1
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Noid WG, Szukalo RJ, Kidder KM, Lesniewski MC. Rigorous Progress in Coarse-Graining. Annu Rev Phys Chem 2024; 75:21-45. [PMID: 38941523 DOI: 10.1146/annurev-physchem-062123-010821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Low-resolution coarse-grained (CG) models provide remarkable computational and conceptual advantages for simulating soft materials. In principle, bottom-up CG models can reproduce all structural and thermodynamic properties of atomically detailed models that can be observed at the resolution of the CG model. This review discusses recent progress in developing theory and computational methods for achieving this promise. We first briefly review variational approaches for parameterizing interaction potentials and their relationship to machine learning methods. We then discuss recent approaches for simultaneously improving both the transferability and thermodynamic properties of bottom-up models by rigorously addressing the density and temperature dependence of these potentials. We also briefly discuss exciting progress in modeling high-resolution observables with low-resolution CG models. More generally, we highlight the essential role of the bottom-up framework not only for fundamentally understanding the limitations of prior CG models but also for developing robust computational methods that resolve these limitations in practice.
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Affiliation(s)
- W G Noid
- Department of Chemistry, Pennsylvania State University, University Park, Pennsylvania, USA;
| | - Ryan J Szukalo
- Department of Chemistry, Pennsylvania State University, University Park, Pennsylvania, USA;
- Current affiliation: Department of Chemistry, Princeton University, Princeton, New Jersey, USA
| | - Katherine M Kidder
- Department of Chemistry, Pennsylvania State University, University Park, Pennsylvania, USA;
| | - Maria C Lesniewski
- Department of Chemistry, Pennsylvania State University, University Park, Pennsylvania, USA;
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2
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Jin J, Reichman DR. Hierarchical Framework for Predicting Entropies in Bottom-Up Coarse-Grained Models. J Phys Chem B 2024; 128:3182-3199. [PMID: 38507575 DOI: 10.1021/acs.jpcb.3c07624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
The thermodynamic entropy of coarse-grained (CG) models stands as one of the most important properties for quantifying the missing information during the CG process and for establishing transferable (or extendible) CG interactions. However, performing additional CG simulations on top of model construction often leads to significant additional computational overhead. In this work, we propose a simple hierarchical framework for predicting the thermodynamic entropies of various molecular CG systems. Our approach employs a decomposition of the CG interactions, enabling the estimation of the CG partition function and thermodynamic properties a priori. Starting from the ideal gas description, we leverage classical perturbation theory to systematically incorporate simple yet essential interactions, ranging from the hard sphere model to the generalized van der Waals model. Additionally, we propose an alternative approach based on multiparticle correlation functions, allowing for systematic improvements through higher-order correlations. Numerical applications to molecular liquids validate the high fidelity of our approach, and our computational protocols demonstrate that a reduced model with simple energetics can reasonably estimate the thermodynamic entropy of CG models without performing any CG simulations. Overall, our findings present a systematic framework for estimating not only the entropy but also other thermodynamic properties of CG models, relying solely on information from the reference system.
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Affiliation(s)
- Jaehyeok Jin
- Department of Chemistry, Columbia University, 3000 Broadway, New York, New York 10027, United States
| | - David R Reichman
- Department of Chemistry, Columbia University, 3000 Broadway, New York, New York 10027, United States
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3
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Ge P, Zhang L, Lei H. Machine learning assisted coarse-grained molecular dynamics modeling of meso-scale interfacial fluids. J Chem Phys 2023; 158:064104. [PMID: 36792498 DOI: 10.1063/5.0131567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
A hallmark of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which often manifests different characteristics across the molecular and continuum scale. The multi-scale nature imposes a challenge to construct reliable coarse-grained (CG) models, where the CG potential function needs to faithfully encode the many-body interactions arising from the unresolved atomistic interactions and account for the heterogeneous density distributions across the interface. We construct the CG models of both single- and two-component polymeric fluid systems based on the recently developed deep coarse-grained potential [Zhang et al., J. Chem. Phys. 149, 034101 (2018)] scheme, where each polymer molecule is modeled as a CG particle. By only using the training samples of the instantaneous force under the thermal equilibrium state, the constructed CG models can accurately reproduce both the probability density function of the void formation in bulk and the spectrum of the capillary wave across the fluid interface. More importantly, the CG models accurately predict the volume-to-area scaling transition for the apolar solvation energy, illustrating the effectiveness to probe the meso-scale collective behaviors encoded with molecular-level fidelity.
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Affiliation(s)
- Pei Ge
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan 48824, USA
| | | | - Huan Lei
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan 48824, USA
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4
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Tang J, Kobayashi T, Zhang H, Fukuzawa K, Itoh S. Enhancing pressure consistency and transferability of structure-based coarse-graining. Phys Chem Chem Phys 2023; 25:2256-2264. [PMID: 36594875 DOI: 10.1039/d2cp04849c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Coarse-graining, which models molecules with coarse-grained (CG) beads, allows molecular dynamics simulations to be applied to systems with large length and time scales while preserving the essential molecular structure. However, CG models generally have insufficient representability and transferability. A commonly used method to resolve this problem is multi-state iterative Boltzmann inversion (MS-IBI) with pressure correction, which matches both the structural properties and pressures at different thermodynamic states between CG and all-atom (AA) simulations. Nevertheless, this method is usually effective only in a narrow pressure range. In this paper, we propose a modified CG scheme to overcome this limitation. We find that the fundamental reason for this limitation is that CG beads at close distances are ellipsoids rather than isotropically compressed spheres, as described in conventional CG models. Hence, we propose a method to compensate for such differences by slightly modifying the radial distribution functions (RDFs) derived from AA simulations and using the modified RDFs as references for pressure-corrected MS-IBI. We also propose a method to determine the initial non-bonded potential using both the target RDF and pressure. Using n-dodecane as a case study, we demonstrate that the CG model developed using our scheme reproduces the RDFs and pressures over a wide range of pressure states, including three reference low-pressure states and two test high-pressure states. The proposed scheme allows for accurate CG simulations of systems in which pressure or density varies with time and/or position.
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Affiliation(s)
- Jiahao Tang
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
| | - Takayuki Kobayashi
- Department of Micro-Nano Systems Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan
| | - Hedong Zhang
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
| | - Kenji Fukuzawa
- Department of Micro-Nano Systems Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan
| | - Shintaro Itoh
- Department of Micro-Nano Systems Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan
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5
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Jin J, Pak AJ, Durumeric AEP, Loose TD, Voth GA. Bottom-up Coarse-Graining: Principles and Perspectives. J Chem Theory Comput 2022; 18:5759-5791. [PMID: 36070494 PMCID: PMC9558379 DOI: 10.1021/acs.jctc.2c00643] [Citation(s) in RCA: 80] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Indexed: 01/14/2023]
Abstract
Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.
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Affiliation(s)
- Jaehyeok Jin
- Department of Chemistry,
Chicago Center for Theoretical Chemistry, Institute for Biophysical
Dynamics, and James Franck Institute, The
University of Chicago, Chicago, Illinois 60637, United States
| | - Alexander J. Pak
- Department of Chemistry,
Chicago Center for Theoretical Chemistry, Institute for Biophysical
Dynamics, and James Franck Institute, The
University of Chicago, Chicago, Illinois 60637, United States
| | - Aleksander E. P. Durumeric
- Department of Chemistry,
Chicago Center for Theoretical Chemistry, Institute for Biophysical
Dynamics, and James Franck Institute, The
University of Chicago, Chicago, Illinois 60637, United States
| | - Timothy D. Loose
- Department of Chemistry,
Chicago Center for Theoretical Chemistry, Institute for Biophysical
Dynamics, and James Franck Institute, The
University of Chicago, Chicago, Illinois 60637, United States
| | - Gregory A. Voth
- Department of Chemistry,
Chicago Center for Theoretical Chemistry, Institute for Biophysical
Dynamics, and James Franck Institute, The
University of Chicago, Chicago, Illinois 60637, United States
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6
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Nkepsu Mbitou RL, Goujon F, Dequidt A, Latour B, Devémy J, Blaak R, Martzel N, Emeriau-Viard C, Tchoufag J, Garruchet S, Munch E, Hauret P, Malfreyt P. Consistent and Transferable Force Fields for Statistical Copolymer Systems at the Mesoscale. J Chem Theory Comput 2022; 18:6940-6951. [PMID: 36205431 DOI: 10.1021/acs.jctc.2c00945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The statistical trajectory matching (STM) method was applied successfully to derive coarse grain (CG) models for bulk properties of homopolymers. The extension of the methodology for building CG models for statistical copolymer systems is much more challenging. We present here the strategy for developing CG models for styrene-butadiene-rubber, and we compare the quality of the resulting CG force fields on the structure and thermodynamics at different chemical compositions. The CG models are used through the use of a genuine mesoscopic method called the dissipative particle dynamics method and compared to high-resolution molecular dynamics simulations. We conclude that the STM method is able to produce coarse-grained potentials that are transferable in composition by using only a few reference systems. Additionally, this methodology can be applied on any copolymer system.
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Affiliation(s)
- R L Nkepsu Mbitou
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut de Chimie de Clermont-Ferrand, F-63000Clermont-Ferrand, France.,Manufacture Française des Pneumatiques Michelin, 23, Place des Carmes, 63040Clermont-Ferrand, France
| | - F Goujon
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut de Chimie de Clermont-Ferrand, F-63000Clermont-Ferrand, France
| | - A Dequidt
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut de Chimie de Clermont-Ferrand, F-63000Clermont-Ferrand, France
| | - B Latour
- Manufacture Française des Pneumatiques Michelin, 23, Place des Carmes, 63040Clermont-Ferrand, France
| | - J Devémy
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut de Chimie de Clermont-Ferrand, F-63000Clermont-Ferrand, France
| | - R Blaak
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut de Chimie de Clermont-Ferrand, F-63000Clermont-Ferrand, France
| | - N Martzel
- Manufacture Française des Pneumatiques Michelin, 23, Place des Carmes, 63040Clermont-Ferrand, France
| | - C Emeriau-Viard
- Manufacture Française des Pneumatiques Michelin, 23, Place des Carmes, 63040Clermont-Ferrand, France
| | - J Tchoufag
- Manufacture Française des Pneumatiques Michelin, 23, Place des Carmes, 63040Clermont-Ferrand, France
| | - S Garruchet
- Manufacture Française des Pneumatiques Michelin, 23, Place des Carmes, 63040Clermont-Ferrand, France
| | - E Munch
- Manufacture Française des Pneumatiques Michelin, 23, Place des Carmes, 63040Clermont-Ferrand, France
| | - P Hauret
- Manufacture Française des Pneumatiques Michelin, 23, Place des Carmes, 63040Clermont-Ferrand, France
| | - P Malfreyt
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut de Chimie de Clermont-Ferrand, F-63000Clermont-Ferrand, France
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7
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DeLyser MR, Noid WG. Coarse-grained models for local density gradients. J Chem Phys 2022; 156:034106. [DOI: 10.1063/5.0075291] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Affiliation(s)
- Michael R. DeLyser
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
| | - W. G. Noid
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
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8
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Dhamankar S, Webb MA. Chemically specific coarse‐graining of polymers: Methods and prospects. JOURNAL OF POLYMER SCIENCE 2021. [DOI: 10.1002/pol.20210555] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Satyen Dhamankar
- Department of Chemical and Biological Engineering Princeton University Princeton New Jersey USA
| | - Michael A. Webb
- Department of Chemical and Biological Engineering Princeton University Princeton New Jersey USA
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9
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Pretti E, Shell MS. A microcanonical approach to temperature-transferable coarse-grained models using the relative entropy. J Chem Phys 2021; 155:094102. [PMID: 34496595 DOI: 10.1063/5.0057104] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Bottom-up coarse-graining methods provide systematic tools for creating simplified models of molecular systems. However, coarse-grained (CG) models produced with such methods frequently fail to accurately reproduce all thermodynamic properties of the reference atomistic systems they seek to model and, moreover, can fail in even more significant ways when used at thermodynamic state points different from the reference conditions. These related problems of representability and transferability limit the usefulness of CG models, especially those of strongly state-dependent systems. In this work, we present a new strategy for creating temperature-transferable CG models using a single reference system and temperature. The approach is based on two complementary concepts. First, we switch to a microcanonical basis for formulating CG models, focusing on effective entropy functions rather than energy functions. This allows CG models to naturally represent information about underlying atomistic energy fluctuations, which would otherwise be lost. Such information not only reproduces energy distributions of the reference model but also successfully predicts the correct temperature dependence of the CG interactions, enabling temperature transferability. Second, we show that relative entropy minimization provides a direct and systematic approach to parameterize such classes of temperature-transferable CG models. We calibrate the approach initially using idealized model systems and then demonstrate its ability to create temperature-transferable CG models for several complex molecular liquids.
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Affiliation(s)
- Evan Pretti
- Department of Chemical Engineering, Engineering II Building, University of California, Santa Barbara, Santa Barbara, California 93106-5080, USA
| | - M Scott Shell
- Department of Chemical Engineering, Engineering II Building, University of California, Santa Barbara, Santa Barbara, California 93106-5080, USA
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10
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Jin J, Han Y, Pak AJ, Voth GA. A new one-site coarse-grained model for water: Bottom-up many-body projected water (BUMPer). I. General theory and model. J Chem Phys 2021; 154:044104. [PMID: 33514116 PMCID: PMC7826168 DOI: 10.1063/5.0026651] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 12/14/2020] [Indexed: 12/26/2022] Open
Abstract
Water is undoubtedly one of the most important molecules for a variety of chemical and physical systems, and constructing precise yet effective coarse-grained (CG) water models has been a high priority for computer simulations. To recapitulate important local correlations in the CG water model, explicit higher-order interactions are often included. However, the advantages of coarse-graining may then be offset by the larger computational cost in the model parameterization and simulation execution. To leverage both the computational efficiency of the CG simulation and the inclusion of higher-order interactions, we propose a new statistical mechanical theory that effectively projects many-body interactions onto pairwise basis sets. The many-body projection theory presented in this work shares similar physics from liquid state theory, providing an efficient approach to account for higher-order interactions within the reduced model. We apply this theory to project the widely used Stillinger-Weber three-body interaction onto a pairwise (two-body) interaction for water. Based on the projected interaction with the correct long-range behavior, we denote the new CG water model as the Bottom-Up Many-Body Projected Water (BUMPer) model, where the resultant CG interaction corresponds to a prior model, the iteratively force-matched model. Unlike other pairwise CG models, BUMPer provides high-fidelity recapitulation of pair correlation functions and three-body distributions, as well as N-body correlation functions. BUMPer extensively improves upon the existing bottom-up CG water models by extending the accuracy and applicability of such models while maintaining a reduced computational cost.
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Affiliation(s)
- Jaehyeok Jin
- Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - Yining Han
- Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - Alexander J. Pak
- Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - Gregory A. Voth
- Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA
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11
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Shahidi N, Chazirakis A, Harmandaris V, Doxastakis M. Coarse-graining of polyisoprene melts using inverse Monte Carlo and local density potentials. J Chem Phys 2020; 152:124902. [PMID: 32241142 DOI: 10.1063/1.5143245] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Bottom-up coarse-graining of polymers is commonly performed by matching structural order parameters such as distribution of bond lengths, bending and dihedral angles, and pair distribution functions. In this study, we introduce the distribution of nearest-neighbors as an additional order parameter in the concept of local density potentials. We describe how the inverse-Monte Carlo method provides a framework for forcefield development that is capable of overcoming challenges associated with the parameterization of interaction terms in polymer systems. The technique is applied on polyisoprene melts as a prototype system. We demonstrate that while different forcefields can be developed that perform equally in terms of matching target distributions, the inclusion of nearest-neighbors provides a straightforward route to match both thermodynamic and conformational properties. We find that several temperature state points can also be addressed, provided that the forcefield is refined accordingly. Finally, we examine both the single-particle and the collective dynamics of the coarse-grain models, demonstrating that all forcefields present a similar acceleration relative to the atomistic systems.
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Affiliation(s)
- Nobahar Shahidi
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA
| | - Antonis Chazirakis
- Department of Mathematics and Applied Mathematics, University of Crete, Heraklion GR-71110, Greece
| | - Vagelis Harmandaris
- Department of Mathematics and Applied Mathematics, University of Crete, Heraklion GR-71110, Greece
| | - Manolis Doxastakis
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA
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12
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Abstract
Low resolution coarse-grained (CG) models are widely adopted for investigating phenomena that cannot be effectively simulated with all-atom (AA) models. Since the development of the many-body dissipative particle dynamics method, CG models have increasingly supplemented conventional pair potentials with one-body potentials of the local density (LD) around each site. These LD potentials appear to significantly extend the transferability of CG models, while also enabling more accurate descriptions of thermodynamic properties, interfacial phenomena, and many-body correlations. In this work, we systematically examine the properties of LD potentials. We first derive and numerically demonstrate a nontrivial transformation of pair and LD potentials that leaves the total forces and equilibrium distribution invariant. Consequently, the pair and LD potentials determined via bottom-up methods are not unique. We then investigate the sensitivity of CG models for glycerol to the weighting function employed for defining the local density. We employ the multiscale coarse-graining (MS-CG) method to simultaneously parameterize both pair and LD potentials. When employing a short-ranged Lucy function that defines the local density from the first solvation shell, the MS-CG model accurately reproduces the pair structure, pressure-density equation of state, and liquid-vapor interfacial profile of the AA model. The accuracy of the model generally decreases as the range of the Lucy function increases further. The MS-CG model provides similar accuracy when a smoothed Heaviside function is employed to define the local density from the first solvation shell. However, the model performs less well when this function acts on either longer or shorter length scales.
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Affiliation(s)
- Michael R DeLyser
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
| | - W G Noid
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
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13
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Jin J, Pak AJ, Voth GA. Understanding Missing Entropy in Coarse-Grained Systems: Addressing Issues of Representability and Transferability. J Phys Chem Lett 2019; 10:4549-4557. [PMID: 31319036 PMCID: PMC6782054 DOI: 10.1021/acs.jpclett.9b01228] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Coarse-grained (CG) models facilitate efficient simulation of complex systems by integrating out the atomic, or fine-grained (FG), degrees of freedom. Systematically derived CG models from FG simulations often attempt to approximate the CG potential of mean force (PMF), an inherently multidimensional and many-body quantity, using additive pairwise contributions. However, they currently lack fundamental principles that enable their extensible use across different thermodynamic state points, i.e., transferability. In this work, we investigate the explicit energy-entropy decomposition of the CG PMF as a means to construct transferable CG models. In particular, despite its high-dimensional nature, we find for liquid systems that the entropic component to the CG PMF can similarly be represented using additive pairwise contributions, which we show is highly coupled to the CG configurational entropy. This approach formally connects the missing entropy that is lost due to the CG representation, i.e., translational, rotational, and vibrational modes associated with the missing degrees of freedom, to the CG entropy. By design, the present framework imparts transferable CG interactions across different temperatures due to the explicit definition of an additive entropic contribution. Furthermore, we demonstrate that transferability across composition state points, such as between bulk liquids and their mixtures, is also achieved by designing combining rules to approximate cross-interactions from bulk CG PMFs. Using the predicted CG model for liquid mixtures, structural correlations of the fitted CG model were found to corroborate a high-fidelity combining rule. Our findings elucidate the physical nature and compact representation of CG entropy and suggest a new approach for overcoming the transferability problem. We expect that this approach will further extend the current view of CG modeling into predictive multiscale modeling.
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14
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Wang J, Olsson S, Wehmeyer C, Pérez A, Charron NE, de Fabritiis G, Noé F, Clementi C. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields. ACS CENTRAL SCIENCE 2019; 5:755-767. [PMID: 31139712 PMCID: PMC6535777 DOI: 10.1021/acscentsci.8b00913] [Citation(s) in RCA: 199] [Impact Index Per Article: 39.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Indexed: 05/17/2023]
Abstract
Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction.
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Affiliation(s)
- Jiang Wang
- Center
for Theoretical Biological Physics, Rice
University, Houston, Texas 77005, United States
- Department
of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Simon Olsson
- Department
of Mathematics and Computer Science, Freie
Universität Berlin, Arnimallee 6, 14195 Berlin, Germany
| | - Christoph Wehmeyer
- Department
of Mathematics and Computer Science, Freie
Universität Berlin, Arnimallee 6, 14195 Berlin, Germany
| | - Adrià Pérez
- Computational
Science Laboratory, Universitat Pompeu Fabra, PRBB, C/Dr Aiguader 88, 08003 Barcelona, Spain
| | - Nicholas E. Charron
- Center
for Theoretical Biological Physics, Rice
University, Houston, Texas 77005, United States
- Department
of Physics, Rice University, Houston, Texas 77005, United States
| | - Gianni de Fabritiis
- Computational
Science Laboratory, Universitat Pompeu Fabra, PRBB, C/Dr Aiguader 88, 08003 Barcelona, Spain
- Institucio
Catalana de Recerca i Estudis Avanats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
| | - Frank Noé
- Center
for Theoretical Biological Physics, Rice
University, Houston, Texas 77005, United States
- Department
of Chemistry, Rice University, Houston, Texas 77005, United States
- Department
of Mathematics and Computer Science, Freie
Universität Berlin, Arnimallee 6, 14195 Berlin, Germany
| | - Cecilia Clementi
- Center
for Theoretical Biological Physics, Rice
University, Houston, Texas 77005, United States
- Department
of Chemistry, Rice University, Houston, Texas 77005, United States
- Department
of Mathematics and Computer Science, Freie
Universität Berlin, Arnimallee 6, 14195 Berlin, Germany
- Department
of Physics, Rice University, Houston, Texas 77005, United States
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15
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Rosenberger D, Sanyal T, Shell MS, van der Vegt NFA. Transferability of Local Density-Assisted Implicit Solvation Models for Homogeneous Fluid Mixtures. J Chem Theory Comput 2019; 15:2881-2895. [PMID: 30995034 DOI: 10.1021/acs.jctc.8b01170] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The application of bottom-up coarse grained (CG) models to study the equilibrium mixing behavior of liquids is rather challenging, since these models can be significantly influenced by the density or the concentration of the state chosen during parametrization. This dependency leads to low transferability in density/concentration space and has been one of the major limitations in bottom-up coarse graining. Recent approaches proposed to tackle this shortcoming range from the addition of thermodynamic constraints, to an extended ensemble parametrization, to the addition of supplementary terms to the system's Hamiltonian. To study fluid phase equilibria with bottom-up CG models, the application of local density (LD) potentials appears to be a promising approach, as shown in previous work by Sanyal and Shell [T. Sanyal, M. S. Shell, J. Phys. Chem. B, 2018, 122, 5678]. Here, we want to further explore this method and test its ability to model a system which contains structural inhomogeneities only on the molecular scale, namely, solutions of methanol and water. We find that a water-water LD potential improves the transferability of an implicit-methanol CG model toward high water concentration. Conversely, a methanol-methanol LD potential does not significantly improve the transferability of an implicit-water CG model toward high methanol concentration. These differences appear due to the presence of cooperative interactions in water at high concentrations that the LD potentials can capture. In addition, we compare two different approaches to derive our CG models, namely, relative entropy optimization and the Inverse Monte Carlo method, and formally demonstrate under which analytical and numerical assumptions these two methods yield equivalent results.
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Affiliation(s)
- David Rosenberger
- Eduard Zintl Institut für Anorganische und Physikalische Chemie , Technische Universität Darmstadt , Darmstadt , Germany
| | - Tanmoy Sanyal
- Department of Chemical Engineering , University of California Santa Barbara , Santa Barbara , California 93106 , United States
| | - M Scott Shell
- Department of Chemical Engineering , University of California Santa Barbara , Santa Barbara , California 93106 , United States
| | - Nico F A van der Vegt
- Eduard Zintl Institut für Anorganische und Physikalische Chemie , Technische Universität Darmstadt , Darmstadt , Germany
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16
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Lebold KM, Noid WG. Systematic study of temperature and density variations in effective potentials for coarse-grained models of molecular liquids. J Chem Phys 2019; 150:014104. [DOI: 10.1063/1.5050509] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Affiliation(s)
- Kathryn M. Lebold
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
| | - W. G. Noid
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
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17
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Potter TD, Tasche J, Wilson MR. Assessing the transferability of common top-down and bottom-up coarse-grained molecular models for molecular mixtures. Phys Chem Chem Phys 2019; 21:1912-1927. [DOI: 10.1039/c8cp05889j] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Assessing the performance of top-down and bottom-up coarse-graining approaches.
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Affiliation(s)
| | - Jos Tasche
- Department of Chemistry
- Durham University
- Lower Mountjoy
- Durham
- UK
| | - Mark R. Wilson
- Department of Chemistry
- Durham University
- Lower Mountjoy
- Durham
- UK
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18
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Jin J, Han Y, Voth GA. Ultra-Coarse-Grained Liquid State Models with Implicit Hydrogen Bonding. J Chem Theory Comput 2018; 14:6159-6174. [PMID: 30354110 DOI: 10.1021/acs.jctc.8b00812] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Coarse-graining (CG) methodologies have been widely used to extend the time and length scales of computer simulations by averaging over the atomistic details beneath the resolution of the CG models. Despite the efficiency of CG models, important configurational information during a given process may be lost at the CG resolution. One example of this is the topology of the hydrogen bonding network in the liquid state. When the functional group that participates in hydrogen bonding (e.g., -OH in methanol) is coarse-grained into one CG site, the effective interactions of the resultant CG model are usually derived from an averaged overall trajectory and, thus, do not take into account the hydrogen bonding interactions and topologies that are present at the all-atom resolution. In order to overcome this challenge, the present study develops new ultra-coarse-grained (UCG) models that include internal states within the CG sites that participate in hydrogen bonding, where each state represents a specific configuration such as the hydrogen bonding donor or acceptor. Internal states of the UCG beads are modeled to remain in quasi-equilibrium, and the degree of mixing is controlled by utilizing the effective local density of the UCG sites. In particular, we consider two groups of UCG models with different types of hydrogen bonding motifs: chain-like and ring-like. Using five different liquid systems that contain the same fundamental functional groups as biomolecules, we demonstrate the ability of the UCG models to reproduce the structural properties that originate from the configurations beneath the resolution of the UCG model. This proposed approach can also be applied to other liquids with such specific and directional interactions, or even to complex biomolecular systems in which hydrogen bonding is critical.
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Affiliation(s)
- Jaehyeok Jin
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics , The University of Chicago , Chicago , Illinois 60637 , United States
| | - Yining Han
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics , The University of Chicago , Chicago , Illinois 60637 , United States
| | - Gregory A Voth
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics , The University of Chicago , Chicago , Illinois 60637 , United States
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19
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20
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Wagner JW, Dannenhoffer-Lafage T, Jin J, Voth GA. Extending the range and physical accuracy of coarse-grained models: Order parameter dependent interactions. J Chem Phys 2018; 147:044113. [PMID: 28764380 DOI: 10.1063/1.4995946] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Order parameters (i.e., collective variables) are often used to describe the behavior of systems as they capture different features of the free energy surface. Yet, most coarse-grained (CG) models only employ two- or three-body non-bonded interactions between the CG particles. In situations where these interactions are insufficient for the CG model to reproduce the structural distributions of the underlying fine-grained (FG) model, additional interactions must be included. In this paper, we introduce an approach to expand the basis sets available in the multiscale coarse-graining (MS-CG) methodology by including order parameters. Then, we investigate the ability of an additive local order parameter (e.g., density) and an additive global order parameter (i.e., distance from a hard wall) to improve the description of CG models in interfacial systems. Specifically, we study methanol liquid-vapor coexistence, acetonitrile liquid-vapor coexistence, and acetonitrile liquid confined by hard-wall plates, all using single site CG models. We find that the use of order parameters dramatically improves the reproduction of structural properties of interfacial CG systems relative to the FG reference as compared with pairwise CG interactions alone.
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Affiliation(s)
- Jacob W Wagner
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, USA
| | - Thomas Dannenhoffer-Lafage
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, USA
| | - Jaehyeok Jin
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, USA
| | - Gregory A Voth
- Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, USA
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21
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Sanyal T, Shell MS. Transferable Coarse-Grained Models of Liquid-Liquid Equilibrium Using Local Density Potentials Optimized with the Relative Entropy. J Phys Chem B 2018; 122:5678-5693. [PMID: 29466859 DOI: 10.1021/acs.jpcb.7b12446] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Bottom-up coarse-grained (CG) models are now regularly pursued to enable large length and time scale molecular simulations of complex, often macromolecular systems. However, predicting fluid phase equilibria using such models remains fundamentally challenging. A major problem stems from the typically low transferability of CG models beyond the densities and/or compositions at which they are parametrized, which is necessary if they are to describe distinct structural and thermodynamic properties unique to each phase. CG model transferability is compounded by the representation of the inherently multibody coarse interactions using pair potentials that neglect higher order effects. Here, we propose to construct transferable single site CG models of liquid mixtures by supplementing traditional CG pair interactions with local density potentials, which constitute a computationally inexpensive mean-field approach to describe many-body effects, in that site energies are modulated by the local solution environment. To illustrate the approach, we use intra- and interspecies local density potentials to develop CG models of benzene-water solutions that show impressive transferability in structural metrics (pair correlation functions, density profiles) throughout composition space, in contrast to pair-only CG representations. While further refinement may be necessary to represent more complex thermodynamic properties, like the liquid-liquid interfacial tension, the generality and improvement offered by the local density approach are highly encouraging for enabling complex phase equilibrium modeling using CG models.
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Affiliation(s)
- Tanmoy Sanyal
- Department of Chemical Engineering , University of California, Santa Barbara , Santa Barbara , California , United States
| | - M Scott Shell
- Department of Chemical Engineering , University of California, Santa Barbara , Santa Barbara , California , United States
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22
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DeLyser MR, Noid WG. Extending pressure-matching to inhomogeneous systems via local-density potentials. J Chem Phys 2017; 147:134111. [DOI: 10.1063/1.4999633] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Michael R. DeLyser
- Department of Chemistry, Penn State University, University
Park, Pennsylvania 16802, USA
| | - William G. Noid
- Department of Chemistry, Penn State University, University
Park, Pennsylvania 16802, USA
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23
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Molavi Tabrizi A, Goossens S, Mehdizadeh Rahimi A, Knepley M, Bardhan JP. Predicting solvation free energies and thermodynamics in polar solvents and mixtures using a solvation-layer interface condition. J Chem Phys 2017. [DOI: 10.1063/1.4977037] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Amirhossein Molavi Tabrizi
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA
| | - Spencer Goossens
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA
| | - Ali Mehdizadeh Rahimi
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA
| | - Matthew Knepley
- Department of Computational and Applied Mathematics, Rice University, Houston, Texas 77005, USA
| | - Jaydeep P. Bardhan
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA
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24
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Montes-Saralegui M, Kahl G, Nikoubashman A. On the applicability of density dependent effective interactions in cluster-forming systems. J Chem Phys 2017; 146:054904. [DOI: 10.1063/1.4975164] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Affiliation(s)
- Marta Montes-Saralegui
- Institute for Theoretical Physics and Center for Computational Material Science (CMS), Technische Universität Wien, Wiedner Hauptstraße 8-10, A-1040 Wien, Austria
| | - Gerhard Kahl
- Institute for Theoretical Physics and Center for Computational Material Science (CMS), Technische Universität Wien, Wiedner Hauptstraße 8-10, A-1040 Wien, Austria
| | - Arash Nikoubashman
- Institute of Physics, Johannes Gutenberg University Mainz, Staudingerweg 7, 55128 Mainz, Germany
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25
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Sanyal T, Shell MS. Coarse-grained models using local-density potentials optimized with the relative entropy: Application to implicit solvation. J Chem Phys 2017; 145:034109. [PMID: 27448876 DOI: 10.1063/1.4958629] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Bottom-up multiscale techniques are frequently used to develop coarse-grained (CG) models for simulations at extended length and time scales but are often limited by a compromise between computational efficiency and accuracy. The conventional approach to CG nonbonded interactions uses pair potentials which, while computationally efficient, can neglect the inherently multibody contributions of the local environment of a site to its energy, due to degrees of freedom that were coarse-grained out. This effect often causes the CG potential to depend strongly on the overall system density, composition, or other properties, which limits its transferability to states other than the one at which it was parameterized. Here, we propose to incorporate multibody effects into CG potentials through additional nonbonded terms, beyond pair interactions, that depend in a mean-field manner on local densities of different atomic species. This approach is analogous to embedded atom and bond-order models that seek to capture multibody electronic effects in metallic systems. We show that the relative entropy coarse-graining framework offers a systematic route to parameterizing such local density potentials. We then characterize this approach in the development of implicit solvation strategies for interactions between model hydrophobes in an aqueous environment.
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Affiliation(s)
- Tanmoy Sanyal
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
| | - M Scott Shell
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
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26
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Wagner JW, Dama JF, Durumeric AEP, Voth GA. On the representability problem and the physical meaning of coarse-grained models. J Chem Phys 2017; 145:044108. [PMID: 27475349 DOI: 10.1063/1.4959168] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
In coarse-grained (CG) models where certain fine-grained (FG, i.e., atomistic resolution) observables are not directly represented, one can nonetheless identify indirect the CG observables that capture the FG observable's dependence on CG coordinates. Often, in these cases it appears that a CG observable can be defined by analogy to an all-atom or FG observable, but the similarity is misleading and significantly undermines the interpretation of both bottom-up and top-down CG models. Such problems emerge especially clearly in the framework of the systematic bottom-up CG modeling, where a direct and transparent correspondence between FG and CG variables establishes precise conditions for consistency between CG observables and underlying FG models. Here we present and investigate these representability challenges and illustrate them via the bottom-up conceptual framework for several simple analytically tractable polymer models. The examples provide special focus on the observables of configurational internal energy, entropy, and pressure, which have been at the root of controversy in the CG literature, as well as discuss observables that would seem to be entirely missing in the CG representation but can nonetheless be correlated with CG behavior. Though we investigate these problems in the framework of systematic coarse-graining, the lessons apply to top-down CG modeling also, with crucial implications for simulation at constant pressure and surface tension and for the interpretations of structural and thermodynamic correlations for comparison to experiment.
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Affiliation(s)
- Jacob W Wagner
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, and Computation Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - James F Dama
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, and Computation Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - Aleksander E P Durumeric
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, and Computation Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - Gregory A Voth
- Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, and Computation Institute, The University of Chicago, Chicago, Illinois 60637, USA
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27
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Affiliation(s)
- M. Scott Shell
- Department of Chemical Engineering; University of California Santa Barbara; Santa Barbara CA USA
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28
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Dunn NJH, Noid WG. Bottom-up coarse-grained models with predictive accuracy and transferability for both structural and thermodynamic properties of heptane-toluene mixtures. J Chem Phys 2016; 144:204124. [DOI: 10.1063/1.4952422] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Nicholas J. H. Dunn
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - W. G. Noid
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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29
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30
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Dalgicdir C, Sensoy O, Peter C, Sayar M. A transferable coarse-grained model for diphenylalanine: How to represent an environment driven conformational transition. J Chem Phys 2013; 139:234115. [DOI: 10.1063/1.4848675] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
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31
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D'Adamo G, Pelissetto A, Pierleoni C. Predicting the thermodynamics by using state-dependent interactions. J Chem Phys 2013; 138:234107. [DOI: 10.1063/1.4810881] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
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32
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Rosch TW, Brennan JK, Izvekov S, Andzelm JW. Exploring the ability of a multiscale coarse-grained potential to describe the stress-strain response of glassy polystyrene. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2013; 87:042606. [PMID: 23679442 DOI: 10.1103/physreve.87.042606] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2012] [Revised: 03/01/2013] [Indexed: 06/02/2023]
Abstract
A new particle-based bottom-up method to develop coarse-grained models of polymers is presented and applied to polystyrene. The multiscale coarse-graining (MS-CG) technique of Izvekov et al. [J. Chem. Phys. 120, 10896 (2004)] is applied to a polymer system to calculate nonbonded interactions. The inverse Boltzmann inversion method was used to parametrize the bonded and bond-angle bending interactions. Molecular dynamics simulations were performed, and the CG model exhibited a significantly lower modulus compared to the atomistic model at low temperature and high strain rate. In an attempt to improve the CG model performance, several other parametrization schemes were used to build other models from this base model. The first of these models included standard frictional forces through use of the constant-temperature dissipative particle dynamics method that improved the modulus, yet was not transferrable to higher temperatures and lower strain rates. Other models were built by increasing the attraction between CG beads through direct manipulation of the nonbonded potential, where an improvement of the stress response was found. For these models, two parametrization protocols that shifted the force to more attractive values were explored. The first protocol involved a uniform shift, while the other protocol shifted the force in a more localized region. The uniformly shifted potential greatly affected the structure of the equilibrium model as compared to the locally shifted potential, yet was more transferrable to different temperatures and strain rates. Further improvements in the coarse-graining protocol to generate models that more satisfactorily capture mechanical properties are suggested.
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Affiliation(s)
- Thomas W Rosch
- U.S. Army Research Laboratory, Weapons and Materials Research Directorate, Aberdeen Proving Ground, Maryland 21005-5066, USA
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33
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Abstract
This chapter provides a primer on theories for coarse-grained (CG) modeling and, in particular, reviews several systematic methods for determining effective potentials for CG models. The chapter first reviews a statistical mechanics framework for relating atomistic and CG models. This framework naturally leads to a quantitative criterion for CG models that are "consistent" with a particular atomistic model for the same system. This consistency criterion is equivalent to minimizing the relative entropy between the two models. This criterion implies that a many-body PMF is the appropriate potential for a CG model that is consistent with a particular atomistic model. This chapter then presents a unified exposition of the theory and numerical methods for several approaches for approximating this many-body PMF. Finally, this chapter closes with a brief discussion of a few of the outstanding challenges facing the field of systematic coarse-graining.
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Affiliation(s)
- W G Noid
- Department of Chemistry, The Pennsylvania State University, University Park, PA, USA
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34
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Computational Studies of Biomembrane Systems: Theoretical Considerations, Simulation Models, and Applications. FROM SINGLE MOLECULES TO NANOSCOPICALLY STRUCTURED MATERIALS 2013. [DOI: 10.1007/12_2013_258] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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35
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Izvekov S, Chung PW, Rice BM. Particle-based multiscale coarse graining with density-dependent potentials: Application to molecular crystals (hexahydro-1,3,5-trinitro-s-triazine). J Chem Phys 2011; 135:044112. [DOI: 10.1063/1.3607603] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
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36
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37
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Engin O, Villa A, Peter C, Sayar M. A Challenge for Peptide Coarse Graining: Transferability of Fragment-Based Models. MACROMOL THEOR SIMUL 2011. [DOI: 10.1002/mats.201100005] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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38
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Shen JW, Li C, van der Vegt NF, Peter C. Transferability of Coarse Grained Potentials: Implicit Solvent Models for Hydrated Ions. J Chem Theory Comput 2011; 7:1916-27. [DOI: 10.1021/ct2001396] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Jia-Wei Shen
- Max Planck Institute for Polymer Research, Ackermannweg 10, D-55128 Mainz, Germany
| | - Chunli Li
- Center of Smart Interfaces, Technische Universität Darmstadt, Petersenstrasse 32, D-64287 Darmstadt, Germany
| | - Nico F.A. van der Vegt
- Center of Smart Interfaces, Technische Universität Darmstadt, Petersenstrasse 32, D-64287 Darmstadt, Germany
| | - Christine Peter
- Max Planck Institute for Polymer Research, Ackermannweg 10, D-55128 Mainz, Germany
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39
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Fritz D, Koschke K, Harmandaris VA, van der Vegt NFA, Kremer K. Multiscale modeling of soft matter: scaling of dynamics. Phys Chem Chem Phys 2011; 13:10412-20. [DOI: 10.1039/c1cp20247b] [Citation(s) in RCA: 145] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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40
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Khounlavong L, Pryamitsyn V, Ganesan V. Many-body interactions and coarse-grained simulations of structure of nanoparticle-polymer melt mixtures. J Chem Phys 2010; 133:144904. [DOI: 10.1063/1.3484940] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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41
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Mullinax JW, Noid WG. Reference state for the generalized Yvon-Born-Green theory: application for coarse-grained model of hydrophobic hydration. J Chem Phys 2010; 133:124107. [PMID: 20886924 PMCID: PMC3188631 DOI: 10.1063/1.3481574] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2010] [Accepted: 08/02/2010] [Indexed: 01/07/2023] Open
Abstract
Coarse-grained (CG) models provide a computationally efficient means for investigating phenomena that remain beyond the scope of atomically detailed models. Although CG models are often parametrized to reproduce the results of atomistic simulations, it is highly desirable to determine accurate CG models from experimental data. Recently, we have introduced a generalized Yvon-Born-Green (g-YBG) theory for directly (i.e., noniteratively) determining variationally optimized CG potentials from structural correlation functions. In principle, these correlation functions can be determined from experiment. In the present work, we introduce a reference state potential into the g-YBG framework. The reference state defines a fixed contribution to the CG potential. The remaining terms in the potential are then determined, such that the combined potential provides an optimal approximation to the many-body potential of mean force. By specifying a fixed contribution to the potential, the reference state significantly reduces the computational complexity and structural information necessary for determining the remaining potentials. We also validate the quantitative accuracy of the proposed method and numerically demonstrate that the reference state provides a convenient framework for transferring CG potentials from neat liquids to more complex systems. The resulting CG model provides a surprisingly accurate description of the two- and three-particle solvation structures of a hydrophobic solute in methanol. This work represents a significant step in developing the g-YBG theory as a useful computational framework for determining accurate CG models from limited experimental data.
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Affiliation(s)
- J W Mullinax
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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42
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Izvekov S, Chung PW, Rice BM. The multiscale coarse-graining method: Assessing its accuracy and introducing density dependent coarse-grain potentials. J Chem Phys 2010; 133:064109. [DOI: 10.1063/1.3464776] [Citation(s) in RCA: 76] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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43
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Villa A, Peter C, van der Vegt NFA. Transferability of Nonbonded Interaction Potentials for Coarse-Grained Simulations: Benzene in Water. J Chem Theory Comput 2010; 6:2434-44. [DOI: 10.1021/ct100228t] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Alessandra Villa
- Karolinska Institutet, SE-14183 Huddingen, Sweden, Max-Planck-Institute for Polymer Research, D-55128 Mainz, Germany, and Center of Smart Interfaces, Technische Universität Darmstadt, D-64287 Darmstadt, Germany
| | - Christine Peter
- Karolinska Institutet, SE-14183 Huddingen, Sweden, Max-Planck-Institute for Polymer Research, D-55128 Mainz, Germany, and Center of Smart Interfaces, Technische Universität Darmstadt, D-64287 Darmstadt, Germany
| | - Nico F. A. van der Vegt
- Karolinska Institutet, SE-14183 Huddingen, Sweden, Max-Planck-Institute for Polymer Research, D-55128 Mainz, Germany, and Center of Smart Interfaces, Technische Universität Darmstadt, D-64287 Darmstadt, Germany
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Allen EC, Rutledge GC. Coarse-grained, density dependent implicit solvent model reliably reproduces behavior of a model surfactant system. J Chem Phys 2009; 130:204903. [PMID: 19485477 DOI: 10.1063/1.3139025] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Density dependent, implicit solvent (DDIS) potentials, the generation of which has been described previously [E. C. Allen and G. C. Rutledge, J. Chem. Phys. 128, 154115 (2008); E. C. Allen and G. C. Rutledge, J. Chem. Phys. 130, 034904 (2009)], are used in this work to examine the self-assembly of a model surfactant system. While the measurement of thermodynamic properties in simulations of solvated micelles requires large computational resources or specialized free energy calculations, the high degree of coarse-graining enabled by the DDIS algorithm allows for the measurement of critical micelle concentration and aggregation number distribution using single processor NVT simulations. In order to evaluate the transferability of potentials derived from the DDIS methodology, the potentials are derived from simulations of simple monomeric solutes and used in the surfactant system without modification. Despite the high degree of coarse graining and the simplicity of the fitting simulations, we demonstrate that the coarse-grained DDIS potentials generated by this method reliably reproduce key properties of the underlying surfactant system: the critical micelle concentration, and the average aggregation number. The success of the DDIS algorithm suggests its utility for more realistic surfactant models.
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Affiliation(s)
- Erik C Allen
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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Mullinax JW, Noid WG. Extended ensemble approach for deriving transferable coarse-grained potentials. J Chem Phys 2009. [DOI: 10.1063/1.3220627] [Citation(s) in RCA: 91] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
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