Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation

Poly(ethylene terephthalate) (PET) is a prevalent single-use plastic, posing a significant threat to the environment and human health due to its nondegradable properties. To confront this pressing issue, there is an urgent need for a method of PET degradation that is not only efficient but also cost-effective. Recognizing the intricate interactions of parameters, we propose an innovative approach that integrates two-step machine learning (ML) techniques to achieve our objective while minimizing experimental costs. A dataset comprising 120 distinct PET degradation conditions was created using high-throughput experimentation (HTE). Initially, eight ML algorithms were employed to train and predict performance, with the decision tree model yielding the most favorable outcomes. Subsequently, the trained ML model was expanded to encompass an extensive array of hypothetical reaction conditions, facilitating the identification of degradation formulations that exhibit exceptional performance. Additionally, through the integration of feature importance analysis, we systematically reconstruct a relevant chemical space. Following regression training and prediction, we identified reaction conditions with significantly higher degradation rates at ambient temperature. The utilization of these conditions not only enhances the efficacy of research and development, leading to reduced experimental costs, but also provides valuable insights for future investigations into PET degradation. In contrast to conventional, resource-intensive trial-and-error approaches, our established platform facilitates the assessment of PET degradation rates across diverse reaction conditions, enabling a preliminary screening for process optimization. Consequently, this approach contributes to the mitigation of solid waste pollution and the advancement of sustainable economic development.