A data-driven approach to control stimulus responsivity of functional polymer materials: Predicting thermoresponsive color-change properties of polydiacetylene

Abstract

Sensing devices are fabricated using stimuli-responsive materials. In general, the responsivity is controlled by designing molecules and materials based on professional experience. If predictors are constructed for the responsivity control, the number of experiments can be reduced without consumption of time, cost, and effort. However, such dynamic properties of functional polymer materials are not easily predicted because of the small data and complex structure-function relationship. How to prepare dataset and train small data remain significant challenges. The present work shows construction and application of a prediction model for controlling thermoresponsive color-changing properties of layered polydiacetylenes (PDAs). The responsivity was changed by the intercalated guest molecules. The training dataset was prepared from a series of the photographs representing the color at each temperature. The prediction model of the thermoresponsivity, namely color-changing temperature, was constructed by combining machine learning and our chemical insight on the small experimental data. The thermoresponsivity of the newly synthesized layered PDAs was predicted by the model. The modeling methods can be applied to predicting various dynamic properties of functional polymer materials.