Chemically-Informed Machine Learning Approach for Prediction of Reactivity Ratios in Radical Copolymerization

TL;DR

This paper introduces a hybrid computational approach combining spectral clustering and neural networks to efficiently predict reactivity ratios in radical copolymerization, aiding polymer design.

Contribution

It presents a novel integration of unsupervised spectral clustering with supervised neural networks for reactivity ratio prediction, improving accuracy and interpretability.

Findings

Spectral clustering identified three monomer groups with distinct reactivity patterns.

Cluster-specific neural network training improved prediction accuracy for targeted chemical domains.

Models trained on full datasets outperformed specialized models, highlighting a trade-off between specificity and data availability.

Abstract

Predicting monomer reactivity ratios is crucial for controlling monomer sequence distribution in copolymers and their properties. Traditional experimental methods of determining reactivity ratios are time-consuming and resource-intensive, while existing computational methods often struggle with accuracy or scalability. Here, we present a method that combines unsupervised learning with artificial neural networks to predict reactivity ratios in radical copolymerization. By applying spectral clustering to physicochemical features of monomers, we identified three distinct monomer groups with characteristic reactivity patterns. This computationally efficient clustering approach revealed specific monomer group interactions leading to different sequence arrangements, including alternating, random, block, and gradient copolymers, providing chemical insights for initial exploration. Building upon these insights, we trained artificial neural networks to achieve quantitative reactivity ratio predictions. We explored two integration strategies including direct feature concatenation, and cluster-specific training, which demonstrated performance enhancements for targeted chemical domains compared to general training with equivalent sample sizes. However, models utilizing complete datasets outperformed specialized models trained on focused subsets, revealing a fundamental trade-off between chemical specificity and data availability. This work demonstrates that unsupervised learning offers rapid chemical insight for exploratory analysis, while supervised learning provides the accuracy necessary for final design predictions, with optimal strategies depending on data availability and application requirements.