WASHINGTON, D.C. – The U.S. Department of Energy (DOE) announced $26 million in funding to harness cutting-edge research tools for new scientific discoveries fundamental to clean energy solutions. The 10 projects announced today will help scientists to unleash the power of data science —including artificial intelligence and machine learning (AI/ML)—on experiments, theory, and computation-based methods to tackle the basic science challenges that will enable clean energy technologies, improve energy efficiency, and advance our understanding of chemical and materials systems.
“Data science, and especially AI/ML, provides unique opportunities to leapfrog to novel capabilities for understanding fundamental properties and processes in physical and chemical systems,” said Dr. Steve Binkley, Acting Director of DOE’s Office of Science. “This research will take advantage of the rapid growth of AI/ML to accelerate the discoveries needed for more efficient energy generation, storage, and use, while eliminating or reducing the emission of greenhouse gases and the use of critical resources.”
The new awards will support multidisciplinary teams of scientists to understand and predict the behavior of the complex systems found in many energy technologies. The resulting predictive AI/ML models, validated by experiments, will accelerate discovery of new chemistries and materials systems with exceptional properties and functionalities. The selected research projects are led by two National Laboratories and eight universities, including an HBCU. A list can be found here.
Projects were chosen based on peer review under the DOE Funding Opportunity Announcement, “Data Science to Advance Chemical and Materials Sciences,” open to universities, National Labs, industry, and non-profit research organizations. Total funding is $26 million for projects lasting up to three years, with $8.7 million in Fiscal Year 2021 dollars and outyear funding contingent on congressional appropriations. The final details for each award are subject to negotiations between DOE and the awardee.