Advancements in Artificial Intelligence for Science

Solicitation PDF

• Original at OSTI
• Cached local copy

Selected Pages

Selected Extracts

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 Area 1: …

…

Research Area 2: AI Innovations for Scientific Knowledge Synthesis and Software Development

The state-of-the-art in knowledge synthesis and programming tools are changing rapidly, fueled by AI Large Language Models (LLMs) trained on text, source code, and other data sources. New AI-driven tools are currently not trustworthy; do not systematically understand mathematical and physical principles; cannot properly ingest and understand scientific literature and data; and do not produce consistent, verified, uncertainty-quantified, reproducible results. In addition to addressing those challenges, there may be particular advantages in such tools building up knowledge and context over many interactions with a user or group of users. However, incremental training of AI systems over long time horizons, and the representation of knowledge in AI systems robust to changes in the underlying AI models, remain critical challenges. 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. For additional background, see Chapter 2, “AI Foundation Models for Scientific Knowledge Discovery, Integration, and Synthesis,” Chapter 6, “AI for Programming and Software Engineering,” Chapter 12, “Mathematics and Foundations,” and Chapter 14, “Data Ecosystem,” of the AI For Science, Energy, and Security report [1].

Additionally, investigations into AI-driven tools for science should be conceptualized accounting for the iterative and collaborative processes that define modern science and scientific-software development. Accordingly, research proposed in this area is encouraged to address the relevant Priority Research Directions (PRDs) from the Basic Research Needs in The Science of Scientific Software Development and Use report [6], which are PRD 1, “Develop next-generation tools to enhance developer productivity and software sustainability,” PRD 2, “Develop methodologies and tools to comprehensively improve team-based scientific software development and use,” and PRD 3, “Develop methodologies, tools, and infrastructure for trustworthy software-intensive science.

Methods proposed for investigation should use any appropriate techniques that might be necessary to accomplish their goals, including, but not limited to, machine learning, natural- language processing, formal reasoning, instrumentation, data management, and compiler technology. The sustainability and explainability of scientific software are critically important to the scientific process, and as a result, particular consideration should be given to maximizing the extent to which human programmers understand and/or trust the outputs of these methods.

Research Area 3: …

…

Research Area 4: …

…

Research Area 5: …

…