Invited talk presented at Summer School and Workshop on Rheology, RheoSamos2024 (https://mathweb.aegean.gr/rheosamos2024/index.php), which took place in Karlovassi, Samos, Greece from 5th to 13th 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. Combination of the computational tools with the rheological measurements would certainly mean a step forward in elucidating this relationship, however, apart from the lack of published data on well-defined samples, the thermal stability of biodegradable polymers is a considerable issue. Here we present a systematic simulation study which aims to connect the bottom-up computational approach with rheological data in order to facilitate the development of new functional sustainable materials for additive manufacturing. We chose poly(lactic acid) as the best candidate to test our methodology. We performed atomistic simulations of PLA samples with a wide range of molecular weights and stereochemistry. 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 https://pubs.acs.org/doi/10.1021/acs.jcim.3c01870[2] https://github.com/SimEA-ERA/PLA-BackMap-CG[3] https://github.com/pbacova/PLA_analysis_tools.git