The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in basic computer science and applied mathematics research in the fundamentals of Artificial Intelligence (AI) for science. Specifically, advancements in this area are sought that can enable the development of:
- Foundation models for computational science;
- Automated scientific workflows and laboratories;
- Scientific programming and scientific-knowledge-management systems;
- Federated and privacy-preserving training for foundation and other AI models for science; and
- Energy-efficient AI algorithms and hardware for science.
The development of new AI techniques applicable to multiple scientific domains can accelerate progress, increase transparency, and open new areas of exploration across the scientific enterprise.
Research Areas (See FOA for details)
Each pre-application and application, as described in Section IV.B.2 and Section IV.D.2 respectively, must identify one primary research area.
- Research Area 1: Extreme-Scale Foundation Models for Computational Science
- ASCR sees an opportunity to capitalize on the rapidly-advancing foundation-model techniques, combined with recently-deployed exascale computing resources, to expeditiously jumpstart the impact of foundation models on computational science in order to significantly advance the state of the art in computational science. Accordingly, this research area seeks the creation of a foundation model, or multiple foundation models, for computational science, covering broad areas in computer science and applied mathematics.
- Research Area 2: AI Innovations for Scientific Knowledge Synthesis and Software Development
- This research area seeks fundamental advancements in knowledge synthesis and programming tools for science. Moreover, realizing AI systems that can truly understand, and assist with, all aspects of the scientific process requires innovation in many areas, including multimodality, tool use, deeper reasoning and planning, memory, and external interaction.
- Research Area 3: AI Innovations for Computational Decision Support of Complex Systems
- The principal focus of this research area is on the use of scientific AI/ML for intelligent automation and decision support for complex systems.
- Research Area 4: Federated and Privacy-Preserving Machine Learning and Synthetic Data Creation
- The focus of this research area is on federated learning, potentially combined with privacy-preserving techniques, and synthetic data creation to address the aforementioned challenges.
- Research Area 5: The Co-Design of Energy-Efficient AI Algorithms and Hardware Architectures
- This research area seeks innovative approaches to energy-efficient scientific AI, including corresponding mathematical paradigms and modeling capabilities capable of predicting the resource efficiency of AI systems, potentially up to the largest scales. Energy-efficient AI technologies, covering both training and inference, and including current technologies as a point of comparison, should be addressed holistically, accounting for energy for parameter representation, model size, and other representational factors, plus computational energy for activations and other inherent operations in the neural networks.
Interested applicants should upload the following documents in sequence in one PDF file (File name: [Last name]_DE-FOA-0003264_2024) no later than 4:00 p.m. on the internal submission deadline:
1. Cover page(1 page, pdf):
- Descriptive title of proposed activity
- PI name, departmental affiliations(s) and contact information
- Co-PI's names and departmental affiliation(s)
- Names of other key personnel
- Participating institution(s)
- Number and title of this funding opportunity
- The research area primarily addressed by the proposed work [list only one primary research area]
2. Project Description (no more than two pages, pdf) Figures and references, if included, must fit within the two-page limit.
- The first page of the pre-application must specify at least one scientific hypothesis whose investigation motivates the proposed work, using no more than 100 words, in a box with a black border. For any hypothesis that is not itself innovative, the pre-application must describe at least one innovative insight into how the hypothesis can be investigated that may be exploited by the planned research.
This information must be followed by a clear and concise description of the objectives and technical approach of the proposed research.
3. Estimated Budget (1 page)
4. 3-page CV's of Investigators
Formatting Guidelines:
Font/size: No smaller than 11 pt.
Document margins: 1.0” (top, bottom, left and right)
Standard paper size (8 ½” x 11)
Questions concerning the limited submissions process may be submitted to limitedsubs@psu.edu.