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

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Berkeley, California
Project Term:
03/19/2020 - 06/22/2022

Technology Description:

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:


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:
Dr. Ravi Prasher
Press and General Inquiries Email:
Project Contact Email:


Georgia Tech Research Corporation

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