Data-driven molecular modeling with the generalized Langevin equation
•Data-driven GLE approximation balances computational cost and accuracy.•Accuracy tunable by adjusting order of memory kernel approximation.•Approximate GLE predicts non-equilibrium properties, like autocorrelation, well. The complexity of molecular dynamics simulations necessitates dimension reduct...
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| Vydáno v: | Journal of computational physics Ročník 418; s. 109633 |
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01.10.2020
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| ISSN: | 0021-9991, 1090-2716 |
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| Abstract | •Data-driven GLE approximation balances computational cost and accuracy.•Accuracy tunable by adjusting order of memory kernel approximation.•Approximate GLE predicts non-equilibrium properties, like autocorrelation, well.
The complexity of molecular dynamics simulations necessitates dimension reduction and coarse-graining techniques to enable tractable computation. The generalized Langevin equation (GLE) describes coarse-grained dynamics in reduced dimensions. In spite of playing a crucial role in non-equilibrium dynamics, the memory kernel of the GLE is often ignored because it is difficult to characterize and expensive to solve. To address these issues, we construct a data-driven rational approximation to the GLE. Building upon previous work leveraging the GLE to simulate simple systems, we extend these results to more complex molecules, whose many degrees of freedom and complicated dynamics require approximation methods. We demonstrate the effectiveness of our approximation by testing it against exact methods and comparing observables such as autocorrelation and transition rates. |
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| AbstractList | The complexity of molecular dynamics simulations necessitates dimension reduction and coarse-graining techniques to enable tractable computation. The generalized Langevin equation (GLE) describes coarse-grained dynamics in reduced dimensions. In spite of playing a crucial role in non-equilibrium dynamics, the memory kernel of the GLE is often ignored because it is difficult to characterize and expensive to solve. To address these issues, we construct a data-driven rational approximation to the GLE. Building upon previous work leveraging the GLE to simulate simple systems, we extend these results to more complex molecules, whose many degrees of freedom and complicated dynamics require approximation methods. We demonstrate the effectiveness of our approximation by testing it against exact methods and comparing observables such as autocorrelation and transition rates. The complexity of molecular dynamics simulations necessitates dimension reduction and coarse-graining techniques to enable tractable computation. The generalized Langevin equation (GLE) describes coarse-grained dynamics in reduced dimensions. In spite of playing a crucial role in non-equilibrium dynamics, the memory kernel of the GLE is often ignored because it is difficult to characterize and expensive to solve. To address these issues, we construct a data-driven rational approximation to the GLE. Building upon previous work leveraging the GLE to simulate simple systems, we extend these results to more complex molecules, whose many degrees of freedom and complicated dynamics require approximation methods. We demonstrate the effectiveness of our approximation by testing it against exact methods and comparing observables such as autocorrelation and transition rates.The complexity of molecular dynamics simulations necessitates dimension reduction and coarse-graining techniques to enable tractable computation. The generalized Langevin equation (GLE) describes coarse-grained dynamics in reduced dimensions. In spite of playing a crucial role in non-equilibrium dynamics, the memory kernel of the GLE is often ignored because it is difficult to characterize and expensive to solve. To address these issues, we construct a data-driven rational approximation to the GLE. Building upon previous work leveraging the GLE to simulate simple systems, we extend these results to more complex molecules, whose many degrees of freedom and complicated dynamics require approximation methods. We demonstrate the effectiveness of our approximation by testing it against exact methods and comparing observables such as autocorrelation and transition rates. •Data-driven GLE approximation balances computational cost and accuracy.•Accuracy tunable by adjusting order of memory kernel approximation.•Approximate GLE predicts non-equilibrium properties, like autocorrelation, well. The complexity of molecular dynamics simulations necessitates dimension reduction and coarse-graining techniques to enable tractable computation. The generalized Langevin equation (GLE) describes coarse-grained dynamics in reduced dimensions. In spite of playing a crucial role in non-equilibrium dynamics, the memory kernel of the GLE is often ignored because it is difficult to characterize and expensive to solve. To address these issues, we construct a data-driven rational approximation to the GLE. Building upon previous work leveraging the GLE to simulate simple systems, we extend these results to more complex molecules, whose many degrees of freedom and complicated dynamics require approximation methods. We demonstrate the effectiveness of our approximation by testing it against exact methods and comparing observables such as autocorrelation and transition rates. |
| ArticleNumber | 109633 |
| Author | Grogan, Francesca Li, Xiantao Lei, Huan Baker, Nathan A. |
| AuthorAffiliation | a Pacific Northwest National Laboratory, Richland, WA 99352, United States d Department of Mathematics, Pennsylvania State University, State College, PA 16801, United States e Division of Applied Mathematics, Brown University, Providence, RI 02912, United States c Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, United States b Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, United States |
| AuthorAffiliation_xml | – name: d Department of Mathematics, Pennsylvania State University, State College, PA 16801, United States – name: e Division of Applied Mathematics, Brown University, Providence, RI 02912, United States – name: b Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, United States – name: a Pacific Northwest National Laboratory, Richland, WA 99352, United States – name: c Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, United States |
| Author_xml | – sequence: 1 givenname: Francesca surname: Grogan fullname: Grogan, Francesca email: francesca.grogan@pnnl.gov organization: Pacific Northwest National Laboratory, Richland, WA 99352, United States – sequence: 2 givenname: Huan surname: Lei fullname: Lei, Huan email: leihuan@msu.edu organization: Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, United States – sequence: 3 givenname: Xiantao surname: Li fullname: Li, Xiantao email: xli@math.psu.edu organization: Department of Mathematics, Pennsylvania State University, State College, PA 16801, United States – sequence: 4 givenname: Nathan A. orcidid: 0000-0002-5892-6506 surname: Baker fullname: Baker, Nathan A. email: nathan.baker@pnnl.gov organization: Pacific Northwest National Laboratory, Richland, WA 99352, United States |
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| Cites_doi | 10.1021/acs.jctc.7b00274 10.1021/acs.energyfuels.9b01321 10.1063/1.5055573 10.1103/PhysRevA.31.1695 10.1073/pnas.1609587113 10.1002/9780470142639.ch2 10.1039/C8SM01579A 10.1002/jcc.20035 10.1016/j.jcp.2018.06.047 10.1007/s10955-006-9165-0 10.1016/0010-4655(95)00042-E 10.1016/j.commatsci.2018.05.029 10.2140/camcos.2016.11.187 10.1137/140981587 10.1063/1.434249 10.1063/1.2199530 10.1002/nme.2892 10.1007/BF01008729 10.1063/1.449379 10.1143/PTP.33.423 10.1122/1.3675625 10.1063/1.434317 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H 10.1016/j.polymer.2007.07.007 10.1063/1.4892412 10.1073/pnas.0902633106 10.1088/1367-2630/16/5/053032 10.1007/s10955-020-02499-y 10.1093/comjnl/12.4.393 10.1088/0034-4885/29/1/306 10.1063/1.464397 10.1063/1.4967936 10.1016/j.cma.2019.03.014 10.1063/1.5003467 |
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| References | Lee, Ahn, Darve (br0300) 2019; 150 Lei, Li, Gao, Stinis, Baker (br0050) 2019; 350 Ma, Wang (br0260) 2018 Zwanzig (br0080) 1973; 9 Turq, Lantelme, Friedman (br0100) 1977; 66 Hudson, Li (br0350) 2018 Zhao, Han, Li, Zhu, Tao, Guo (br0060) 2019; 33 Adelman (br0090) 2007 Lei, Caswell, Karniadakis (br0190) 2010; 81 Kubo (br0230) 1966; 29 Linz (br0330) 1969; 12 Wang, Wolf, Caldwell, Kollman, Case (br0380) 2004; 25 Hoover (br0390) 1985; 31 Mori (br0070) 1965; 33 Guàrdia, Padró (br0180) 1985; 83 Córdoba, Indei, Schieber (br0110) 2012; 56 Razi, Narayan, Kirby, Bedrov (br0040) 2018; 152 Darve, Solomon, Kia (br0150) 2009; 106 Lei, Baker, Li (br0220) 2016; 113 Russo, Durán-Olivencia, Kevrekidis, Kalliadasis (br0270) 2019 Lange, Grubmüller (br0310) 2006; 124 Hess, Bekker, Berendsen, Fraaije (br0410) 1997; 18 Démery, Bénichou, Jacquin (br0120) 2014; 16 Chen, Li, Liu (br0320) 2014; 141 Lu, Lin, Chorin (br0250) 2016; 11 Zhu, Dominy, Venturi (br0210) 2018; 59 Lin, Lu (br0240) 2019 Zhu, Venturi (br0280) 2018; 372 Zhu, Venturi (br0290) 2020 Darden, York, Pedersen (br0400) 1993; 98 Gubskaya, Kholodovych, Knight, Kohn, Welsh (br0010) 2007; 48 Zhu, Venturi (br0360) 2020 Berendsen, van der Spoel, van Drunen (br0370) 1995; 91 Nance (br0020) 2015 Shugard, Tully, Nitzan (br0200) 1977; 66 Lei, Yang, Zheng, Lin, Baker (br0030) 2015; 13 Ariel, Vanden-Eijnden (br0140) 2007; 126 Davies (br0340) 2010 Ma, Li, Liu (br0420) 2016; 145 Jung, Hanke, Schmid (br0170) 2017; 13 Li (br0160) 2010; 83 Wu, Yu (br0130) 2018; 14 Lei (10.1016/j.jcp.2020.109633_br0220) 2016; 113 Lange (10.1016/j.jcp.2020.109633_br0310) 2006; 124 Razi (10.1016/j.jcp.2020.109633_br0040) 2018; 152 Hudson (10.1016/j.jcp.2020.109633_br0350) Wu (10.1016/j.jcp.2020.109633_br0130) 2018; 14 Linz (10.1016/j.jcp.2020.109633_br0330) 1969; 12 Lu (10.1016/j.jcp.2020.109633_br0250) 2016; 11 Zhu (10.1016/j.jcp.2020.109633_br0360) Gubskaya (10.1016/j.jcp.2020.109633_br0010) 2007; 48 Lei (10.1016/j.jcp.2020.109633_br0050) 2019; 350 Wang (10.1016/j.jcp.2020.109633_br0380) 2004; 25 Hoover (10.1016/j.jcp.2020.109633_br0390) 1985; 31 Shugard (10.1016/j.jcp.2020.109633_br0200) 1977; 66 Hess (10.1016/j.jcp.2020.109633_br0410) 1997; 18 Adelman (10.1016/j.jcp.2020.109633_br0090) 2007 Darve (10.1016/j.jcp.2020.109633_br0150) 2009; 106 Démery (10.1016/j.jcp.2020.109633_br0120) 2014; 16 Guàrdia (10.1016/j.jcp.2020.109633_br0180) 1985; 83 Córdoba (10.1016/j.jcp.2020.109633_br0110) 2012; 56 Zwanzig (10.1016/j.jcp.2020.109633_br0080) 1973; 9 Darden (10.1016/j.jcp.2020.109633_br0400) 1993; 98 Ma (10.1016/j.jcp.2020.109633_br0260) Davies (10.1016/j.jcp.2020.109633_br0340) 2010 Lee (10.1016/j.jcp.2020.109633_br0300) 2019; 150 Lin (10.1016/j.jcp.2020.109633_br0240) Zhu (10.1016/j.jcp.2020.109633_br0210) 2018; 59 Mori (10.1016/j.jcp.2020.109633_br0070) 1965; 33 Berendsen (10.1016/j.jcp.2020.109633_br0370) 1995; 91 Zhao (10.1016/j.jcp.2020.109633_br0060) 2019; 33 Li (10.1016/j.jcp.2020.109633_br0160) 2010; 83 Zhu (10.1016/j.jcp.2020.109633_br0280) 2018; 372 Lei (10.1016/j.jcp.2020.109633_br0030) 2015; 13 Jung (10.1016/j.jcp.2020.109633_br0170) 2017; 13 Ariel (10.1016/j.jcp.2020.109633_br0140) 2007; 126 Lei (10.1016/j.jcp.2020.109633_br0190) 2010; 81 Chen (10.1016/j.jcp.2020.109633_br0320) 2014; 141 Turq (10.1016/j.jcp.2020.109633_br0100) 1977; 66 Ma (10.1016/j.jcp.2020.109633_br0420) 2016; 145 Kubo (10.1016/j.jcp.2020.109633_br0230) 1966; 29 Zhu (10.1016/j.jcp.2020.109633_br0290) 2020 Nance (10.1016/j.jcp.2020.109633_br0020) 2015 Russo (10.1016/j.jcp.2020.109633_br0270) |
| References_xml | – volume: 126 start-page: 43 year: 2007 end-page: 73 ident: br0140 article-title: Testing transition state theory on Kac-Zwanzig model publication-title: J. Stat. Phys. – volume: 145 year: 2016 ident: br0420 article-title: The derivation and approximation of coarse-grained dynamics from Langevin dynamics publication-title: J. Chem. Phys. – volume: 56 start-page: 185 year: 2012 end-page: 212 ident: br0110 article-title: Elimination of inertia from a generalized Langevin equation: applications to microbead rheology modeling and data analysis publication-title: J. Rheol. – volume: 106 start-page: 10884 year: 2009 end-page: 10889 ident: br0150 article-title: Computing generalized Langevin equations and generalized Fokker-Planck equations publication-title: Proc. Natl. Acad. Sci. – volume: 150 year: 2019 ident: br0300 article-title: The multi-dimensional generalized Langevin equation for conformational motion of proteins publication-title: J. Chem. Phys. – volume: 13 start-page: 2481 year: 2017 end-page: 2488 ident: br0170 article-title: Iterative reconstruction of memory kernels publication-title: J. Chem. Theory Comput. – volume: 33 start-page: 423 year: 1965 end-page: 455 ident: br0070 article-title: Transport collective motion, and Brownian motion publication-title: Prog. Theor. Phys. – volume: 13 start-page: 1327 year: 2015 end-page: 1353 ident: br0030 article-title: Constructing surrogate models of complex systems with enhanced sparsity: quantifying the influence of conformational uncertainty in biomolecular solvation publication-title: SIAM Multiscale Model. Simul. – year: 2019 ident: br0270 article-title: Deep learning as closure for irreversible processes: a data-driven generalized Langevin equation – year: 2018 ident: br0260 article-title: Model reduction with memory and the machine learning of dynamical systems – start-page: 143 year: 2007 end-page: 253 ident: br0090 article-title: Generalized Langevin equations and many-body problems in chemical dynamics publication-title: Advances in Chemical Physics – year: 2015 ident: br0020 article-title: Investigating Molecular Dynamics with Sparse Grid Surrogate Models – volume: 83 start-page: 986 year: 2010 end-page: 997 ident: br0160 article-title: A coarse-grained molecular dynamics model for crystalline solids publication-title: Int. J. Numer. Methods Eng. – volume: 12 start-page: 393 year: 1969 end-page: 397 ident: br0330 article-title: Numerical methods for Volterra integral equations of the first kind publication-title: Comput. J. – volume: 29 start-page: 255 year: 1966 end-page: 284 ident: br0230 article-title: The fluctuation-dissipation theorem publication-title: Rep. Prog. Phys. – volume: 59 year: 2018 ident: br0210 article-title: On the estimation of the Mori-Zwanzig memory integral publication-title: J. Math. Phys. – volume: 113 start-page: 14183 year: 2016 end-page: 14188 ident: br0220 article-title: Data-driven parameterization of the generalized Langevin equation publication-title: Proc. Natl. Acad. Sci. – year: 2018 ident: br0350 article-title: Coarse-graining of overdamped Langevin dynamics via the Mori-Zwanzig formalism – volume: 33 start-page: 7176 year: 2019 end-page: 7187 ident: br0060 article-title: Comparison of RP-3 pyrolysis reactions between surrogates and 45-component model by ReaxFF molecular dynamics simulations publication-title: Energy Fuels – volume: 98 start-page: 10089 year: 1993 end-page: 10092 ident: br0400 article-title: Particle mesh Ewald: an publication-title: J. Chem. Phys. – volume: 48 start-page: 5788 year: 2007 end-page: 5801 ident: br0010 article-title: Prediction of fibrinogen adsorption for biodegradable polymers: integration of molecular dynamics and surrogate modeling publication-title: Polymer – volume: 91 start-page: 43 year: 1995 end-page: 56 ident: br0370 article-title: GROMACS: a message-passing parallel molecular dynamics implementation publication-title: Comput. Phys. Commun. – volume: 25 start-page: 1157 year: 2004 end-page: 1174 ident: br0380 article-title: Development and testing of a general AMBER force field publication-title: J. Comput. Chem. – volume: 9 start-page: 215 year: 1973 end-page: 220 ident: br0080 article-title: Nonlinear generalized Langevin equations publication-title: J. Stat. Phys. – volume: 124 year: 2006 ident: br0310 article-title: Collective Langevin dynamics of conformational motions in proteins publication-title: J. Chem. Phys. – volume: 350 start-page: 199 year: 2019 end-page: 227 ident: br0050 article-title: A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness publication-title: Comput. Methods Appl. Mech. Eng. – volume: 66 start-page: 2534 year: 1977 end-page: 2544 ident: br0200 article-title: Dynamics of gas-solid interactions: calculations of energy transfer and sticking publication-title: J. Chem. Phys. – volume: 31 start-page: 1695 year: 1985 end-page: 1697 ident: br0390 article-title: Canonical dynamics: equilibrium phase-space distributions publication-title: Phys. Rev. A – volume: 11 start-page: 187 year: 2016 end-page: 216 ident: br0250 article-title: Comparison of continuous and discrete-time data-based modeling for hypoelliptic systems publication-title: Commun. Appl. Math. Comput. Sci. – volume: 14 start-page: 9910 year: 2018 end-page: 9922 ident: br0130 article-title: Adhesion of a polymer-grafted nanoparticle to cells explored using generalized Langevin dynamics publication-title: Soft Matter – volume: 372 start-page: 694 year: 2018 end-page: 718 ident: br0280 article-title: Faber approximation of the Mori-Zwanzig equation publication-title: J. Comput. Phys. – volume: 83 start-page: 1917 year: 1985 end-page: 1920 ident: br0180 article-title: Generalized Langevin dynamics simulation of interacting particles publication-title: J. Chem. Phys. – volume: 66 start-page: 3039 year: 1977 end-page: 3044 ident: br0100 article-title: Brownian dynamics: its application to ionic solutions publication-title: J. Chem. Phys. – volume: 81 year: 2010 ident: br0190 article-title: Direct construction of mesoscopic models from microscopic simulations publication-title: Phys. Rev. E – volume: 16 year: 2014 ident: br0120 article-title: Generalized Langevin equations for a driven tracer in dense soft colloids: construction and applications publication-title: New J. Phys. – year: 2020 ident: br0290 article-title: Generalized Langevin equations for systems with local interactions publication-title: J. Stat. Phys. – volume: 152 start-page: 125 year: 2018 end-page: 133 ident: br0040 article-title: Fast predictive models based on multi-fidelity sampling of properties in molecular dynamics simulations publication-title: Comput. Mater. Sci. – year: 2019 ident: br0240 article-title: Data-driven model reduction, Wiener projections, and the Mori-Zwanzig formalism – volume: 141 year: 2014 ident: br0320 article-title: Computation of the memory functions in the generalized Langevin models for collective dynamics of macromolecules publication-title: J. Chem. Phys. – year: 2010 ident: br0340 article-title: Integral Transforms and Their Applications – year: 2020 ident: br0360 article-title: Hypoellipticity and the Mori-Zwanzig formulation of stochastic differential equations – volume: 18 year: 1997 ident: br0410 article-title: LINCS: a linear constraint solver for molecular simulations publication-title: J. Comput. Chem. – volume: 13 start-page: 2481 issue: 6 year: 2017 ident: 10.1016/j.jcp.2020.109633_br0170 article-title: Iterative reconstruction of memory kernels publication-title: J. Chem. Theory Comput. doi: 10.1021/acs.jctc.7b00274 – volume: 33 start-page: 7176 issue: 8 year: 2019 ident: 10.1016/j.jcp.2020.109633_br0060 article-title: Comparison of RP-3 pyrolysis reactions between surrogates and 45-component model by ReaxFF molecular dynamics simulations publication-title: Energy Fuels doi: 10.1021/acs.energyfuels.9b01321 – volume: 150 issue: 17 year: 2019 ident: 10.1016/j.jcp.2020.109633_br0300 article-title: The multi-dimensional generalized Langevin equation for conformational motion of proteins publication-title: J. Chem. Phys. doi: 10.1063/1.5055573 – volume: 31 start-page: 1695 issue: 3 year: 1985 ident: 10.1016/j.jcp.2020.109633_br0390 article-title: Canonical dynamics: equilibrium phase-space distributions publication-title: Phys. Rev. A doi: 10.1103/PhysRevA.31.1695 – volume: 113 start-page: 14183 issue: 50 year: 2016 ident: 10.1016/j.jcp.2020.109633_br0220 article-title: Data-driven parameterization of the generalized Langevin equation publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.1609587113 – start-page: 143 year: 2007 ident: 10.1016/j.jcp.2020.109633_br0090 article-title: Generalized Langevin equations and many-body problems in chemical dynamics doi: 10.1002/9780470142639.ch2 – volume: 14 start-page: 9910 issue: 48 year: 2018 ident: 10.1016/j.jcp.2020.109633_br0130 article-title: Adhesion of a polymer-grafted nanoparticle to cells explored using generalized Langevin dynamics publication-title: Soft Matter doi: 10.1039/C8SM01579A – volume: 25 start-page: 1157 issue: 9 year: 2004 ident: 10.1016/j.jcp.2020.109633_br0380 article-title: Development and testing of a general AMBER force field publication-title: J. Comput. Chem. doi: 10.1002/jcc.20035 – volume: 372 start-page: 694 year: 2018 ident: 10.1016/j.jcp.2020.109633_br0280 article-title: Faber approximation of the Mori-Zwanzig equation publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2018.06.047 – volume: 126 start-page: 43 issue: 1 year: 2007 ident: 10.1016/j.jcp.2020.109633_br0140 article-title: Testing transition state theory on Kac-Zwanzig model publication-title: J. Stat. Phys. doi: 10.1007/s10955-006-9165-0 – volume: 91 start-page: 43 issue: 1–3 year: 1995 ident: 10.1016/j.jcp.2020.109633_br0370 article-title: GROMACS: a message-passing parallel molecular dynamics implementation publication-title: Comput. Phys. Commun. doi: 10.1016/0010-4655(95)00042-E – volume: 152 start-page: 125 year: 2018 ident: 10.1016/j.jcp.2020.109633_br0040 article-title: Fast predictive models based on multi-fidelity sampling of properties in molecular dynamics simulations publication-title: Comput. Mater. Sci. doi: 10.1016/j.commatsci.2018.05.029 – volume: 11 start-page: 187 issue: 2 year: 2016 ident: 10.1016/j.jcp.2020.109633_br0250 article-title: Comparison of continuous and discrete-time data-based modeling for hypoelliptic systems publication-title: Commun. Appl. Math. Comput. Sci. doi: 10.2140/camcos.2016.11.187 – volume: 13 start-page: 1327 issue: 4 year: 2015 ident: 10.1016/j.jcp.2020.109633_br0030 article-title: Constructing surrogate models of complex systems with enhanced sparsity: quantifying the influence of conformational uncertainty in biomolecular solvation publication-title: SIAM Multiscale Model. Simul. doi: 10.1137/140981587 – volume: 66 start-page: 2534 issue: 6 year: 1977 ident: 10.1016/j.jcp.2020.109633_br0200 article-title: Dynamics of gas-solid interactions: calculations of energy transfer and sticking publication-title: J. Chem. Phys. doi: 10.1063/1.434249 – ident: 10.1016/j.jcp.2020.109633_br0270 – volume: 124 issue: 21 year: 2006 ident: 10.1016/j.jcp.2020.109633_br0310 article-title: Collective Langevin dynamics of conformational motions in proteins publication-title: J. Chem. Phys. doi: 10.1063/1.2199530 – volume: 83 start-page: 986 issue: 8–9 year: 2010 ident: 10.1016/j.jcp.2020.109633_br0160 article-title: A coarse-grained molecular dynamics model for crystalline solids publication-title: Int. J. Numer. Methods Eng. doi: 10.1002/nme.2892 – volume: 9 start-page: 215 issue: 3 year: 1973 ident: 10.1016/j.jcp.2020.109633_br0080 article-title: Nonlinear generalized Langevin equations publication-title: J. Stat. Phys. doi: 10.1007/BF01008729 – volume: 81 issue: 2 year: 2010 ident: 10.1016/j.jcp.2020.109633_br0190 article-title: Direct construction of mesoscopic models from microscopic simulations publication-title: Phys. Rev. E – volume: 83 start-page: 1917 issue: 4 year: 1985 ident: 10.1016/j.jcp.2020.109633_br0180 article-title: Generalized Langevin dynamics simulation of interacting particles publication-title: J. Chem. Phys. doi: 10.1063/1.449379 – volume: 33 start-page: 423 issue: 3 year: 1965 ident: 10.1016/j.jcp.2020.109633_br0070 article-title: Transport collective motion, and Brownian motion publication-title: Prog. Theor. Phys. doi: 10.1143/PTP.33.423 – volume: 56 start-page: 185 issue: 1 year: 2012 ident: 10.1016/j.jcp.2020.109633_br0110 article-title: Elimination of inertia from a generalized Langevin equation: applications to microbead rheology modeling and data analysis publication-title: J. Rheol. doi: 10.1122/1.3675625 – ident: 10.1016/j.jcp.2020.109633_br0350 – volume: 66 start-page: 3039 issue: 7 year: 1977 ident: 10.1016/j.jcp.2020.109633_br0100 article-title: Brownian dynamics: its application to ionic solutions publication-title: J. Chem. Phys. doi: 10.1063/1.434317 – year: 2010 ident: 10.1016/j.jcp.2020.109633_br0340 – volume: 18 issue: 12 year: 1997 ident: 10.1016/j.jcp.2020.109633_br0410 article-title: LINCS: a linear constraint solver for molecular simulations publication-title: J. Comput. Chem. doi: 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H – volume: 48 start-page: 5788 issue: 19 year: 2007 ident: 10.1016/j.jcp.2020.109633_br0010 article-title: Prediction of fibrinogen adsorption for biodegradable polymers: integration of molecular dynamics and surrogate modeling publication-title: Polymer doi: 10.1016/j.polymer.2007.07.007 – year: 2015 ident: 10.1016/j.jcp.2020.109633_br0020 – volume: 141 issue: 6 year: 2014 ident: 10.1016/j.jcp.2020.109633_br0320 article-title: Computation of the memory functions in the generalized Langevin models for collective dynamics of macromolecules publication-title: J. Chem. Phys. doi: 10.1063/1.4892412 – volume: 106 start-page: 10884 issue: 27 year: 2009 ident: 10.1016/j.jcp.2020.109633_br0150 article-title: Computing generalized Langevin equations and generalized Fokker-Planck equations publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0902633106 – volume: 16 issue: 5 year: 2014 ident: 10.1016/j.jcp.2020.109633_br0120 article-title: Generalized Langevin equations for a driven tracer in dense soft colloids: construction and applications publication-title: New J. Phys. doi: 10.1088/1367-2630/16/5/053032 – year: 2020 ident: 10.1016/j.jcp.2020.109633_br0290 article-title: Generalized Langevin equations for systems with local interactions publication-title: J. Stat. Phys. doi: 10.1007/s10955-020-02499-y – volume: 12 start-page: 393 issue: 4 year: 1969 ident: 10.1016/j.jcp.2020.109633_br0330 article-title: Numerical methods for Volterra integral equations of the first kind publication-title: Comput. J. doi: 10.1093/comjnl/12.4.393 – volume: 29 start-page: 255 issue: 1 year: 1966 ident: 10.1016/j.jcp.2020.109633_br0230 article-title: The fluctuation-dissipation theorem publication-title: Rep. Prog. Phys. doi: 10.1088/0034-4885/29/1/306 – ident: 10.1016/j.jcp.2020.109633_br0260 – volume: 98 start-page: 10089 issue: 12 year: 1993 ident: 10.1016/j.jcp.2020.109633_br0400 article-title: Particle mesh Ewald: an N⋅log(N) method for Ewald sums in large systems publication-title: J. Chem. Phys. doi: 10.1063/1.464397 – ident: 10.1016/j.jcp.2020.109633_br0240 – ident: 10.1016/j.jcp.2020.109633_br0360 – volume: 145 issue: 20 year: 2016 ident: 10.1016/j.jcp.2020.109633_br0420 article-title: The derivation and approximation of coarse-grained dynamics from Langevin dynamics publication-title: J. Chem. Phys. doi: 10.1063/1.4967936 – volume: 350 start-page: 199 issn: 0045-7825 year: 2019 ident: 10.1016/j.jcp.2020.109633_br0050 article-title: A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2019.03.014 – volume: 59 issue: 10 year: 2018 ident: 10.1016/j.jcp.2020.109633_br0210 article-title: On the estimation of the Mori-Zwanzig memory integral publication-title: J. Math. Phys. doi: 10.1063/1.5003467 |
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| SubjectTerms | Approximation Coarse-grained models Complexity Computational physics Data-driven parametrization Dimension reduction Generalized Langevin equation Granulation Mathematical analysis Molecular dynamics |
| Title | Data-driven molecular modeling with the generalized Langevin equation |
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