Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data

Effective designs of biodegradable polymers are highly desirable for achieving a sustainable society by decreasing environmental burden and replacing petroleum-based resources with biomass. Low-field NMR is one of the candidate techniques because it provides information on the higher-order structure and dynamics of polymers quickly and conveniently. Although machine learning approaches such as Bayesian optimization (BO) and convolutional neural networks (CNNs) are significant, there have been almost no reports on effective material design based on low-field nuclear magnetic resonance (NMR) data. This study proposes a method for optimizing polymer process conditions using CNN-based features extracted from relaxation curves. This approach identified important features related to material properties while reconstructing denoised relaxation curves of polylactic acid. BO of process conditions using these features achieved an optimization rate comparable to using material property values, suggesting that effective material design is possible without directly evaluating a large number of properties. This might be potentially insightful for the feasibility of a framework to accelerate polymer development through low-field NMR with minimal property data.