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.

Knowledge representation systems for enabling AI-guided polymerization catalyst design and validation

Where thousands of chemistry professionals meet to share ideas and advance scientific and technical knowledge. Spring 2023 abstracts, posters, and presentations will launch at the end of March.

ACS Spring 2023

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.