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A machine learning approach to designing and understanding tough, degradable polyamides
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Machine learning models were applied to predict how microplastics adsorb organic pollutants from aquatic environments, identifying the key physicochemical factors that drive sorption behavior. The approach enables faster and more accurate prediction of microplastic-pollutant interactions than traditional experimental methods alone. These insights can guide risk assessments of microplastics as vectors for toxic contaminants in water systems.
The development of environmentally friendly plastics has received renewed attention for a sustainable society. Although the trade-off between toughness and degradability is a common challenge in biodegradable polymers, the design of biodegradable polymers to overcome these issues is often difficult. In this study, we demonstrated that machine learning techniques can contribute to the development of multiblock polyamides composed of Nylon6 and α-amino acid segments that are mechanically tough and degradable. Multi-objective optimization based on Gaussian process regression for the degradation rate, strain at break, and Young’s modulus (the last two parameters correspond to toughness) suggested appropriate α-amino acid sequences for polyamides endowed with both properties. Ridge regression revealed that the physical factors associated with the sequences, as well as the higher-order multiblock-derived structures (such as the crystal lattice structure, melting points, and hydrogen bonding), were essential for endowing these polymers with satisfactory properties among the multimodal measurement/calculation data. Our method provides a useful approach for designing and understanding environment-friendly plastics and other materials with multiple properties based on machine learning techniques.
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