End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning
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 National Renewable Energy Laboratory (NREL) will develop a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. However, at present, humans must sift through the vast amounts of data generated and manually identify new compositions. To accelerate this process, NREL plans to develop a machine learning enhanced prediction tool that uses existing simulation data to predict the performance of new material compositions at high fidelity but lower cost. NREL plans to combine this tool with reinforcement learning techniques to automate the identification of new candidate compositions. It is expected that these design tools will enable the identification of new battery materials faster and thereby accelerate the rate at which battery performance is improving.
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:
Peter St. John
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.govProject Contact Email:
Colorado School of Mines