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Talk: Polymer Informatics Tools for Sustainable Practices

Bacova, Petra; Christofi, Eleftherios; Harmandaris, Vagelis; Molina, Sergio I.

Publisher
Zenodo
DOI
10.5281/zenodo.17290363
Repository
zenodo.org
Language
en

Abstract

Oral contribution presented at IUPAC Macro 2024 World Polymer Congress (https://www.macro2024.org/) which took place at the University of Warwick, UK on 1st-4th of July 2024. Abstract: In pursuance of making the polymer industry more sustainable, computational studies have been enriched with biodegradable polymers. However, owing to their complex structure resembling proteins, investigating the structure-properties-performance relationship of these polymers by simulations is still challenging.We present a systematic bottom-up approach starting from the atomistic description and involving multiple computational techniques. We focus on poly(lactic acid) due to its wide usage in additive manufacturing. We build a chemistry-specific coarse-grained (CG) model to extend the time and length scales to those relevant for experimental studies. To close the loop, we reinsert the atomistic details into CG models by applying a machine-learning based methodology.Since the computational techniques are considered to be a more sustainable alternative to the experimental characterization, the presented generic and open-access methodology aims to facilitate the digital polymer design.

Full text

Polymer Informatics Tools for Sustainable Practices Petra Baˇ cová, Eleftherios Christofi, Vagelis Harmandaris, Sergio I. Molina Departamento de Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica, Facultad de Ciencias, IMEYMAT, Campus Universitario Río San Pedro s/n., Puerto Real, Cádiz 11510, Spain PITS3D [1,2] * [1] https://adicork.es/index.php/sectores/; [2] Created with BioRender.com PITS3D Poly(lactic acid) in additive manufacturing 3widely popular, easy to print 3low melting point, no heated tray 8sensitive to sunlight and high temperatures 8commercial samples of unknown composition úLvs. Dstereoisomer úadditives, polydisperse samples úempirical approach to nanocomposite design PITS3D Systematic bottom-up simulation approach Computational methods: (A) quantum: force field (B) atomistic: chemistry-specific interactions (C) coarse-grained: trends at mesoscale (D) continuum (A) (B) (C) (D) time length Multiscale: 3quantitative predictions of macroscopic properties 8time and resourse intensive [1] Guseva, D. V.; Glagolev, M. K.; Lazutin, A. A.; Vasilevskaya, V. V.; Polym. Rev. 2023, 0, 1–39 PITS3D Computer design: reproducing experiment A. F. Behbahani et al., Macromolecules 2021 54 (6), 2740-2762 PITS3D Computer design: backmapping strategy Eleftherios Christofi et al., J. Chem. Phys. 2022 157 (18), 184903 PITS3D Challenges and motivation 8biodegradable polymers similar to proteins: slow structural rearrangements 8H-bonds: atomistic detail 8chirality: random Land Dcontent 8lack of open-access data for data-driven approaches 3extensive atomistic data set which can be used for systematic bottom-up and/or data-driven approaches 8lack of experimental reference, monodisperse and well-defined systems 3theoretical and experimental rheological data on reference systems to determine printability PITS3D Systems under investigation Ïmolecular dynamics simulations: atomistic and coarse-grained Ïnon-entangled melts Ï500K, 1 atm Label Mw Microstructure [g/mol] PLLA PDLA PLLA100 7.2 k 100%0% PDLA100 7.2 k 0%100% Copo100 7.2 k 45%55% PLLA30 2.2 k 100%0% PDLA30 2.2 k 0%100% Copo30 2.2 k 84%16% PITS3D Backmapping procedure (A) atomistic dataset, 5000 frames per system, training set: PLLA100, PDLA100 and Copo100 (B) encoding and learning úconvolutional neural network úversatile: bond vectors and lengths úlocal: no intermonomeric and intermolecular information (C) coarse-graining, monomer-like representation (D) backmapping úverify the model útesting the transferability (A) (B) (C) (D)