Darío Gil and Kathryn A. Moler assert that integrating artificial intelligence (AI) into research workflows has the potential to substantially increase scientific productivity. This assertion arrives alongside the White House’s announcement of the US Genesis Mission, prompting discussion of creating integrated infrastructure—spanning data, algorithms, hardware, and agentic control—to accelerate research. Success hinges on identifying scientific questions offering transformative breakthroughs, such as utilizing AI to predict plasma instabilities for real-time control in fusion energy, developing predictive models for new molecular and material discoveries, and accelerating algorithm development for quantum simulations.
AI’s Potential to Accelerate Scientific Discovery
AI has the potential to significantly increase scientific productivity by integrating into research workflows. Success hinges on two key efforts: building the necessary infrastructure—including data, algorithms, and computing power—and establishing policies to empower scientists. For example, in fusion energy, AI can be used to predict plasma instabilities for real-time control, while in materials science, it can develop predictive models for new discoveries. This approach aims to accelerate science through human-AI collaboration.
A critical element for AI-driven discovery is access to high-quality data. The Protein Data Bank exemplifies how decades of investment in experimental tools and open-access repositories can fuel AI models, like those used for protein structure prediction. Future progress depends on transforming isolated datasets into a unified engine, requiring concerted efforts to prepare existing data and establish standards for AI accessibility. Major instruments such as the Vera C. Rubin Observatory, Advanced Photon Source, and Large Hadron Collider provide abundant data.
Beyond general-purpose AI, specialized models will combine AI learning with traditional simulations grounded in physics and chemistry. These “hybrid models” will be validated against known physical models and real-world data, augmenting—not replacing—existing scientific methods. Coupling these with AI “agents” – systems that autonomously coordinate research steps under human direction – promises to compress discovery timelines, creating a self-reinforcing cycle of improvement and potentially boosting economic growth.
Data Infrastructure for AI-Driven Research
Data infrastructure for AI-driven research requires a unified approach to data, uniting exascale computing, specialized AI, quantum supercomputers, and secure networks. Crucially, this infrastructure must also connect to devices like sensors for real-time data acquisition and control during live experiments. Beyond general AI, specialized models will combine AI learning with traditional simulations grounded in physics and chemistry, augmenting—not replacing—existing scientific models with checkpoints for validation against real-world data.
Transforming isolated datasets into a unified engine for discovery necessitates concerted effort. The Protein Data Bank serves as a model, built on decades of investment in experimental tools and open-access repositories. While instruments like the Vera C. Rubin Observatory offer abundant data, the challenge lies in managing the vast, heterogeneous data across R&D, requiring standardized data and accessibility for AI use.
Strategic public-private partnerships are vital for accelerating science with AI. While the federal government catalyzes fundamental research, over 70% of the total $1 trillion annual US R&D investment comes from the private sector. Joint investments in computing infrastructure and data sharing frameworks can amplify the value of government funding and fuel innovation, ultimately boosting economic growth and improving lives.
Success begins with asking the right scientific questions—identifying problems that may offer transformative breakthroughs and drive advances in AI methods and human-AI teaming, thereby inspiring broader acceleration of science.
Integrating AI with Scientific Modeling
Integrating AI with scientific modeling requires a new generation of computing infrastructure uniting exascale computing, specialized AI, and quantum supercomputers. This infrastructure will connect to devices for real-time data acquisition and control during live experiments. Beyond general AI, specialized models will combine AI’s learning capabilities with the predictive power of traditional simulations grounded in physics and chemistry. These hybrid models will augment, not replace, existing scientific models, with checkpoints for validation against real-world data.
Success in AI-driven science depends on data, with the Protein Data Bank cited as a successful example fueled by decades of investment in experimental tools and open-access repositories. Transforming isolated datasets requires concerted effort to prepare data for AI use and establish standards for future accessibility. Major instruments like the Vera C. Rubin Observatory and the Large Hadron Collider provide abundant data, but challenges remain with the vast, messy world of heterogeneous data across research and development.
AI will also function through “scientific agents”—AI systems coordinating research steps under human direction. Coupling these agents with hybrid models promises to compress discovery timelines, as data from each AI analysis feeds a self-reinforcing cycle of improvement. For AI to be a true partner, results must be verifiable with data, methodologies, code, and outputs publicly available, fostering open-source models and standardized tools.
Public-Private Partnerships for R&D Funding
Public-private partnerships are seen as crucial for amplifying the impact of R&D funding in the US, which currently totals a trillion dollars annually. While the federal government catalyzes fundamental research, over 70% of this support originates from the private sector. Strategic collaborations—like joint investments in computing infrastructure and data sharing frameworks—can combine governmental foundational work with private sector resources and innovative capacity, extending beyond science to benefit the entire economy.
A key aim of these partnerships is to boost the productivity and impact of research, with R&D representing 3.5% of the US gross domestic product and serving as a powerful economic engine. By empowering researchers with AI tools, these collaborations aim to fuel innovation and economic growth. Pilot programs focused on AI-enabled discovery could develop new methods and address challenges across multiple disciplines and institutions.
These partnerships aren’t solely about funding; they also involve creating frameworks for data sharing and collaboration. The source highlights the need for readily accessible data, standardized tools, and open-source models, all of which require cooperative efforts. This collaborative approach extends to developing computing infrastructure – uniting resources like exascale computing, AI specialization, and secure networks – to accelerate the pace of scientific discovery.
