Predicting the Composition and Mechanical Properties of Seaweed Bioplastics from the Scientific Literature: A Machine Learning Approach for Modeling Sparse Data

Abstract

The design of biodegradable polymeric materials is of increasing scientific interest due to accelerating levels of plastics pollution. One area of increasing interest is the design of biodegradable polymer films based on seaweed as a raw material. The goal of the study is to explore whether machine learning techniques can be used to predict the properties of unknown compositions based on existing data from the literature. Clustering algorithms are used, which show how some ingredients components at certain concentration levels alter the mechanical properties of the films. Robust regression algorithms with three popular models, namely decision tree, random forest, and gradient boosting. Their predictive capabilities are compared, resulting in the random forest algorithm being the most stable with the greatest predictive capacity. These analyses offer a decision support system for biomaterials manufacturing and experimentation. The results and conclusions of the study indicate that bioplastics made from seaweed have promising potential as a sustainable alternative to traditional plastics, discovering interesting additives to improve the performance of biopolymers. In addition, the machine learning approaches used provide effective tools for analyzing and predicting the properties of these materials in structured but highly sparse data.