Tokyo Electric Power Company Holdings (Tepco) said Saturday that an alert system did not work during a test operation held the day prior as part of the restart of the No. 6 reactor at its Kashiwazaki-Kariwa nuclear plant in Niigata Prefecture. The company is working to identify the cause of the problem, but failure to resolve it soon may affect its plan to restart the reactor on Tuesday. According to Tepco, the problem was confirmed at 12:36 p.m., and it stopped the test operation. The alert system is designed to activate when a control rod is being pulled out of the reactor while another rod is already out. The Kashiwazaki-Kariwa reactor would be the first of Tepco's nuclear reactors to be restarted since the March 2011 accident at its tsunami-crippled Fukushima No. 1 nuclear plant.

At a crucial moment for climate change, these technologies show us where we're heading. I know it's a bit late to say, but it never quite feels like the year has started until the new edition of our 10 Breakthrough Technologies list comes out. For 25 years, has put together this package, which highlights the technologies that we think are going to matter in the future. This year's version has some stars, including gene resurrection (remember all the dire wolf hype last year?) And of course, the world of climate and energy is represented with sodium-ion batteries, next-generation nuclear, and hyperscale AI data centers . Let's take a look at what ended up on the list, and what it says about this moment for climate tech.

Meta will finance Oklo's purchase of uranium for its reactors. It's a massive vote of confidence for both the startup and nuclear power, but challenges remain. There are two ways for tech companies to invest in nuclear power right now. One is to buy power from traditional reactors that are already built, either by purchasing electricity from the plants directly or financing the reconstruction of decommissioned units. The other is to invest in one of the dozens of reactor startups promising to commercialize designs and technologies never before used in the American market to generate electricity.

When the US Department of Energy announced that it would stop funding the tokamak at MIT's Plasma Science and Fusion Center, Dennis Whyte considered giving up on fusion research. But then he had a brainstorm--and challenged his students to bring the idea to life. This full-scale high-temperature superconducting magnet designed and built by Commonwealth Fusion Systems and MIT's Plasma Science and Fusion Center (PSFC) has demonstrated a recordbreaking 20 tesla magnetic field. It is the strongest fusion magnet in the world. Ever since nuclear fusion was discovered in the 1930s, scientists have wondered if we could somehow replicate and harness the phenomenon behind starlight--the smashing together of hydrogen atoms to form helium and a stupendous amount of clean energy. Fusing hydrogen would yield times more energy than simply burning it. Unlike nuclear fission, which powers the world's 440 atomic reactors, hydrogen fusion produces no harmful radiation, only neutrons that are captured and added back to the reaction.

US support for nuclear energy is soaring. Meanwhile, coal plants are on their way out and electricity-sucking data centers are meeting huge pushback. Welcome to the next front in the energy battle. Take yourself back to 2017. Get Out and The Shape of Water were playing in theaters, Zohran Mamdani was still known as rapper Young Cardamom, and the Trump administration, freshly in power, was eager to prop up its favored energy sources. That year, the administration introduced a series of subsidies for struggling coal-fired power plants and nuclear power plants, which were facing increasing price pressures from gas and cheap renewables.

Microsoft and Constellation Energy partner to restart Three Mile Island nuclear reactor with $1 billion federal loan to power artificial intelligence operations.

Though the two countries are now in a race to develop atomic technology, China's most advanced reactor was the result of collaboration with American scientists. This April, in a speech given at the Shanghai branch of the Chinese Academy of Sciences, the physicist Xu Hongjie announced a breakthrough. For over a decade, his team had been working on an experimental nuclear reactor that runs on a lava-hot solution of fissile material and molten salt, rather than on solid fuel. The reactor, which went online two years ago, was a feat in itself. It is still the only one of its kind in operation in the world, and has the potential to be both safer and more efficient than the water-cooled nuclear plants that dominate the industry. Now, Xu explained, his team had been able to refuel the reactor without shutting it down, demonstrating a level of mastery over their new system. As dazzling as that was, the timing of Xu's speech also freighted the topic with geopolitical import. Only a few months earlier, DeepSeek, the Chinese artificial-intelligence company, had set alarms ringing through the U.S. tech world when it became clear that the relatively small Chinese startup, operating under U.S. export controls, had created a large language model that rivalled anything devised by the behemoths of Silicon Valley.

OpenAI is testing a new way to expose the complicated processes at work inside large language models. Researchers at the company can make an LLM produce what they call a confession, in which the model explains how it carried out a task and (most of the time) own up to any bad behavior. Figuring out why large language models do what they do--and in particular why they sometimes appear to lie, cheat, and deceive--is one of the hottest topics in AI right now. If this multitrillion-dollar technology is to be deployed as widely as its makers hope it will be, it must be made more trustworthy. OpenAI sees confessions as one step toward that goal. Sometimes geothermal hot spots are obvious, marked by geysers and hot springs on Earth's surface.

Generation IV (Gen-IV) nuclear power plants are envisioned to replace the current reactor fleet, bringing improvements in performance, safety, reliability, and sustainability. However, large cost investments currently inhibit the deployment of these advanced reactor concepts. Digital twins bridge real-world systems with digital tools to reduce costs, enhance decision-making, and boost operational efficiency. In this work, a digital twin framework is designed to operate the Gen-IV Fluoride-salt-cooled High-temperature Reactor, utilizing data-enhanced methods to optimize operational and maintenance policies while adhering to system constraints. The closed-loop framework integrates surrogate modeling, reinforcement learning, and Bayesian inference to streamline end-to-end communication for online regulation and self-adjustment. Reinforcement learning is used to consider component health and degradation to drive the target power generations, with constraints enforced through a Reference Governor control algorithm that ensures compliance with pump flow rate and temperature limits. These input driving modules benefit from detailed online simulations that are assimilated to measurement data with Bayesian filtering. The digital twin is demonstrated in three case studies: a one-year long-term operational period showcasing maintenance planning capabilities, short-term accuracy refinement with high-frequency measurements, and system shock capturing that demonstrates real-time recalibration capabilities when change in boundary conditions. These demonstrations validate robustness for health-aware and constraint-informed nuclear plant operation, with general applicability to other advanced reactor concepts and complex engineering systems.