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SBIR Phase I: Implementing AL-enhanced Machine-Learning for Advanced Electrochemical Manufacturing

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2041577
Agency Tracking Number: 2041577
Amount: $256,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: M
Solicitation Number: N/A
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-04-01
Award End Date (Contract End Date): 2021-12-31
Small Business Information
United States
DUNS: 117050006
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Daniela Blanco
 (917) 789-0424
Business Contact
 Daniela Blanco
Phone: (917) 789-0424
Research Institution

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. *

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