(649b) Getting More out of Your Kinetics Experiments: Democratization of Digital Twins and Fairification of Data Assets

Exploration and optimization of reaction kinetics is critical in designing synthesis steps for small molecules. In silico modeling tools have been well established in allowing scientists to predict the behavior of these reaction systems. However, in practice, these modeling tools are often siloed with only a few power-users able to fully leverage their capabilities.

Additionally, kinetics experimentation is often coupled with tedious and manual data processing tasks. With a lack of controlled data storage, experimental data is susceptible to potential transcription errors, lack of visibility, and data loss. Enterprise Data Lakes have attempted to address this issue. However, these data lakes can often be fragile and not user-friendly enough to reach the majority scientists and engineers.

In the current work, a framework for FAIR data processing and democratized in silico modeling is presented. The framework includes an automated data ingestion, processing, and reporting pipeline for reaction kinetics experiments. This is coupled with a web application designed to broaden access to modeling of reaction kinetics under a variety of process conditions and configurations. The combination of FAIR experimental data with easily accessible modeling tools provides a strong digital twin to wet-lab chemistry, enabling fewer and more impactful experiments.