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Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials

Lawrence Berkeley National Laboratory (LBNL)
Program: 
ARPA-E Award: 
$803,730
Location: 
Berkeley, CA
Project Term: 
03/19/2020 to 03/22/2022
Project Status: 
ACTIVE
Technical Categories: 
Critical Need: 
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: 
Over the past 50 years, progress in optical metamaterial device design has led to the manipulation of light over a wide range of wavelengths spanning the ultraviolet to the far infrared, resulting in technological advancements such as selective radiative absorbers for solar energy and daytime passive cooling using deep space. Further advances in optical metamaterial devices could enable increased energy efficiency, reduced national primary energy consumption, inexpensive long duration energy storage, and next generation solid-state heat engines. Lawrence Berkley National Laboratory (LBNL) will develop an optical metamaterial design tool to increase energy efficiency and reduce national primary energy consumption. Besides creating high-quality datasets, LBNL will train physics-informed generative adversarial networks that automatically suggest candidate structures to produce desired optical properties within the constraints of cost of materials and manufacturing. Currently, finding an optimal design can take years and is based mostly on intuition and iteration. The team's machine learning tool will be 10,000 to 100,000 times faster than existing technology.
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.
Contacts
ARPA-E Program Director: 
Dr. David Tew
Project Contact: 
Dr. Ravi Prasher
Partners
Georgia Institute of Technology
Release Date: 
4/5/2019