Poster: Polymer Informatics Tools for Sustainable Polymers and 3D printing

Poster presented at 2nd Spanish Soft Matter 1 1/2 Day (https://www.benasque.org/2024ssm/) which was held in Benasque, Spain from November 3 to November 6, 2024. Abstract: Biodegradable polymers are intended to replace the synthetically produced polymers, however, their wider usage is limited by their moderate thermal and mechanical properties. In order to elucidate the structure-properties-performance relationship of these materials, we design a bottom-up approach, combining multiple computational tools, with the ultimate goal to facilitate their characterization and property optimization during the additive manufacturing. We use the poly(lactic acid) (PLA) as the model polymer, which due to its chirality and wide popularity in 3D printing represents an ideal candidate to test the developed methodology. In the first stage of the bottom-up approach, we produced an extensive data set consisting of atomistic trajectories of different stereoisomers of PLA in melt. We used those data to build a chemistry-specific coarse-grained (CG) model to extend the time and length scales to those relevant in the experimental studies. In addition, to close the loop, we implement a machine-learning based methodology to reinsert the atomistic details into CG models of different stereochemistry [1]. Since the computational techniques are considered to be a more sustainable alternative to the experimental characterization, we aim to extend the simulation practices commonly used for synthetic polymers to more complex bio-based polymers. By combining different computational techniques, we provide a consistent set of open-access tools [2,3] with the ultimate goal to facilitate the usage of multiscale computational analysis in the fast-growing field of biodegradable materials and additive manufacturing. [1]A Physics-informed Deep Learning Approach for Re-introducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic Acid) Stereoisomers, E. Christofi, P. Bačová, V. Harmandaris, Journal of Chemical Information and Modeling, 2024 64 (6), 1853-1867 [2]https://github.com/SimEA-ERA/PLABackMap-CG[3]https://github.com/pbacova/PLA_analysis_tools.git