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

Default ARPA-E Project Image

Program:
DIFFERENTIATE
Award:
$1,800,000
Location:
Golden, Colorado
Status:
ALUMNI
Project Term:
04/09/2020 - 09/30/2022
Website:

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:

The National Renewable Energy Laboratory (NREL) will develop a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. However, at present, humans must sift through the vast amounts of data generated and manually identify new compositions. To accelerate this process, NREL plans to develop a machine learning enhanced prediction tool that uses existing simulation data to predict the performance of new material compositions at high fidelity but lower cost. NREL plans to combine this tool with reinforcement learning techniques to automate the identification of new candidate compositions. It is expected that these design tools will enable the identification of new battery materials faster and thereby accelerate the rate at which battery performance is improving.

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:

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

ARPA-E Program Director:
Dr. David Tew
Project Contact:
Peter St. John
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.gov
Project Contact Email:
Peter.STJohn@nrel.gov

Partners

Colorado School of Mines

Related Projects

• Carnegie Mellon University (CMU) - High-fidelity Accelerated Design of High-performance Electrochemical Systems
• Carnegie Mellon University (CMU) - Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials
• General Electric (GE) Global Research - IMPACT: Design of Integrated Multi-physics, Producible Additive Components for Turbomachinery
• General Electric (GE) Global Research - Probabalistic Machine Learning for Inverse Design of Aerodynamic Systems (Pro-ML IDeAS)
• 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) - INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements
• National Renewable Energy Laboratory (NREL) - End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning
• 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

Release Date:
04/05/2019