End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

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Golden, Colorado
Project Term:
04/09/2020 - 09/30/2022

Technology Description:

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.

Potential Impact:

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 Tew
Project Contact:
Peter St. John
Press and General Inquiries Email:
Project Contact Email:


Colorado School of Mines

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