Talk: Bottom-up approach for biodegradable polymers used in additive manufacturing: building computational tools to bridge the gaps

Oral contribution presented at Annual European Rheology Conference 2024 (https://rheology-esr.org/aercs/aerc-2024/welcome/) which took place from 9th to 12th of April 2024 in Leeds, UK. 

Abstract:

In the family of biodegradable polymers, poly(lactic acid) (PLA) occupies a special place among those most popular, mainly due to its versatile usage in additive manufacturing.

Despite being commercially available at low price, its usage as a full replacement of the synthetic polymers is hindered by its poor mechanical properties and thermal stability.

In order to be able to tackle fundamental problems related to the structure-properties-performance relationship, we present a systematic simulation study of PLA of a wide range of molecular weights and stereochemistry. More specifically, we analyze the basic structural and dynamical properties at atomistic level and 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/PLA-BackMap-CG

[3] https://github.com/pbacova/PLA_analysis_tools.git