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.

The AI for Nuclear Energy workshop at Oak Ridge National Laboratory evaluated the potential of Large Language Models (LLMs) to accelerate fusion and fission research. Fourteen interdisciplinary teams explored diverse nuclear science challenges using ChatGPT, Gemini, Claude, and other AI models over a single day. Applications ranged from developing foundation models for fusion reactor control to automating Monte Carlo simulations, predicting material degradation, and designing experimental programs for advanced reactors. Teams employed structured workflows combining prompt engineering, deep research capabilities, and iterative refinement to generate hypotheses, prototype code, and research strategies. Key findings demonstrate that LLMs excel at early-stage exploration, literature synthesis, and workflow design, successfully identifying research gaps and generating plausible experimental frameworks. However, significant limitations emerged, including difficulties with novel materials designs, advanced code generation for modeling and simulation, and domain-specific details requiring expert validation. The successful outcomes resulted from expert-driven prompt engineering and treating AI as a complementary tool rather than a replacement for physics-based methods. The workshop validated AI's potential to accelerate nuclear energy research through rapid iteration and cross-disciplinary synthesis while highlighting the need for curated nuclear-specific datasets, workflow automation, and specialized model development. These results provide a roadmap for integrating AI tools into nuclear science workflows, potentially reducing development cycles for safer, more efficient nuclear energy systems while maintaining rigorous scientific standards.

Optimally designing molten salt applications requires knowledge of their thermophysical properties, but existing databases are incomplete, and experiments are challenging. Ideal mixing and Redlich-Kister models are computationally cheap but lack either accuracy or generality. To address this, a transfer learning approach using deep neural networks (DNNs) is proposed, combining Redlich-Kister models, experimental data, and ab initio properties. The approach predicts molten salt density with high accuracy ($r^{2}$ > 0.99, MAPE < 1%), outperforming the alternatives.

Technology developed at Argonne can help narrow the field of candidates for molten salts, a new study demonstrates. Scientists are searching for new materials to advance the next generation of nuclear power plants. In a recent study, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory showed how artificial intelligence could help pinpoint the right types of molten salts, a key component for advanced nuclear reactors. The ability to absorb and store heat makes molten salt important to clean energy and national climate goals. Molten salts can serve as both coolant and fuel in nuclear power reactors that generate electricity without emitting greenhouse gases.

The future of clean energy is hot. Temperatures hit 800 Celsius in parts of solar energy plants and advanced nuclear reactors. Finding materials that can stand that type of heat is tough. So experts look to Mark Messner for answers. A principal mechanical engineer at the U.S. Department of Energy's (DOE) Argonne National Laboratory, Messner is among a group of engineers who are discovering better ways to predict how materials will behave under high temperatures and pressures.

A new take on artificial intelligence may open many doors for 3D printing and designing advanced nuclear reactors. The future of clean energy is hot. Temperatures hit 800 Celsius in parts of solar energy plants and advanced nuclear reactors. Finding materials that can stand that type of heat is tough. So experts look to Mark Messner for answers.

Like a kid burning holes in their toys using a magnifying glass, solar furnaces essentially do the same thing on a much grander scale. The larger an array of reflectors you can build, the bigger the sun-focusing lens you get. But a new startup is promising a better way to build solar furnaces using AI to reduce their footprint while boosting their power output. Even Donald Trump's solar tariffs and desire to prop up the coal industry can't stop renewable… In recent years the price of solar energy has dropped dramatically, and it's estimated that the cost of building plants like Nevada's Eagle Shadow Mountain Solar Farm, which officially begins generating power sometime in 2021, is actually cheaper than just operating existing coal or natural gas plants. Harnessing the immense energy of the sun is an obvious alternative to relying on fossil fuels to generate power, but in order to generate the temperatures needed to create molten salt, which is what solar plants like these use to create steam to turn electrical generators, temperatures of around 600 C are needed, which requires a vast array of reflectors (or heliostats), and a big chunk of land on which to install them.

In the 1970s, the U.S. Department of Energy poured money into making practical a miraculous technology: the ability to convert sunlight into electricity. Solar energy was a pipe dream, far too expensive and unreliable to be considered a practical power source. But yesterday's moon shot is today's reality. The expense of solar power has fallen more quickly than expected, with installations costing about 80% less today than a decade ago. Alternative energy (like wind and solar) is now often cheaper than conventional energy (like coal and gas).

Google parent Alphabet Inc. GOOGL 0.58% is pitching an idea to store power from renewable energy in tanks of molten salt and cold liquid, an example of the tech giant trying to marry its far-reaching ambitions with business demand. Alphabet's research lab, dubbed X, said Monday that it has developed plans to store electricity generated from solar panels or wind turbines as thermal energy in hot salt and cold liquids, such as antifreeze. The lab is seeking partners in the energy industry, including power-plant developers and utilities, to build a prototype to plug into the electrical grid. Whether the project, called Malta, ever comes to market depends as much on a sound business model as it does on science. Academics said the technology is likely years away from market, if it ever makes it.