High-fidelity Accelerated Design of High-performance Electrochemical Systems
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:
Carnegie Mellon University (CMU) and team will develop an integrated machine learning-accelerated design and optimization workflow that will reduce the time and cost required to develop functional energy materials in devices. The core innovation pairs machine learning based filtering of candidate materials with accelerated high-fidelity modeling to efficiently search a large design space for high-performance materials under realistic operating conditions. The team will create detailed designs for (1) catalyst systems for electrochemical reactions that convert electrical energy into carbon-neutral chemicals and fuels and (2) electrolyte systems for next-generation batteries. Designing electrochemical systems capable of high turnover and efficiency is a challenge to enable the cost-effective production of carbon-neutral chemicals and fuels. Designing liquid electrolytes for next-generation batteries/fuel cells will provide an alternative transportation technologies to petroleum by improving energy density, thus enabling long-range electric vehicles. In particular, the project will develop software and hardware-accelerated methods using the Julia language for high-fidelity objective function evaluation, and an efficient global optimization approach using sequential learning and design of experiments to achieve its goals.
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. Venkat Viswanathan
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.govProject Contact Email:
Julia Computing, Inc.
Massachusetts Institute of Technology