Improved Knowledge Representation Enables AI-Guided Polymer Design and Experimental Validation

Abstract

The delivery of actionable predictions or hypothesis is a key outcome of any predictive modeling effort for experimental science. Within the context of polymer chemistry—where experimentalists must navigate a highly complex design space—effective knowledge representation of experimental becomes critical to create useful models with actionable predictions. Here, we detail our efforts on the development of new, extensible open-source tools to enable experimentalists to accurately represent experimental data and facilitate its consumption in AI/ML or informatics pipelines. Moreover, we will demonstrate how these tools enabled the development and validation of generative models for new polymerization catalysts and how these generated catalysts can be repurposed in adjacent application areas within small-molecule organic chemistry. Finally, the integration of these toolkits within a growing ecosystem of open-source platforms polymer AI/ML and polymer data repositories will be discussed.