1
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Wu Z, Zhou T. Structural Coarse-Graining via Multiobjective Optimization with Differentiable Simulation. J Chem Theory Comput 2024; 20:2605-2617. [PMID: 38483262 DOI: 10.1021/acs.jctc.3c01348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
In the realm of multiscale molecular simulations, structure-based coarse-graining is a prominent approach for creating efficient coarse-grained (CG) representations of soft matter systems, such as polymers. This involves optimizing CG interactions by matching static correlation functions of the corresponding degrees of freedom in all-atom (AA) models. Here, we present a versatile method, namely, differentiable coarse-graining (DiffCG), which combines multiobjective optimization and differentiable simulation. The DiffCG approach is capable of constructing robust CG models by iteratively optimizing the effective potentials to simultaneously match multiple target properties. We demonstrate our approach by concurrently optimizing bonded and nonbonded potentials of a CG model of polystyrene (PS) melts. The resulting CG-PS model effectively reproduces both the structural characteristics, such as the equilibrium probability distribution of microscopic degrees of freedom and the thermodynamic pressure of the AA counterpart. More importantly, leveraging the multiobjective optimization capability, we develop a precise and efficient CG model for PS melts that is transferable across a wide range of temperatures, i.e., from 400 to 600 K. It is achieved via optimizing a pairwise potential with nonlinear temperature dependence in the CG model to simultaneously match target data from AA-MD simulations at multiple thermodynamic states. The temperature transferable CG-PS model demonstrates its ability to accurately predict the radial distribution functions and density at different temperatures, including those that are not included in the target thermodynamic states. Our work opens up a promising route for developing accurate and transferable CG models of complex soft-matter systems through multiobjective optimization with differentiable simulation.
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Affiliation(s)
- Zhenghao Wu
- Department of Chemistry, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, P. R. China
| | - Tianhang Zhou
- College of Carbon Neutrality Future Technology, State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Beijing), Beijing 102249, P. R. China
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2
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Lesniewski MC, Noid WG. Insight into the Density-Dependence of Pair Potentials for Predictive Coarse-Grained Models. J Phys Chem B 2024; 128:1298-1316. [PMID: 38271676 DOI: 10.1021/acs.jpcb.3c06890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
We investigate the temperature- and density-dependence of effective pair potentials for 1-site coarse-grained (CG) models of two industrial solvents, 1,4-dioxane and tetrahydrofuran. We observe that the calculated pair potentials are much more sensitive to density than to temperature. The generalized-Yvon-Born-Green framework reveals that this striking density-dependence reflects corresponding variations in the many-body correlations that determine the environment-mediated indirect contribution to the pair mean force. Moreover, we demonstrate, perhaps surprisingly, that this density-dependence is not important for accurately modeling the intermolecular structure. Accordingly, we adopt a density-independent interaction potential and transfer the density-dependence of the calculated pair potentials into a configuration-independent volume potential. Furthermore, we develop a single global potential that accurately models the intermolecular structure and pressure-volume equation of state across a very wide range of liquid state points. Consequently, this work provides fundamental insight into the density-dependence of effective pair potentials and also provides a significant step toward developing predictive CG models for efficiently modeling industrial solvents.
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Affiliation(s)
- Maria C Lesniewski
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - W G Noid
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
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3
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Mahajan S, Tang T. Automated Parameterization of Coarse-Grained Polyethylenimine under a Martini Framework. J Chem Inf Model 2023; 63:4328-4341. [PMID: 37424081 DOI: 10.1021/acs.jcim.3c00103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
As a versatile polymer in many applications, synthesized polyethylenimine (PEI) is polydisperse with diverse branched structures that attain pH-dependent protonation states. Understanding the structure-function relationship of PEI is necessary for enhancing its efficacy in various applications. Coarse-grained (CG) simulations can be performed at length and time scales directly comparable with experimental data while maintaining the molecular perspective. However, manually developing CG forcefields for complex PEI structures is time-consuming and prone to human errors. This article presents a fully automated algorithm that can coarse-grain any branched architecture of PEI from its all-atom (AA) simulation trajectories and topology. The algorithm is demonstrated by coarse-graining a branched 2 kDa PEI, which can replicate the AA diffusion coefficient, radius of gyration, and end-to-end distance of the longest linear chain. Commercially available 25 and 2 kDa Millipore-Sigma PEIs are used for experimental validation. Specifically, branched PEI architectures are proposed, coarse-grained using the automated algorithm, and then simulated at different mass concentrations. The CG PEIs can reproduce existing experimental data on PEI's diffusion coefficient and Stokes-Einstein radius at infinite dilution as well as its intrinsic viscosity. This suggests a strategy where probable chemical structures of synthetic PEIs can be inferred computationally using the developed algorithm. The coarse-graining methodology presented here can also be extended to other polymers.
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Affiliation(s)
- Subhamoy Mahajan
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Tian Tang
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
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4
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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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5
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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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6
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Christofi E, Chazirakis A, Chrysostomou C, Nicolaou MA, Li W, Doxastakis M, Harmandaris VA. Deep convolutional neural networks for generating atomistic configurations of multi-component macromolecules from coarse-grained models. J Chem Phys 2022; 157:184903. [DOI: 10.1063/5.0110322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Despite the modern advances in the available computational resources, the length and time scales of the physical systems that can be studied in full atomic detail, via molecular simulations, are still limited. To overcome such limitations, coarse-grained (CG) models have been developed to reduce the dimensionality of the physical system under study. However, to study such systems at the atomic level, it is necessary to re-introduce the atomistic details into the CG description. Such an ill-posed mathematical problem is typically treated via numerical algorithms, which need to balance accuracy, efficiency, and general applicability. Here, we introduce an efficient and versatile method for backmapping multi-component CG macromolecules of arbitrary microstructures. By utilizing deep learning algorithms, we train a convolutional neural network to learn structural correlations between polymer configurations at the atomistic and their corresponding CG descriptions, obtained from atomistic simulations. The trained model is then utilized to get predictions of atomistic structures from input CG configurations. As an illustrative example, we apply the convolutional neural network to polybutadiene copolymers of various microstructures, in which each monomer microstructure (i.e., cis-1,4, trans-1,4, and vinyl-1,2) is represented as a different CG particle type. The proposed methodology is transferable over molecular weight and various microstructures. Moreover, starting from a specific single CG configuration with a given microstructure, we show that by modifying its chemistry (i.e., CG particle types), we are able to obtain a set of well equilibrated polymer configurations of different microstructures (chemistry) than the one of the original CG configuration.
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Affiliation(s)
- Eleftherios Christofi
- Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus
| | - Antonis Chazirakis
- Department of Mathematics and Applied Mathematics, University of Crete, Heraklion GR-71110, Greece
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology–Hellas, GR-71110 Heraklion, Crete, Greece
| | - Charalambos Chrysostomou
- Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus
| | - Mihalis A. Nicolaou
- Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus
| | - Wei Li
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo 001-0021, Japan
| | - Manolis Doxastakis
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA
| | - Vagelis A. Harmandaris
- Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus
- Department of Mathematics and Applied Mathematics, University of Crete, Heraklion GR-71110, Greece
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology–Hellas, GR-71110 Heraklion, Crete, Greece
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7
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Kanekal KH, Rudzinski JF, Bereau T. Broad chemical transferability in structure-based coarse-graining. J Chem Phys 2022; 157:104102. [DOI: 10.1063/5.0104914] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Compared to top-down coarse-grained (CG) models, bottom-up approaches are capable of offering higher structural fidelity. This fidelity results from the tight link to a higher-resolution reference, making the CG model chemically specific. Unfortunately, chemical specificity can be at odds with compound-screening strategies, which call for transferable parametrizations. Here we present an approach to reconcile bottom-up, structure-preserving CG models with chemical transferability. We consider the bottom-up CG parametrization of 3,441 C7O2 small-molecule isomers. Our approach combines atomic representations, unsupervised learning, and a large-scale extended-ensemble force-matching parametrization. We first identify a subset of 19 representative molecules, which maximally encode the local environment of all gas-phase conformers. Reference interactions between the 19 representative molecules were obtained from both homogeneous bulk liquids and various binary mixtures. An extended-ensemble parametrization over all 703 state points leads to a CG model that is both structure-based and chemically transferable. Remarkably, the resulting force field is on average more structurally accurate than single-state-point equivalents. Averaging over the extended ensemble acts as a mean-force regularizer, smoothing out both force and structural correlations that are overly specific to a single state point. Our approach aims at transferability through a set of CG bead types that can be used to easily construct new molecules, while retaining the benefits of a structure-based parametrization.
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Affiliation(s)
- Kiran H. Kanekal
- AK Kremer - Theory Group, Max Planck Institute for Polymer Research, Germany
| | | | - Tristan Bereau
- Van 't Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam, Netherlands
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8
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Wu Z, Müller-Plathe F. Slip-Spring Hybrid Particle-Field Molecular Dynamics for Coarse-Graining Branched Polymer Melts: Polystyrene Melts as an Example. J Chem Theory Comput 2022; 18:3814-3828. [PMID: 35617016 DOI: 10.1021/acs.jctc.2c00107] [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 topology of chains significantly modifies the dynamical properties of polymer melts. Here, we extend a recently developed efficient simulation method, namely the slip-spring hybrid particle-field (SS-hPF) model, to study the structural and dynamical properties of branched polymer melts over large spatial-temporal scales. In the coarse-grained SS-hPF simulation of polymers, the bonded potentials are derived by iterative Boltzmann inversion from the underlying fine-grained model. The nonbonded potentials are computed from a density functional field instead of pairwise interactions used in standard molecular dynamics simulations, which increases the computational efficiency by a factor of 10-20. The entangled dynamics is lost due to the soft-core nature of density functional field interactions. It is recovered by a multichain slip-spring model that is rigorously parametrized from existing experimental or simulation data. To quantitatively predict the relaxation and diffusion of branched polymers, which are dominated by arm retraction rather than chain reptation, the slip-spring algorithm is augmented to improve the polymer dynamics near the branch point. Multiple dynamical observables, e.g., diffusion coefficients, arm relaxations, and tube survival probabilities, are characterized in an example coarse-grained model of symmetric and asymmetric star-shaped polystyrene melts. Consistent dynamical behaviors are identified and compared with theoretical predictions. With a single rescaling factor, the prediction of diffusion coefficients agrees well with the available experimental measurements. In this work, an efficient approach is provided to build chemistry-specific coarse-grained models for predicting the dynamics of branched polymers.
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Affiliation(s)
- Zhenghao Wu
- Eduard-Zintl-Institut für Anorganische und Physikalische Chemie, Technische Universität Darmstadt, Alarich-Weiss-Strasse 8, 64287 Darmstadt, Germany
| | - Florian Müller-Plathe
- Eduard-Zintl-Institut für Anorganische und Physikalische Chemie, Technische Universität Darmstadt, Alarich-Weiss-Strasse 8, 64287 Darmstadt, Germany
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9
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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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10
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Rissanou A, Chazirakis A, Polinska P, Burkhart C, Doxastakis M, Harmandaris V. Polybutadiene Copolymers via Atomistic and Systematic Coarse-Grained Simulations. Macromolecules 2022. [DOI: 10.1021/acs.macromol.1c01939] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Anastassia Rissanou
- Institute of Applied and Computational Mathematics (IACM), Foundation for Research and Technology Hellas, (FORTH), IACM/FORTH, GR-71110 Heraklion, Greece
- Department of Mathematics and Applied Mathematics, University of Crete, GR-71409 Heraklion, Crete, Greece
| | - Antonis Chazirakis
- Institute of Applied and Computational Mathematics (IACM), Foundation for Research and Technology Hellas, (FORTH), IACM/FORTH, GR-71110 Heraklion, Greece
- Department of Mathematics and Applied Mathematics, University of Crete, GR-71409 Heraklion, Crete, Greece
| | | | - Craig Burkhart
- The Goodyear Tire and Rubber Company, 142 Goodyear Blvd., 44305 Akron, Ohio, United States
| | - Manolis Doxastakis
- Department of Chemical and Biomolecular Engineering, University of Tennessee, 37996 Knoxville, Tennessee, United States
| | - Vagelis Harmandaris
- Institute of Applied and Computational Mathematics (IACM), Foundation for Research and Technology Hellas, (FORTH), IACM/FORTH, GR-71110 Heraklion, Greece
- Department of Mathematics and Applied Mathematics, University of Crete, GR-71409 Heraklion, Crete, Greece
- Computation-Based Science and Technology Research Center, The Cyprus Institute, 2121 Nicosia, Cyprus
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11
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In‐noi O, Prasitnok K. A Coarse‐Grained Model for
cis
‐Polyisoprene: Thermal Expansion and Glass Transition Temperature. MACROMOL THEOR SIMUL 2022. [DOI: 10.1002/mats.202100083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Orrasa In‐noi
- Department of Chemistry Faculty of Science Mahasarakham University Mahasarakham 44150 Thailand
| | - Khongvit Prasitnok
- Department of Chemistry Faculty of Science Mahasarakham University Mahasarakham 44150 Thailand
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12
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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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13
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Li W, Jana PK, Behbahani AF, Kritikos G, Schneider L, Polińska P, Burkhart C, Harmandaris VA, Müller M, Doxastakis M. Dynamics of Long Entangled Polyisoprene Melts via Multiscale Modeling. Macromolecules 2021. [DOI: 10.1021/acs.macromol.1c01376] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Wei Li
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Pritam K. Jana
- Institute for Theoretical Physics, Georg-August University, 37077 Göttingen, Germany
| | - Alireza F. Behbahani
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Heraklion GR-71110, Greece
| | - Georgios Kritikos
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Ludwig Schneider
- Institute for Theoretical Physics, Georg-August University, 37077 Göttingen, Germany
| | | | - Craig Burkhart
- The Goodyear Tire & Rubber Company, Akron, Ohio 44305, United States
| | - Vagelis A. Harmandaris
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Heraklion GR-71110, Greece
- Department of Mathematics and Applied Mathematics, University of Crete, Heraklion GR-71110, Greece
- Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus
| | - Marcus Müller
- Institute for Theoretical Physics, Georg-August University, 37077 Göttingen, Germany
| | - Manolis Doxastakis
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
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14
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Giulini M, Rigoli M, Mattiotti G, Menichetti R, Tarenzi T, Fiorentini R, Potestio R. From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules. Front Mol Biosci 2021; 8:676976. [PMID: 34164432 PMCID: PMC8215203 DOI: 10.3389/fmolb.2021.676976] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 05/06/2021] [Indexed: 12/18/2022] Open
Abstract
The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail. However, the relevant length and time scales of many processes of interest are still hardly within reach even for state-of-the-art hardware, thus leaving important questions often unanswered. The computer-aided investigation of many biological physics problems thus largely benefits from the usage of coarse-grained models, that is, simplified representations of a molecule at a level of resolution that is lower than atomistic. A plethora of coarse-grained models have been developed, which differ most notably in their granularity; this latter aspect determines one of the crucial open issues in the field, i.e. the identification of an optimal degree of coarsening, which enables the greatest simplification at the expenses of the smallest information loss. In this review, we present the problem of coarse-grained modeling in biophysics from the viewpoint of system representation and information content. In particular, we discuss two distinct yet complementary aspects of protein modeling: on the one hand, the relationship between the resolution of a model and its capacity of accurately reproducing the properties of interest; on the other hand, the possibility of employing a lower resolution description of a detailed model to extract simple, useful, and intelligible information from the latter.
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Affiliation(s)
- Marco Giulini
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Marta Rigoli
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Giovanni Mattiotti
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Roberto Menichetti
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Thomas Tarenzi
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Raffaele Fiorentini
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Raffaello Potestio
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
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15
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Rudzinski JF, Kloth S, Wörner S, Pal T, Kremer K, Bereau T, Vogel M. Dynamical properties across different coarse-grained models for ionic liquids. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2021; 33:224001. [PMID: 33592598 DOI: 10.1088/1361-648x/abe6e1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 02/16/2021] [Indexed: 06/12/2023]
Abstract
Room-temperature ionic liquids (RTILs) stand out among molecular liquids for their rich physicochemical characteristics, including structural and dynamic heterogeneity. The significance of electrostatic interactions in RTILs results in long characteristic length- and timescales, and has motivated the development of a number of coarse-grained (CG) simulation models. In this study, we aim to better understand the connection between certain CG parameterization strategies and the dynamical properties and transferability of the resulting models. We systematically compare five CG models: a model largely parameterized from experimental thermodynamic observables; a refinement of this model to increase its structural accuracy; and three models that reproduce a given set of structural distribution functions by construction, with varying intramolecular parameterizations and reference temperatures. All five CG models display limited structural transferability over temperature, and also result in various effective dynamical speedup factors, relative to a reference atomistic model. On the other hand, the structure-based CG models tend to result in more consistent cation-anion relative diffusion than the thermodynamic-based models, for a single thermodynamic state point. By linking short- and long-timescale dynamical behaviors, we demonstrate that the varying dynamical properties of the different CG models can be largely collapsed onto a single curve, which provides evidence for a route to constructing dynamically-consistent CG models of RTILs.
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Affiliation(s)
| | - Sebastian Kloth
- Institute of Condensed Matter Physics, Technische Universität Darmstadt, Hochschulstr. 6, 64289 Darmstadt, Germany
| | - Svenja Wörner
- Max Planck Institute for Polymer Research, 55128 Mainz, Germany
| | - Tamisra Pal
- Institute of Condensed Matter Physics, Technische Universität Darmstadt, Hochschulstr. 6, 64289 Darmstadt, Germany
| | - Kurt Kremer
- Max Planck Institute for Polymer Research, 55128 Mainz, Germany
| | - Tristan Bereau
- Max Planck Institute for Polymer Research, 55128 Mainz, Germany
- Van 't Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam, Amsterdam 1098 XH, The Netherlands
| | - Michael Vogel
- Institute of Condensed Matter Physics, Technische Universität Darmstadt, Hochschulstr. 6, 64289 Darmstadt, Germany
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16
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Szukalo RJ, Noid WG. Investigating the energetic and entropic components of effective potentials across a glass transition. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2021; 33:154004. [PMID: 33498016 DOI: 10.1088/1361-648x/abdff8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/26/2021] [Indexed: 06/12/2023]
Abstract
By eliminating unnecessary details, coarse-grained (CG) models provide the necessary efficiency for simulating scales that are inaccessible to higher resolution models. However, because they average over atomic details, the effective potentials governing CG degrees of freedom necessarily incorporate significant entropic contributions, which limit their transferability and complicate the treatment of thermodynamic properties. This work employs a dual-potential approach to consider the energetic and entropic contributions to effective interaction potentials for CG models. Specifically, we consider one- and three-site CG models for ortho-terphenyl (OTP) both above and below its glass transition. We employ the multiscale coarse-graining (MS-CG) variational principle to determine interaction potentials that accurately reproduce the structural properties of an all-atom (AA) model for OTP at each state point. We employ an energy-matching variational principle to determine an energy operator that accurately reproduces the intra- and inter-molecular energy of the AA model. While the MS-CG pair potentials are almost purely repulsive, the corresponding pair energy functions feature a pronounced minima that corresponds to contacting benzene rings. These energetic functions then determine an estimate for the entropic component of the MS-CG interaction potentials. These entropic functions accurately predict the MS-CG pair potentials across a wide range of liquid state points at constant density. Moreover, the entropic functions also predict pair potentials that quite accurately model the AA pair structure below the glass transition. Thus, the dual-potential approach appears a promising approach for modeling AA energetics, as well as for predicting the temperature-dependence of CG effective potentials.
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Affiliation(s)
- Ryan J Szukalo
- Department of Chemistry, Penn State University, University Park, PA 16802 United States of America
| | - W G Noid
- Department of Chemistry, Penn State University, University Park, PA 16802 United States of America
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Behbahani AF, Schneider L, Rissanou A, Chazirakis A, Bačová P, Jana PK, Li W, Doxastakis M, Polińska P, Burkhart C, Müller M, Harmandaris VA. Dynamics and Rheology of Polymer Melts via Hierarchical Atomistic, Coarse-Grained, and Slip-Spring Simulations. Macromolecules 2021. [DOI: 10.1021/acs.macromol.0c02583] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Alireza F. Behbahani
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Heraklion GR-71110, Greece
| | - Ludwig Schneider
- Institute for Theoretical Physics, Georg-August University Göttingen, Goettingen 37077, Germany
| | - Anastassia Rissanou
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Heraklion GR-71110, Greece
| | - Anthony Chazirakis
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Heraklion GR-71110, Greece
| | - Petra Bačová
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Heraklion GR-71110, Greece
| | - Pritam Kumar Jana
- Institute for Theoretical Physics, Georg-August University Göttingen, Goettingen 37077, Germany
| | - Wei Li
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Manolis Doxastakis
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | | | - Craig Burkhart
- The Goodyear Tire and Rubber Company, 142 Goodyear Blvd., Akron, Ohio 44305, United States
| | - Marcus Müller
- Institute for Theoretical Physics, Georg-August University Göttingen, Goettingen 37077, Germany
| | - Vagelis A. Harmandaris
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Heraklion GR-71110, Greece
- Department of Mathematics and Applied Mathematics, University of Crete, Heraklion GR-71110, Greece
- Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus
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18
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Rudzinski JF, Bereau T. Coarse-grained conformational surface hopping: Methodology and transferability. J Chem Phys 2020; 153:214110. [DOI: 10.1063/5.0031249] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
| | - Tristan Bereau
- Max Planck Institute for Polymer Research, 55128 Mainz, Germany
- Van ’t Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam, Amsterdam 1098 XH, The Netherlands
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Kapoor U, Kulshreshtha A, Jayaraman A. Development of Coarse-Grained Models for Poly(4-vinylphenol) and Poly(2-vinylpyridine): Polymer Chemistries with Hydrogen Bonding. Polymers (Basel) 2020; 12:E2764. [PMID: 33238611 PMCID: PMC7709027 DOI: 10.3390/polym12112764] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/06/2020] [Accepted: 11/09/2020] [Indexed: 11/16/2022] Open
Abstract
In this paper, we identify the modifications needed in a recently developed generic coarse-grained (CG) model that captured directional interactions in polymers to specifically represent two exemplary hydrogen bonding polymer chemistries-poly(4-vinylphenol) and poly(2-vinylpyridine). We use atomistically observed monomer-level structures (e.g., bond, angle and torsion distribution) and chain structures (e.g., end-to-end distance distribution and persistence length) of poly(4-vinylphenol) and poly(2-vinylpyridine) in an explicitly represented good solvent (tetrahydrofuran) to identify the appropriate modifications in the generic CG model in implicit solvent. For both chemistries, the modified CG model is developed based on atomistic simulations of a single 24-mer chain. This modified CG model is then used to simulate longer (36-mer) and shorter (18-mer and 12-mer) chain lengths and compared against the corresponding atomistic simulation results. We find that with one to two simple modifications (e.g., incorporating intra-chain attraction, torsional constraint) to the generic CG model, we are able to reproduce atomistically observed bond, angle and torsion distributions, persistence length, and end-to-end distance distribution for chain lengths ranging from 12 to 36 monomers. We also show that this modified CG model, meant to reproduce atomistic structure, does not reproduce atomistically observed chain relaxation and hydrogen bond dynamics, as expected. Simulations with the modified CG model have significantly faster chain relaxation than atomistic simulations and slower decorrelation of formed hydrogen bonds than in atomistic simulations, with no apparent dependence on chain length.
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Affiliation(s)
- Utkarsh Kapoor
- Department of Chemical and Biomolecular Engineering, Colburn Laboratory, University of Delaware, 150 Academy Street, Newark, DE 19716, USA; (U.K.); (A.K.)
| | - Arjita Kulshreshtha
- Department of Chemical and Biomolecular Engineering, Colburn Laboratory, University of Delaware, 150 Academy Street, Newark, DE 19716, USA; (U.K.); (A.K.)
| | - Arthi Jayaraman
- Department of Chemical and Biomolecular Engineering, Colburn Laboratory, University of Delaware, 150 Academy Street, Newark, DE 19716, USA; (U.K.); (A.K.)
- Department of Materials Science and Engineering, University of Delaware, Newark, DE 19716, USA
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Li W, Burkhart C, Polińska P, Harmandaris V, Doxastakis M. Backmapping coarse-grained macromolecules: An efficient and versatile machine learning approach. J Chem Phys 2020; 153:041101. [DOI: 10.1063/5.0012320] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Wei Li
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA
| | - Craig Burkhart
- The Goodyear Tire and Rubber Company, Akron, Ohio 44305, USA
| | - Patrycja Polińska
- Goodyear Innovation Center Luxembourg, Avenue Gordon Smith, L-7750 Colmar-Berg, Luxembourg
| | - Vagelis Harmandaris
- Department of Applied Mathematics, University of Crete, and IACM FORTH, GR-71110 Heraklion, Greece
- Computation-Based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus
| | - Manolis Doxastakis
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA
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