Improving recyclability of polymers: machine learning helps finding needle in haystack
Sid Kumar (Photo: TU Delft)
March 17, 2025 – Polymers are everywhere in modern life, from cars to mobile phones, but their ubiquity comes at a steep cost. Only 9% of all polymers are being recycled, a figure that urgently needs to improve, says TU Delft Associate Professor Sid Kumar. He is leveraging machine learning to find and design polymers that are better recyclable, paving the way for a more sustainable future.
Their recent development is an advanced machine learning algorithm that requires only a small amount of input data to discover new polymers. Additionally, the algorithm is designed to be interpretable and explainable, ensuring collaboration between AI and human scientists.
The team tested their algorithm on vitrimers – a new class of healable polymers. Vitrimers combine durability with end-of-life recyclability, offering a promising solution to plastic waste. These polymers can repair themselves when heated, thanks to their unique molecular bonds.
However, commercially available vitrimers are limited by the scarcity of suitable molecular building blocks, which restricts their self-healing properties and broader applications. Kumar’s team set a target temperature for self-healing and used their algorithm to identify promising molecular candidates. What once could have taken years—or remained impossible—was achieved in just days.
The results of the research were published on by under the title ‘AI-Guided Inverse Design and Discovery of Recyclable Vitrimeric Polymers’.
