SBIR-STTR Award

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to demonstrate feasibility of machine-learning (ML)-guided experimental campaigns that predict, assess, and optimize electroorganic transformations with small experimental datasets. Companies across the chemical industry have pinpointed electrochemistry as a promising avenue for the implementation of more sustainable and energy-efficient manufacturing processes. However, the large cost and effort required in new process development hinders the implementation of electrochemical technologies. ML predictive algorithms can be a powerful tool to accelerate the development and optimization of more sustainable chemical processes, but repeatedly require large amounts of experimental data to train the models. These large datasets are often unavailable and expensive to obtain, which significantly limits the use of ML in the chemical industry. The project will advance future manufacturing by enabling the development of new and more sustainable chemical production routes using 50% less experiments, ultimately unlocking the manufacture of new molecules, medicines, and materials in societal applications. Moreover, by reducing the number of experiments required, the technology will significantly lower emissions and resource consumption in the industry.The proposed project introduces a ML platform capable of guiding experimental campaigns and data collection to enable accurate predictions of reaction behavior with the smallest possible datasets. The approach relies on the combination of chemical engineering and ML knowledge to overcome the optimization limitations found within each field. It will be validated using the electrooxidation of p-methoxytoluene as a model reaction and will elucidate the fundamental limitations and strengths of ML predictive models capturing the complexity of physical systems with small datasets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project will improve and accelerate the development of new chemicals, processes, and formulations in the pharmaceutical industry. By harnessing the power of machine learning (ML), this project aims to save time and resources by up to 95%, while reducing waste generation, thereby enhancing the commercial and societal impact of drug development. Traditional process optimization in drug development is a time-consuming and expensive endeavor, often relying on trial-and-error approaches. Lead optimization and route scouting in pre-clinical drug development can take months and involve thousands of experiments, costing millions of dollars in personnel expenses alone. This project seeks to address these challenges by employing ML to guide experimental design with small datasets. Through a combination of ML and chemistry knowledge, this project aims to streamline the optimization process by suggesting only the most promising experiments and minimizing the number of failed attempts. This solution not only grants patients quicker access to medicines but also enables companies to generate earlier revenue and maintain longer market exclusivity.This Small Business Innovation Research Phase II project addresses one of the most significant challenges faced by research and development (R&D) chemists: the optimization of categorical variables in synthetic processes, specifically solvent selection. Solvents play a vital role in the chemical industry, including reaction, separation, purification, and formulation. The proper solvent can improve efficiency, reduce costs, and result in a more environmentally friendly process. Despite successful advances in ML-guided optimization and green solvent selection methodologies, available tools do not effectively combine environmental and performance parameters for simultaneous solvent selection and process optimization. This project will provide a solution for scientists across the chemical industry to leverage small data and innovative ML technologies to advance manufacturing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.