Design Intelligence Fostering Formidable Energy Reduction and Enabling Novel Totally Impactful Advanced Technology Enhancements

Program Image


Status:
Active
Release Date:
Project Count:
23

Program Description:

In the 250 years since the dawn of the Industrial Revolution, the pace of technology-driven economic growth has dwarfed that achieved in prior centuries. The emerging artificial intelligence revolution has similar transformational potential, which we seek to leverage to help resolve the energy and environmental challenges that are tied to the modern industrial age.

Artificial intelligence (A.I.) makes it possible for machines to learn from experience, adjust to new inputs and perform like humans. Machine learning is a core part of A.I., and it is the study of computer algorithms that improve automatically through experience. Incorporating machine learning into the energy technology and/or product design processes is anticipated to facilitate a rapid transition to lower-carbon-footprint energy sources and systems.

Innovation Need:

The DIFFERENTIATE program seeks to enhance the pace of energy innovation by incorporating machine learning into the energy technology development process. In order to organize the proposed efforts, the program adopts and utilizes a simplified engineering design process framework to identify three general mathematical optimization problems that are common to many engineering design processes. It then conceptualizes several machine learning tools that could help engineers to execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation.

DIFFERENTIATE aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. The program seeks to develop machine learning tools that:

  • Enhance the creativity of the hypothesis generation (i.e., conceptual design) process by helping engineers develop new concepts and by enabling the consideration of a larger and more diverse set of design options during the hypothesis generation phase;
  • Enhance the efficiency of the high-fidelity evaluation (i.e., detailed design) process by accelerating the high-fidelity analysis and optimization of the hypothesized solution; and
  • Ultimately reduce (ideally eliminate) design iteration by developing the capability to execute “inverse design” processes in which the product design is effectively expressed as an explicit function of the problem statement.

Potential Impact:

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.

Contact

Program Director:
Dr. David Tew
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.gov

Project Listing

• Carnegie Mellon University (CMU) - Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials
• Carnegie Mellon University (CMU) - High-fidelity Accelerated Design of High-performance Electrochemical Systems
• General Electric (GE) Global Research - Probabalistic Machine Learning for Inverse Design of Aerodynamic Systems (Pro-ML IDeAS)
• General Electric (GE) Global Research - IMPACT: Design of Integrated Multi-physics, Producible Additive Components for Turbomachinery
• IBM T.J. Watson Research Center - Model-based Reinforcement Learning with Active Learning for Efficient Electrical Power Converter Design
• Iowa State University (ISU) - Context-Aware Learning for Inverse Design in Photovoltaics
• Julia Computing - Accelerating Coupled HVAC/Building Simulation with a Neural Component Architecture
• Lawrence Berkeley National Laboratory (LBNL) - Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials
• Los Alamos National Laboratory (LANL) - Machine Learning-Based Well Design to Enhance Unconventional Energy Production
• Massachusetts Institute of Technology (MIT) - Machine Learning Assisted Models for Understanding and Optimizing Boiling Heat Transfer on Scalable Random Surfaces
• Massachusetts Institute of Technology (MIT) - Global Optimization of Multicomponent Oxide Catalysts for OER/ORR
• National Renewable Energy Laboratory (NREL) - End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning
• National Renewable Energy Laboratory (NREL) - 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
• Pacific Northwest National Laboratory (PNNL) - Machine Learning for Natural Gas to Electric Power System Design
• Princeton University - MLSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics
• Stanford University - Energy Efficient Integrated Photonic Systems based on Inverse Design
• United Technologies Research Center (UTRC) - MULTI-source Learning-Accelerated Design of high-Efficiency multi-stage compRessor (MULTI-LEADER)
• United Technologies Research Center (UTRC) - Learning Enabled Network Synthesis (LENS)
• University of Maryland (UMD) - Invertible Design Manifolds for Heat Transfer Surfaces (INVERT)
• University of Michigan, Dearborn - ML-ACCEPT: Machine-Learning-enhanced Automated Circuit Configuration and Evaluation of Power Converters
• University of Missouri - Deep Learning Prediction of Protein Complex Structures
• University of Texas at Austin (UT Austin) - Learning Optimal Aerodynamic Designs