems. , 2017, Physical review. E.
[31] C. Vega,et al. Seeding approach to crystal nucleation. , 2016, The Journal of chemical physics.
[32] R. Doolittle,et al. A simple method for displaying the hydropathic character of a protein. , 1982, Journal of molecular biology.
[33] Peter J Rossky,et al. A simple atomic-level hydrophobicity scale reveals protein interfacial structure. , 2014, Journal of molecular biology.
[34] Raffaela Cabriolu,et al. Ice nucleation on carbon surface supports the classical theory for heterogeneous nucleation. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.
[35] Andrew L. Ferguson,et al. Machine learning for autonomous crystal structure identification. , 2017, Soft matter.
[36] Ioannis G. Kevrekidis,et al. Nonlinear dimensionality reduction in molecular simulation: The diffusion map approach , 2011 .
[37] M. Engel,et al. Self-Assembly of Colloidal Nanocrystals: From Intricate Structures to Functional Materials. , 2016, Chemical reviews.
[38] Sapna Sarupria,et al. Heterogeneous Ice Nucleation: Interplay of Surface Properties and Their Impact on Water Orientations. , 2017, Langmuir : the ACS journal of surfaces and colloids.
[39] Pablo G Debenedetti,et al. Direct calculation of ice homogeneous nucleation rate for a molecular model of water , 2015, Proceedings of the National Academy of Sciences.
[40] H. C. Andersen,et al. Molecular dynamics study of melting and freezing of small Lennard-Jones clusters , 1987 .
[41] Michele Parrinello,et al. Predicting polymorphism in molecular crystals using orientational entropy , 2018, Proceedings of the National Academy of Sciences.
[42] David Quigley,et al. NaCl nucleation from brine in seeded simulations: Sources of uncertainty in rate estimates. , 2018, The Journal of chemical physics.
[43] Brian C. Barnes,et al. Two-component order parameter for quantifying clathrate hydrate nucleation and growth. , 2014, The Journal of chemical physics.
[44] Samuel S. Schoenholz,et al. Machine learning determination of atomic dynamics at grain boundaries , 2018, Proceedings of the National Academy of Sciences.
[45] J. Doye,et al. Local order parameters for use in driving homogeneous ice nucleation with all-atom models of water. , 2012, The Journal of chemical physics.
[46] Sapna Sarupria,et al. Molecular dynamics study of carbon dioxide hydrate dissociation. , 2011, The journal of physical chemistry. A.
[47] Felix Kling,et al. Structure and Dynamics of the Quasi-Liquid Layer at the Surface of Ice from Molecular Simulations , 2018, The Journal of Physical Chemistry C.
[48] David Chandler,et al. Quantifying Density Fluctuations in Volumes of All Shapes and Sizes Using Indirect Umbrella Sampling , 2011, Journal of statistical physics.
[49] A. Michaelides,et al. Crystal Nucleation in Liquids: Open Questions and Future Challenges in Molecular Dynamics Simulations , 2016, Chemical reviews.
[50] D. Wolf,et al. Deformation-mechanism map for nanocrystalline metals by molecular-dynamics simulation , 2004, Nature materials.
[51] Valeria Molinero,et al. Self-Assembly of Mesophases from Nanoparticles. , 2017, The journal of physical chemistry letters.
[52] Athanassios Z Panagiotopoulos,et al. Automated crystal characterization with a fast neighborhood graph analysis method. , 2018, Soft matter.
[53] P. B. Shepson,et al. Molecular dynamics simulations of ice growth from supercooled water , 2005 .
[54] Sharon C Glotzer,et al. Machine learning for crystal identification and discovery , 2017, 1710.09861.
[55] J. Behler. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. , 2011, Physical chemistry chemical physics : PCCP.
[56] Christoph Dellago,et al. Kinetic pathways of ion pair dissociation in water , 1999 .
[57] Adam P Willard,et al. Characterizing Hydration Properties Based on the Orientational Structure of Interfacial Water Molecules. , 2018, Journal of chemical theory and computation.
[58] S. Garde,et al. Mapping hydrophobicity at the nanoscale: applications to heterogeneous surfaces and proteins. , 2010, Faraday discussions.
[59] S. Schmidt,et al. Robust structural identification via polyhedral template matching , 2016, 1603.05143.
[60] Andrew H. Nguyen,et al. Identification of Clathrate Hydrates, Hexagonal Ice, Cubic Ice, and Liquid Water in Simulations: the CHILL+ Algorithm. , 2015, The journal of physical chemistry. B.
[61] Michele Parrinello,et al. Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.