Linear viscoelastic shear and bulk relaxation moduli in poly(tetramethylene oxide) (PTMO) using united-atom molecular dynamics

Shireen, Zakiya; Hajizadeh, Elnaz; Daivis, Peter J.; Brandl, Christian

doi:10.1016/j.commatsci.2022.111824

Cited by 5 publications

(4 citation statements)

References 80 publications

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“…The UA potential used in the reference system is the Transferable Potentials for Phase Equilibria-UA (TraPPE-UA) for the intra (bonded) and inter-molecular (nonbonded) interactions initially developed by Lempesis et al 3 . Shireen et al further validated and established the TraPPE-UA potentials to study shear and bulk relaxation behavior of melt PTMO 70 . Before collecting data, the system was equilibrated at T = 453 K for 5 ns in a canonical ensemble using the deterministic Nose-Hoover thermostat 71,72 .…”

Section: Simulation Detailsmentioning

confidence: 99%

A machine learning enabled hybrid optimization framework for efficient coarse-graining of a model polymer

et al. 2022

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This work presents a framework governing the development of an efficient, accurate, and transferable coarse-grained (CG) model of a polyether material. The framework combines bottom-up and top-down approaches of coarse-grained model parameters by integrating machine learning (ML) with optimization algorithms. In the bottom-up approach, bonded interactions of the CG model are optimized using deep neural networks (DNN), where atomistic bonded distributions are matched. In the top-down approach, optimization of nonbonded parameters is accomplished by reproducing the temperature-dependent experimental density. We demonstrate that developed framework addresses the thermodynamic consistency and transferability issues associated with the classical coarse-graining approaches. The efficiency and transferability of the CG model is demonstrated through accurate predictions of chain statistics, the limiting behavior of the glass transition temperature, diffusion, and stress relaxation, where none were included in the parametrization process. The accuracy of the predicted properties are evaluated in context of molecular theories and available experimental data.

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Section: Simulation Detailsmentioning

confidence: 99%

A machine learning enabled hybrid optimization framework for efficient coarse-graining of a model polymer

et al. 2022

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“…Conventionally, reaction limited cluster aggregation (RLCA) is distinguished from DLCA with m ∝ R g d f = 2.0 and N ( m ) ∝ m –τ=1.0 . To differentiate the structures observed in RLCA systems and lattice animals in this study, we further analyze the chemical distance (similar to the end-to-end distance in polymer models 38 ) in A clusters at all flexibility values. The chemical distance l is the length of the shortest path between two monomers of a cluster.…”

Section: Resultsmentioning

confidence: 99%

Rigidity-Induced Controlled Aggregation of Binary Colloids

Shireen,

Curk,

Brandl

et al. 2023

ACS Omega

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Here, we report the proof-of-concept for controlled aggregation in a binary colloidal system. The binary systems are studied by varying bond flexibility of only one species, while the other species’ bonds remain fully flexible. By establishing the underlying relation between gelation and bond rigidity, we demonstrate how the interplay among bond flexibility, critical concentration, and packing volume fraction influenced the aggregation kinetics. Our result shows that rigidity in bonds increases the critical concentration for gels to be formed in the binary mixture. Furthermore, the average number of bonded neighbor analyses reveal the influence of bond rigidity both above and below critical concentrations and show that variation in bond flexibility in only one species alters the kinetics of aggregation of both species. This finding improves our understanding of colloidal aggregation in soft and biological systems.

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“…We note that these systems are strongly dynamically arrested due to the lack of rearrangements in local and mesoscale order rather than chain-scale dynamics. Particle-based simulations often cannot reach the required time scales to fully access the relaxation dynamics of entangled linear polymer melts, − but simulations of typical entangled melts show a slow decay on G ( t ). However, in these SCLCP systems at low T , the G ( t ) curves are relatively flat without a transitional regime (the modulus change with time is subtle) compared to entangled linear polymer melts due to the morphology constraints imposed by LC moieties. , …”

Section: Resultsmentioning

confidence: 99%

“…At lower temperatures (T < 2.0 ε/k B ), there is no transitional behavior observed at intermediate time scales, unlike in entangled linear polymer melts. 61,63 Instead, the relaxation modulus spectra display a flat profile with a power law close to 0, extending to longer time scales. This highly non-Newtonian behavior is attributed to the slow dynamics resulting from the formation of crystalline networks or, in the case of systems with random LC sequences, to chains' arrested motion due to highly disrupted structures (Figure 4).…”

mentioning

confidence: 96%

Impact of Molecular-level Structural Disruption on Relaxation Dynamics of Polymers with End-on and Side-on Liquid Crystal Moieties

Becerra,

Xu,

Wang

et al. 2023

ACS Nano

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Linear viscoelastic shear and bulk relaxation moduli in poly(tetramethylene oxide) (PTMO) using united-atom molecular dynamics

Cited by 5 publications

References 80 publications

A machine learning enabled hybrid optimization framework for efficient coarse-graining of a model polymer

A machine learning enabled hybrid optimization framework for efficient coarse-graining of a model polymer

Rigidity-Induced Controlled Aggregation of Binary Colloids

Impact of Molecular-level Structural Disruption on Relaxation Dynamics of Polymers with End-on and Side-on Liquid Crystal Moieties

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