Convergence of high throughput experimentation and machine learning to rapidly advance application-specific polymer development

Abstract

Machine learning (ML) has heavily influenced the way scientific study is done with demonstrated successes in nearly every field. However, furthering performance and explainability of ML models in increasingly complex systems and with increasingly demanding outcomes requires a significant influx of high-quality data. To that end, this Review covers some of the techniques, instrumentation, and methodologies that have shown promise for significantly accelerating the discovery of polymer materials, optimization of their properties, and elucidation of property-application relationships through high throughput (HT) experimentation, characterization, and analysis. Attention is given to not only ML, but also to hardware advancements and their synergy with computational tools. Multiple studies are highlighted that demonstrate effective implementation of HT synthetic, data acquisition, or analytical methods, often with full integration of ML for the creation of fully autonomous workflows. We present our outlook and perspectives on the incorporation of HT techniques for the discovery and study of polymer materials within a broad range of applications, and include practical considerations for implementing HT methods at the laboratory scale.