(628b) Leveraging Small Data for Enhanced Process Development

Although over 95% of goods around us come from the business of chemistry, developing new molecules, formulations, and manufacturing processes is an expensive challenge. Complex optimization efforts are commonly required to enhance performance and avoid harsh operating conditions, high costs, and other limitations.

Data-driven machine-learning (ML) offers the possibility to dramatically reduce the resources needed for reaction and process optimization through predictions of reaction performance and optimal operating conditions. However, most ML algorithms require large datasets (i.e. 100’s-1000’s of points) to provide accurate predictions, and experimental data collection is expensive in human and material resources. The challenge of building large datasets can be averted with Bayesian optimization (BO) frameworks. These guide experimental campaigns and carefully build training datasets that ultimately reduce the number of experiments required to achieve reliable understanding of variable effects and reaction behavior. However, their implementation is not straightforward in many cases.

Developing an enhanced BO approach, we elucidate a reaction-agnostic pathway for the design and implementation of intelligent experimental campaigns using catalytic and electrochemical transformations as case studies. Working at the intersection of ML and chemistry, we created SuntheticsML, an online, user-friendly, ML platform available to researchers that does not require coding or ML expertise. It leverages very small data (starting with only 5 data points) to enable the development of new chemical processes, materials, and formulations with unprecedented efficiencies and up to 15x less time and experiments.

The insights from this work innovate on the future of reaction engineering, material design, and process optimization while lowering the barrier to the implementation of new chemical technologies. SuntheticsML accelerates lengthy and complex traditional optimization campaigns, harnessing the power of ML and small data to fast-track innovation, sustainability, and digitalization in the chemical industry, further reducing development waste, emissions, and resource consumption by up to 95%