Global Optimization of Multicomponent Oxide Catalysts for OER/ORR
The DIFFERENTIATE program seeks to leverage the emerging artificial intelligence (AI) revolution to help resolve the energy and environmental challenges of our time. The program aims to speed energy innovation by incorporating machine learning (ML) into the energy technology development process. A core part of AI, ML is the study of computer algorithms that improve automatically through experience. This approach is expected to facilitate a rapid transition to lower-carbon-footprint energy sources and systems. To organize the proposed efforts, the program uses a simplified engineering design process framework to conceptualize several ML tools that could help engineers execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation.
Project Innovation + Advantages:
The Massachusetts Institute of Technology (MIT) will develop machine learning (ML) enhanced tools to accelerate the development of catalysts that promote the oxygen evolution reaction (OER) or the oxygen reduction reaction (ORR). These reactions are critical to the cost-effective generation (OER) or oxidation (ORR) of hydrogen. Available catalysts for promoting these reactions include scarce and costly precious metals like platinum. Hence, their practical applications are limited by high cost and low abundance in addition to moderate stability. The MIT team will tailor the chemical composition of non-platinum-group transition metal oxides to improve their catalytic performance and reduce the number of potential combinations required for testing. MIT’s ML approach will integrate literature extraction, simulations, synthesis, lab-scale testing, and industrial prototyping to yield a catalyst design methodology that is faster and more efficient than traditional trial-and-error or serial experimentation-based approaches. The ML techniques to be employed are expected to include generative models and message-passing neural networks for materials as well as convolutional neural networks for machine vision to characterize catalysts.
DIFFERENTIATE aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. If successful, DIFFERENTIATE will yield the following benefits in ARPA-E mission areas:
Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.
Use these tools to solve our most challenging energy and environmental problems by facilitating an economically-attractive transition to lower carbon-footprint energy sources and systems.
Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.
ARPA-E Program Director:
Dr. David TewProject Contact:
Prof. Rafael Gomez-Bombarelli
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.govProject Contact Email:
Argonne National Laboratory