Our analytically based technique for coarse-graining (CG) polymer simulations dramatically improves spatial and temporal scaling while preserving thermodynamic quantities and bulk properties. The purpose of CG codes is to run more efficient molecular dynamics simulations, yet the research field generally lacks thorough analysis of how such codes scale with respect to full-atom representations. This paper conducts an in-depth performance study of highly realistic polymer melts on modern supercomputing systems. We also present a workflow that integrates our analytical solution for calculating CG forces with new high-performance techniques for mapping back and forth between the atomistic and CG descriptions in LAMMPS. The workflow benefits from the performance of CG, while maintaining full-atom accuracy. Our results show speedups up to 12x faster than atomistic simulations.
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Coarse-graining simulation approaches for polymer melts: the effect of potential range on computational efficiency
The integral equation coarse-graining (IECG) approach is a promising high-level coarse-graining (CG) method for polymer melts, with variable resolution from soft spheres to multi CG sites, which preserves the structural and thermodynamical consistencies with the related atomistic simulations. When compared to the atomistic description, the procedure of coarse-graining results in smoother free energy surfaces, longer-ranged potentials, a decrease in the number of interaction sites for a given polymer, and more. Because these changes have competing effects on the computational efficiency of the CG model, care needs to be taken when studying the effect of coarse-graining on the computational speed-up in CG molecular dynamics simulations. For instance, treatment of long-range CG interactions requires the selection of cutoff distances that include the attractive part of the effective CG potential and force. In particular, we show how the complex nature of the range and curvature of the effective CG potential, the selection of a suitable CG timestep, the choice of the cutoff distance, the molecular dynamics algorithms, and the smoothness of the CG free energy surface affect the efficiency of IECG simulations. By direct comparison with the atomistic simulations of relatively short chain polymer melts, we find that the overall computational efficiency is highest for the highest level of CG (soft spheres), with an overall improvement of the computational efficiency being about 10 6 –10 8 for various CG levels/resolutions. Therefore, the IECG method can have important applications in molecular dynamics simulations of polymeric systems. Finally, making use of the standard spatial decomposition algorithm, the parallel scalability of the IECG simulations for various levels of CG is presented. Optimal parallel scaling is observed for a reasonably large number of processors. Although this study is performed using the IECG approach, its results on the relation between the level of CG and the computational efficiency are general and apply to any properly-constructed CG model.
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- Award ID(s):
- 1665466
- NSF-PAR ID:
- 10105624
- Date Published:
- Journal Name:
- Soft Matter
- Volume:
- 14
- Issue:
- 35
- ISSN:
- 1744-683X
- Page Range / eLocation ID:
- 7126 to 7144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Bottom‐up prediction of physical performance of glass‐forming (GF) polymers via coarse‐grained (CG) modeling is challenging because these CG models normally experience significantly altered dynamics that strongly vary with temperature. Building upon the recently developed energy‐renormalization (ER) coarse‐graining method based on molecular dynamics simulations, generalized entropy theory (GET) is employed to theoretically investigate the influence of fundamental molecular parameters on CG modeling of polymers having different glass “fragilities” Taking a linear polymer melt as a model system within the GET framework, it is shown that the chain bending rigidity and cohesive interaction play critical roles in the glass formation of polymers and their CG analogs. To coarse‐grain polymers having a higher fragility index, it requires greater magnitudes of ER factor εCGto rescale the cohesive interaction strength under coarse‐graining and thus recover the atomistic relaxation dynamics over a wide temperature range. The GET further predicts that a higher degree of coarse‐graining generally requires greater magnitudes of εCGdue to the influence of loss of configuration entropy
s con the dynamics. GET analyses herein theoretically demonstrate the efficacy of the ER method toward building a multiscale temperature transferable modeling framework for GF polymers, and confirm the importance of preservings cin CG modeling of dynamics of soft materials. -
null (Ed.)We developed a new coarse-grained (CG) molecular dynamics force field for polyacrylamide (PAM) polymer based on fitting to the quantum mechanics (QM) equation of state (EOS). In this method, all nonbond interactions between representative beads are parameterized using a series of QM-EOS, which significantly improves the accuracy in comparison to common CG methods derived from atomistic molecular dynamics. This CG force-field has both higher accuracy and improved computational efficiency with respect to the OPLS atomistic force field. The nonbond components of the EOS were obtained from cold-compression curves on PAM crystals with rigid chains, while the covalent terms that contribute to the EOS were obtained using relaxed chains. For describing PAM gels we developed water–PAM interaction parameters using the same method. We demonstrate that the new CG-PAM force field reproduces the EOS of PAM crystals, isolated PAM chains, and water–PAM systems, while successfully predicting such experimental quantities as density, specific heat capacity, thermal conductivity and melting point.more » « less
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Bottom-up coarse-grained (CG) molecular dynamics models are parameterized using complex effective Hamiltonians. These models are typically optimized to approximate high dimensional data from atomistic simulations. However, human validation of these models is often limited to low dimensional statistics that do not necessarily differentiate between the CG model and said atomistic simulations. We propose that classification can be used to variationally estimate high dimensional error and that explainable machine learning can help convey this information to scientists. This approach is demonstrated using Shapley additive explanations and two CG protein models. This framework may also be valuable for ascertaining whether allosteric effects at the atomistic level are accurately propagated to a CG model.
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Abstract A persistent challenge in molecular modeling of thermoset polymers is capturing the effects of chemical composition and degree of crosslinking (DC) on dynamical and mechanical properties with high computational efficiency. We established a coarse-graining (CG) approach combining the energy renormalization method with Gaussian process surrogate models of molecular dynamics simulations. This allows a machine-learning informed functional calibration of DC-dependent CG force field parameters. Taking versatile epoxy resins consisting of Bisphenol A diglycidyl ether combined with curing agent of either 4,4-Diaminodicyclohexylmethane or polyoxypropylene diamines, we demonstrated excellent agreement between all-atom and CG predictions for density, Debye-Waller factor, Young’s modulus, and yield stress at any DC. We further introduced a surrogate model-enabled simplification of the functional forms of 14 non-bonded calibration parameters by quantifying the uncertainty of a candidate set of calibration functions. The framework established provides an efficient methodology for chemistry-specific, large-scale investigations of the dynamics and mechanics of epoxy resins.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/C8SM00868J
