DOE Announces $68M for AI for Scientific Research

The funded projects will develop new ways to create foundation models, which are machine learning or deep learning models that can be used across a wide range of applications because they’re trained on broad data. Foundation models are a key building block of AI.

Those models will be used in computational science, to automate workflow in laboratories, to accelerate scientific programming, and much more. The possibilities are endless. Models will also be created using privacy-preserving and distributed methods and to develop energy-efficient AI algorithms and hardware for science.

These efforts expand upon work directed to DOE in Executive Order 14110 from the White House on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, including work to develop tools for the creation of foundation models for science and work to develop privacy-preserving AI technologies.

“Privacy preserving” is a priority because it protects personal data during machine-learning processes, allowing users to gain insights from data without jeopardizing sensitive information.

“Progress in AI is inspiring us to imagine faster and more-efficient ways to do science,” said Ceren Susut, DOE Associate Director of Science for Advanced Scientific Computing Research. “These research efforts will make scientific AI both more trustworthy and more energy efficient, unlocking AI’s potential to accelerate scientific discovery. There is a huge variety in the number of applications where scientists can use AI, from the laboratory to the field to producing scientific research.”

The projects in the funding cover a wide range of activities and diverse scientific applications. Included are studying how large foundation models for science improve as the models increase in size and complexity; training foundation models to preserve privacy and use data spread across multiple institutions; and developing energy-efficient algorithms using next-generation microtechnologies.

The projects were selected by competitive peer review under the DOE Funding Opportunity Announcement for Advancements in Artificial Intelligence for Science.