Sorry, you need to enable JavaScript to visit this website.

We’re Not Saying We’re the Avengers, But...

In the 2019 blockbuster "Avengers: Endgame", it takes genius-billionaire-playboy-philanthropist Tony Stark as long to perfect time travel as it takes most people to mow their lawn. Critical to his success is his Artificial Intelligence (AI) assistant, which is able to almost instantly turn his offhand musings into workable, visually pleasing solutions.

While a time heist isn’t on ARPA-E’s agenda, we are excited to announce the project selections for our first AI/machine learning-focused program: Design Intelligence Fostering Formidable Energy Reduction (and) Enabling Novel Totally Impactful Advanced Technology Enhancements (DIFFERENTIATE).

ARPA-E and the DIFFERENTIATE team selected projects that incorporate AI and machine learning into the energy technology and/or product design processes. Their goal is to enhance the productivity of energy engineers and help them develop much-needed, next-generation energy technologies. Movie magic aside, AI and machine learning are proven to help speed up the design process for new technology.

So, you may ask yourself, how do AI and machine learning help design more efficient, lower carbon energy systems? AI makes it possible for machines to learn from experience, adjust to new inputs, and perform like humans. Machine learning is a core part of AI, and it centers around computer algorithms that improve automatically through experience.

The way these technologies help design and development is summed up in the old adage, “if at first you don’t succeed, try, try again.” It’s just that when machine learning is involved, the number of tries required to successfully train a new algorithm may be prefixed by kilo, mega, or even giga.

At its core, machine learning is an application of AI that enables a system to “learn” from experience.  Machine learning algorithms take in huge amounts of data, and “learn” that particular inputs result in specific responses. The trick is to build these algorithms in such a way that they can “learn” to predict a response for an input that hasn’t been specifically programmed.

In order to organize the anticipated efforts, the DIFFERENTIATE program has adopted a simplified engineering design process framework and used it to identify several general mathematical optimization problems that are common to many engineering design processes. It then conceptualizes corresponding machine learning tools that could help engineers execute and solve these problems in a manner that would dramatically accelerate the pace of energy innovation.

ARPA-E is excited to open the next phase of energy technology design and innovation using AI and machine learning. The DIFFERENTIATE projects include:

National Renewable Energy Laboratory

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

National Renewable Energy Laboratory

INTEGRATE – Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

Northwestern University

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

Iowa State University

Context-Aware Learning for Inverse Design in Photovoltaics

Massachusetts Institute of Technology

Machine Learning Assisted Models for Understanding and Optimizing Boiling Heat Transfer on Scalable Random Surfaces

Massachusetts Institute of Technology

Global Optimization of Multicomponent Oxide Catalysts for OER/ORR

University of Michigan-Dearborn

Machine-Learning-Enhanced Automated Circuit Configuration and Evaluation of Power Converters

Carnegie Mellon University

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

Julia Computing, Inc.

Accelerating Coupled HV AC-Building Simulation with a Neural Component

University of Maryland

Invertible Design Manifolds for Heat Transfer Surfaces (INVERT)

Los Alamos National Laboratory

Machine-Learning-Based Well Design to Enhance Unconventional Energy Production

University of Texas at Austin

Learning Optimal Aerodynamic Designs

IBM Research

Model-based Reinforcement Learning with Active Learning for Efficient Electrical Power Converter Design

Carnegie Mellon University

High-Fidelity Accelerated Design of High-Performance Electrochemical Systems

Stanford University

Energy Efficient Integrated Photonic Systems Based on Inverse Design

University of Missouri

Deep Learning Prediction of Protein Complex Structures

United Technologies Research Center

LENS: Learning Enabled Network Synthesis

United Technologies Research Center

MULTI-LEADER: MULTI-Source LEarning-Accelerated Design of High-Efficiency Multi-Stage Compressor

GE Research

IMPACT: Design of Integrated Multi-physics Producible Additive Components for Turbomachinery

GE Research

Pro-ML IDeAS: Probabilistic Machine Learning for Inverse Design of Aerodynamic Systems

Princeton University

MILSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics

Lawrence Berkeley National Laboratory

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

Pacific Northwest National Laboratory

Machine Learning for Natural Gas to Electric Power System Design

For more information, visit the DIFFERENTIATE program page here.