Revealing Structural and Physical Properties of Polylactide: What Simulation Can Do beyond the Experimental Methods

Guseva, Daria V.; Glagolev, M. K.; Lazutin, Alexei А.; Vasilevskaya, V. V.

doi:10.1080/15583724.2023.2174136

Cited by 6 publications

(2 citation statements)

References 179 publications

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“…PLA consists of monomers with chiral centers, and therefore, it exists in three stereoisomeric forms: poly( l -lactide) acid (PLLA), poly( d -lactide) acid (PDLA) and poly( dl -lactide) acid (PDLLA). The popularity of PLA is also reflected in the high number of computational methods which have been used so far to describe the polymer structure at multiple length and time scales (see, e.g., the recent review by Vasilevskaya and co-workers). Regarding CG models of PLA, two types of mapping have been reported: “A- graft -B” mapping corresponding to two beads per monomer , and 1:1 mapping with one of the oxygens acting as the center of the CG bead .…”

Section: Introductionmentioning

confidence: 99%

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers

Christofi,

Bačová,

Harmandaris

2024

J. Chem. Inf. Model.

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Multiscale modeling of complex molecular systems, such as macromolecules, encompasses methods that combine information from fine and coarse representations of molecules to capture material properties over a wide range of spatiotemporal scales. Being able to exchange information between different levels of resolution is essential for the effective transfer of this information. The inverse problem of reintroducing atomistic degrees of freedom in coarse-grained (CG) molecular configurations is particularly challenging as, from a mathematical point of view, it is an ill-posed problem; the forward mapping from the atomistic to the CG description is typically defined via a deterministic operator (“one-to-one” problem), whereas the reversed mapping from the CG to the atomistic model refers to creating one representative configuration out of many possible ones (“one-to-many” problem). Most of the backmapping methods proposed so far balance accuracy, efficiency, and general applicability. This is particularly important for macromolecular systems with different types of isomers, i.e., molecules that have the same molecular formula and sequence of bonded atoms (constitution) but differ in the three-dimensional configurations of their atoms in space. Here, we introduce a versatile deep learning approach for backmapping multicomponent CG macromolecules with chiral centers, trained to learn structural correlations between polymer configurations at the atomistic level and their corresponding CG descriptions. This method is intended to be simple and flexible while presenting a generic solution for resolution transformation. In addition, the method is aimed to respect the structural features of the molecule, such as local packing, capturing therefore the physical properties of the material. As an illustrative example, we apply the model on linear poly(lactic acid) (PLA) in melt, which is one of the most popular biodegradable polymers. The framework is tested on a number of model systems starting from homopolymer stereoisomers of PLA to copolymers with randomly placed chiral centers. The results demonstrate the efficiency and efficacy of the new approach.

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Section: Introductionmentioning

confidence: 99%

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers

Christofi,

Bačová,

Harmandaris

2024

J. Chem. Inf. Model.

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“…In molecular simulations, force fields or potentials describing structure and interactions in model systems are critical constituents, which dictate some studies devoted to developing reliable all-atomistic (AA) and coarse-grained (CG) models. 4 Among those, coarse graining provides a promising route to simulate large-scale behaviors (i.e., mechanical deformations and glass transitions) of complex polymer systems (i.e., films and nanocomposites). The main focus in this work is placed on the volumetric properties, which are related to almost all other physical properties (including the T g and mechanical modulus).…”

Section: Introductionmentioning

confidence: 99%

Temperature-Transferable Coarse-Grained Models for Volumetric Properties of Poly(lactic Acid)

2023

J. Phys. Chem. B

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A new coarse-grained (CG) model, for which each monomer is mapped as one bead at its center of mass, was developed for simulating the volumetric properties of the polylactide (PLA) bulk. The three bonded CG potentials are first parametrized against the strain energies of the dimer, trimer, and tetramer, and the nonbonded CG potentials are then optimized to match the melt densities of the decamer. With the derived CG potentials, molecular dynamics (MD) simulations are found to reproduce thermal expansion and glass transition. By rescaling the dihedral and nonbonded potentials with temperatureindependent factors, the glass transition temperature (T g ) is also satisfactorily restored with little modifications on the volumetric expansive coefficients at both rubbery and glassy states. Therefore, the finally optimized CG potentials exhibit excellent temperature transferability, as rationalized by the Simha-Boyer relation. Furthermore, it is confirmed that the dihedral torsions and nonbonded interactions play key roles in glass transition. Also, the simulated bulk moduli and conformational properties in a wide temperature range compare well with the referenced data. The proposed multiscale scheme has great potential in simulating thermo-mechanical properties of PLA and other polymers.

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UV-shielding, flexible and enhanced thermal-conductive polylactide composites modified with single-layered, large-sized MXene nano-sheets

Qiang,

Qi,

Gao

et al. 2024

Polym. Bull.

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Revealing Structural and Physical Properties of Polylactide: What Simulation Can Do beyond the Experimental Methods

Cited by 6 publications

References 179 publications

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers

Temperature-Transferable Coarse-Grained Models for Volumetric Properties of Poly(lactic Acid)

UV-shielding, flexible and enhanced thermal-conductive polylactide composites modified with single-layered, large-sized MXene nano-sheets

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