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Machine Learning Methods for Mineralization-Based Biodegradation Prediction in Polyhydroxyalkanoate-Based Biopolymers: Insights from Lab-Scale Experiments.

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Machine Learning Prediction of Thermal Properties of PHB/PHBV-Based Materials: A Quantitative Structure-Property

Nikolaos P Sotiropoulos1, Leonidas Mindrinos1, Jean-David Peltier2

  • 1Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.

Polymers
|July 15, 2026
PubMed
Summary

This study created a dataset of polyhydroxyalkanoates (PHAs) and used machine learning to predict thermal properties like glass transition temperature (Tg), melting temperature (Tm), and crystallization temperature (Tc) for sustainable polymer design.

Keywords:
Poly(3-hydroxybutyrate)Poly(3-hydroxybutyrate-co-3-hydroxyvalerate)machine learningpolymer informaticsquantitative structure–activity relationship modelsthermal properties

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Area of Science:

  • Polymer Science
  • Materials Science
  • Computational Chemistry

Background:

  • Bio-based and biodegradable polymers like poly(3-hydroxybutyrate) (PHB) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) have broad applications.
  • High production costs and difficulty in tailoring thermal properties (Tg, Tm, Tc) limit their wider adoption.

Purpose of the Study:

  • To develop a comprehensive dataset of PHB and PHBV materials.
  • To utilize machine learning models for predicting the thermal properties (Tg, Tm, Tc) of these polymers.
  • To identify key features influencing polymer thermal behavior for rational design.

Main Methods:

  • Compiled a dataset of 572 instances of PHB and PHBV properties from literature and experiments.
  • Applied feature engineering to integrate chemical, physical, and experimental variables.
  • Trained and evaluated Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models to predict Tg, Tc, and Tm.

Main Results:

  • Achieved high predictive accuracy with R² values of 0.77 for Tg, 0.76 for Tc, and 0.82 for Tm.
  • Identified optimal feature sets for predicting each thermal property.
  • Demonstrated reliable prediction of thermal properties for short-chain-length polyhydroxyalkanoates (scl-PHAs).

Conclusions:

  • Curated polymer datasets and interpretable machine learning models can aid in the design of sustainable polymers.
  • The study highlights the potential for ML in tailoring polymer properties for specific applications.
  • Addressing data completeness and size limitations can further enhance predictive capabilities.