Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials

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Evanston, Illinois
Project Term:
04/28/2020 - 11/30/2022

Technology Description:

Northwestern University will develop a machine learning-enhanced mixed-variable conceptual design optimization framework to construct new functional materials for energy savings. The team will use natural language processing (NLP) and physics-based machine ML to more efficiently guide the autonomous search for materials. The project will deliver a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, and reinforcement learning in Bayesian optimization. Northwestern University’s project leverages functional and promising materials exhibiting metal-insulation transitions, a set of materials that can revolutionize microelectronics science to provide energy-saving solutions.

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:
Prof. Wei Chen
Press and General Inquiries Email:
Project Contact Email:

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