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SBIR Phase I: Implementing AL-enhanced Machine-Learning for Advanced Electrochemical Manufacturing
Phone: (917) 789-0424
Phone: (917) 789-0424
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.
* Information listed above is at the time of submission. *