Artificial intelligence is transforming how nuclear engineers design reactors and prepare regulatory documents, potentially speeding up the deployment of next-generation nuclear technology by months or even years. Rather than replacing human expertise, AI is augmenting established nuclear safety procedures, helping engineers make better design decisions earlier in development and reducing computational burdens that have historically slowed innovation in the nuclear industry.

What Role Is AI Playing in Advanced Reactor Design?

At MIT, Assistant Professor Dean Price is pioneering the use of machine learning to understand the complex physical relationships inside nuclear reactor cores. His research focuses on multiphysics modeling, which examines how different processes in a reactor interact with one another. Traditionally, nuclear engineers study neutronics (how neutrons cause fission) and thermal hydraulics (how cooling systems extract heat) separately, then try to understand their interactions. This approach requires solving extremely difficult nonlinear equations on supercomputers, a computationally expensive process .

Price is exploring how AI and machine learning can identify patterns in reactor data without solving those burdensome equations. "If you tell me the power level of your reactor, it could tell you what the fuel temperature is and even tell you the 3-dimensional temperature distribution in your core," Price explained. This capability could dramatically reduce computational costs while helping engineers design novel reactor types more efficiently .

"By really pinning down those relationships, we can make better design decisions in the early stages. And when that technology is developed and deployed, AI can help us make more intelligent control decisions that will enable us to operate our reactors in a safer and more economical way," said Dean Price.

Dean Price, Assistant Professor, Department of Nuclear Science and Engineering at MIT

Price emphasizes that AI's role is to augment established safety frameworks rather than replace them. The technology helps fill existing knowledge gaps and accelerates the design process for small modular reactors (SMRs) and microreactors, which operate at much smaller scales than traditional nuclear plants and require different modeling approaches .

How Is AI Accelerating Nuclear Licensing and Regulatory Documents?

The practical impact of AI on nuclear development became concrete in early 2026 when the Department of Energy announced a significant breakthrough in regulatory documentation. The DOE collaborated with Everstar, Idaho and Argonne National Laboratories, and Microsoft to demonstrate how AI could generate a critical regulatory document in a fraction of the time it normally takes .

The AI tool, called Gordian, generated a 208-page chapter of an NRC (Nuclear Regulatory Commission) license application in approximately one day. A team of human experts would typically require between four and six weeks to complete the same work. According to the DOE, a subject-matter expert who reviewed the AI-generated document described it as "consistent with what would be expected of a Revision 0 document," meaning it met professional standards for a first draft .

Gordian uses a technique called retrieval-augmented generation (RAG), which anchors the AI's responses to source documents rather than allowing it to generate information from scratch. This approach significantly reduces hallucinations, the tendency of AI systems to invent plausible-sounding but false information. The tool was trained on preliminary safety documents, NRC databases, and federal regulations, and it includes built-in quality assurance routines to enforce accuracy .

Steps to Implement AI in Nuclear Regulatory Processes

• Draft Authorship: Use AI tools to generate initial versions of regulatory documents, shifting expert labor from writing to refinement and review, which accelerates the overall timeline.
• Discrepancy Detection: Deploy AI systems to identify missing, derived, or inconsistent information across source documents, surfacing issues that might otherwise persist through multiple human review cycles.
• Formal Regulatory Collaboration: Establish working groups between the DOE and NRC to develop standards for AI-assisted licensing and ensure document formats are written in ways that AI can properly parse and cite.
• Modular Architecture Expansion: Extend the same AI framework to additional regulatory chapters, preliminary safety analysis documents, nuclear quality assurance compliance, and fuel fabrication facility licensing.

The DOE has identified several next steps for this technology. A near-term priority is developing a dedicated review tool to systematically document and resolve discrepancies that the AI might miss. The team is also pursuing a formal NRC working group on AI-assisted licensing, and document standards may evolve to ensure source materials are written in ways that AI can properly parse and cite .

Why Does This Matter for the Nuclear Energy Renaissance?

The acceleration of reactor design and licensing comes at a critical moment for nuclear energy. Europe has set an ambitious goal to deploy its first small modular reactors by the early 2030s, with total SMR capacity potentially reaching between 17 gigawatts and 53 gigawatts by 2050 . The European Commission has unveiled a comprehensive strategy to coordinate SMR development across multiple EU countries, emphasizing the need for faster commercialization and streamlined regulatory processes .

In the United States, the nuclear industry is experiencing renewed momentum. The country currently operates 94 nuclear reactors, more than any other nation, providing nearly 20 percent of the nation's electricity. However, the nuclear engineering workforce is remarkably small, and the industry faces pressure to expand capacity to meet growing energy demands, particularly from artificial intelligence data centers .

SMRs and advanced modular reactors are viewed as more cost-effective and flexible alternatives to traditional large-scale nuclear plants. They can be factory-manufactured and transported to sites for assembly, making them particularly well-suited for powering data centers, producing hydrogen, and supporting local heating networks. By reducing the time required for design iteration and regulatory approval, AI tools could accelerate the deployment timeline for these technologies by months or years .

The convergence of AI-assisted design and regulatory acceleration represents a fundamental shift in how the nuclear industry operates. Rather than waiting months for regulatory documents to be prepared, engineers can now focus on refining designs and addressing technical challenges. This shift could prove decisive in determining whether nuclear energy can scale quickly enough to meet the world's growing demand for clean, reliable power .