Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials

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Program:
DIFFERENTIATE
Award:
$1,234,849
Location:
Pittsburgh, Pennsylvania
Status:
ALUMNI
Project Term:
04/08/2020 - 05/31/2022

Technology Description:

Carnegie Mellon University will use deep reinforcement learning and atomistic machine learning potentials to predict catalyst surface stability under reaction conditions. Current methods for determining the metastability of bifunctional and complex surfaces undergoing reaction are difficult and expensive. Carnegie Mellon’s technology will enable stability analysis in both traditional catalysts and new classes of materials, including those used in tribology (friction), corrosion-resistant alloys, additive manufacturing, and battery materials.

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:

Security:

Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.

Environment:

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.

Economy:

Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.

Contact

ARPA-E Program Director:
Dr. Daniel Cunningham
Project Contact:
Zachary Ulissi
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.gov
Project Contact Email:
zulissi@andrew.cmu.edu

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Release Date:
04/05/2019