Reducing Data Requirements for Sequence–Property Prediction in Copolymer Compatibilizers via Deep Neural Network Tuning

Abstract

Synthetic sequence-controlled polymers promise to transform polymer science by combining the chemical versatility of synthetic polymers with the precise sequence-mediated functionality of biological proteins. However, design of these materials has proven extraordinarily challenging, because they lack the massive data sets of closely related evolved molecules that accelerate design of proteins. Here, we report on an Artificial Intelligence strategy to reduce the amount of data necessary to accelerate the design of these materials. We focus on data connecting the repeat-unit-sequence of a model compatibilizer molecule to its ability to reduce the interfacial tension between distinct polymer domains. The optimal sequence of these molecules, which are essential for applications such as mixed-waste polymer recycling, depends strongly on variables such as concentration and chemical details of the polymer. We show that a deep neural network trained on low-fidelity data for sequence/interfacial tension relations at one set of conditions can be rapidly tuned to make higher-fidelity predictions at a distinct set of conditions, requiring far less data than would ordinarily be needed within the data sets we have tested. This priming using a pretrained model and then tuning it on unseen data from a different data set approach could allow a single low-fidelity parent data set to accelerate prediction and design in a larger set of related systems.