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

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:

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
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Project Contact Email:

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