Stock Markets February 12, 2026

NuScale Shares Jump After DOE-Backed AI Fuel Management Project with ORNL

Partnership will test AI-enabled fuel optimization across a 12-module reactor arrangement under the DOE's GAIN program

By Leila Farooq SMR
NuScale Shares Jump After DOE-Backed AI Fuel Management Project with ORNL
SMR

NuScale Power (NYSE:SMR) saw its stock climb 10% following the announcement of a collaboration with Oak Ridge National Laboratory to research artificial intelligence-guided nuclear fuel management for a 12-module reactor configuration. Funded through the U.S. Department of Energy's GAIN initiative, the work will explore whether sharing a single fuel pool across multiple modules can yield higher plant-wide fuel efficiency and lower costs.

Key Points

  • NuScale shares rose 10% following the announcement of an AI-guided fuel management study with ORNL.
  • The DOE-funded project will use an AI-enabled design framework to explore fuel management for a 12-module reactor configuration.
  • NuScale's multi-module architecture and a single shared fuel pool may enable plant-level fuel efficiencies beyond single-reactor plants, potentially lowering costs.

Overview

NuScale Power Corporation (NYSE:SMR) experienced a 10% increase in its share price after revealing a research collaboration with Oak Ridge National Laboratory (ORNL). The effort, financed through the U.S. Department of Energy's Gateway for Accelerated Innovation in Nuclear (GAIN) initiative, will apply an AI-enabled nuclear design framework to examine fuel management strategies for a 12-module reactor configuration.

Project focus

The research will investigate how nuclear fuel might be managed more efficiently when multiple reactors on a single site operate with a common fuel pool. Unlike conventional large single-reactor plants, NuScale's multi-module architecture permits fuel sharing across as many as 12 modules. The company believes this arrangement could open opportunities to improve overall plant fuel efficiencies beyond what is typically achievable in a single-reactor facility, with the potential to reduce operating costs.

Role of ORNL and DOE support

ORNL will contribute expertise in artificial intelligence, machine learning, fuel management techniques, and computational resources to support the study. The project is included in the first round of GAIN Vouchers awarded in fiscal year 2026, providing federal backing for the collaboration.

Company comment

"We are thrilled to be collaborating with ORNL, with the support of the DOE, to assess exciting new opportunities for potentially managing fuel even more efficiently across multiple nuclear reactors and further reducing costs going forward," said John Hopkins, NuScale President and Chief Executive Officer.

Implications

The study aims to quantify whether AI-driven design and operational planning can extract efficiency gains from a shared-fuel arrangement across NuScale's modular reactors. ORNL's computational and machine learning capabilities are intended to drive modeling and optimization work, focusing on fuel management across the full 12-module configuration.

Limitations

The announcement describes the scope and backing for the project but does not provide technical results or timelines. The research is exploratory in nature and intends to assess potential pathways to improved fuel efficiency rather than guarantee specific outcomes.


Key points

  • NuScale shares rose 10% after announcing an AI-guided fuel management collaboration with ORNL.
  • The project, funded by the DOE's GAIN initiative, will apply an AI-enabled framework to a 12-module reactor configuration.
  • NuScale's multi-module design allows a single shared fuel pool across up to 12 reactors, which the company says could enable greater plant-level fuel efficiency and cost reductions.

Risks and uncertainties

  • The research is exploratory and does not provide results or timelines, leaving outcome and timing uncertain.
  • Potential efficiency gains are described as possibilities - the announcement does not confirm that specific cost reductions will be achieved.

Risks

  • The collaboration is exploratory and provides no technical results or timeline, creating uncertainty about outcomes and timing.
  • The announcement frames potential efficiency and cost reductions as possibilities rather than guaranteed results.

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