The development of artificial intelligence (AI) in fusion research has taken a significant leap forward. A public-private partnership between Commonwealth Fusion Systems (CFS), the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL), and Oak Ridge National Laboratory has led to the creation of HEAT-ML, an AI approach that rapidly finds and simulates “magnetic shadows” in fusion vessels: safe havens protected from intense heat plasma.

HEAT-ML uses a deep neural network to learn how to calculate shadow masks, which are 3D maps of specific areas on internal components shielded from direct heat. This AI surrogate was trained using a database of approximately 1,000 SPARC simulations and can now simulate the same calculations in mere milliseconds, as opposed to the previous 30 minutes.

The goal is to create software that significantly speeds up fusion system design and enables good decision-making during operations by adjusting plasma settings to prevent potential problems. HEAT-ML was specifically designed for a small part of the SPARC tokamak under construction by CFS but has the potential to be expanded to generalize the calculation of shadow masks for exhaust systems of any shape and size, as well as other plasma-facing components.

Researchers believe that this AI breakthrough could pave the way for faster fusion system design, enabling good decision-making during operations, and potentially leading to limitless amounts of electricity on Earth.

The study of atomic-scale phenomena has led researchers to a groundbreaking discovery that could reshape the design of quantum technologies and ultrathin electronics. Yichao Zhang, an assistant professor in the University of Maryland Department of Materials Science and Engineering, has developed an innovative technique called “electron ptychography” to directly image the thermal vibrations of individual atoms. This achievement was published in the journal Science on July 24.

Two-dimensional materials, which are sheet-like structures a few nanometers thick, have been explored as new components for next-generation quantum and electronic devices. A crucial feature of twisted two-dimensional materials is “moiré phasons,” essential to understanding their thermal conductivity, electronic behavior, and structural order. However, detecting moiré phasons experimentally had proven challenging, hindering further research in these revolutionary materials.

Zhang’s team overcame this challenge by employing electron ptychography, a technique that achieved the highest resolution documented (better than 15 picometers) and detected the blurring of individual atoms caused by thermal vibrations. This groundbreaking study revealed that spatially localized moiré phasons dominate thermal vibrations in twisted two-dimensional materials, fundamentally reshaping our understanding of their impact.

The breakthrough confirmed long-standing theoretical predictions of moiré phasons and demonstrated that electron ptychography can be used to map thermal vibrations with atomic precision for the first time. This achievement opens up new possibilities for exploring previously hidden physics in quantum materials.

“This is like decoding a hidden language of atomic motion,” said Zhang. “Electron ptychography lets us see these subtle vibrations directly. Now we have a powerful new method to explore previously hidden physics, which will accelerate discoveries in two-dimensional quantum materials.”

Zhang’s research team will next focus on resolving how thermal vibrations are affected by defects and interfaces in quantum and electronic materials. Controlling the thermal vibration behavior of these materials could enable the design of novel devices with tailored thermal, electronic, and optical properties – paving the way for advances in quantum computing, energy-efficient electronics, and nanoscale sensors.

The world’s climate is on the brink of disaster, with brutal droughts, melting glaciers, and devastating natural disasters ravaging our planet every year. A significant contributor to this crisis is the constant emission of carbon dioxide into the atmosphere, primarily through concrete production. However, a team of researchers at the USC Viterbi School of Engineering has made a groundbreaking discovery that could change everything.

Led by Professors Aiichiro Nakano and Ken-Ichi Nomura, the team developed an artificial intelligence-driven simulation model called Allegro-FM. This revolutionary AI model can simulate the behavior of billions of atoms simultaneously, opening new possibilities for materials design and discovery at unprecedented scales.

The breakthrough lies in the model’s scalability, which is roughly 1,000 times larger than conventional approaches. Allegro-FM demonstrated 97.5% efficiency when simulating over four billion atoms on the Aurora supercomputer at Argonne National Laboratory. This represents computational capabilities that can accurately predict molecular behavior for applications ranging from cement chemistry to carbon storage.

The implications are staggering. Concrete is a fire-resistant material, making it an ideal building choice in areas prone to wildfires. However, concrete production is also a significant emitter of carbon dioxide, a particularly concerning environmental problem in cities like Los Angeles. Allegro-FM has been shown to be carbon neutral, making it a better choice than other concrete.

Moreover, this breakthrough doesn’t only solve one problem. Ancient Roman concrete has lasted for over 2,000 years, whereas modern concrete typically lasts about 100 years on average. The recapture of CO2 can help extend the lifespan of concrete structures, making them more robust and durable.

The professors leading this research have an appreciation for how AI has been an accelerator of their complex work. Normally, to simulate the behavior of atoms, they would need a precise series of mathematical formulas. However, the last two years have changed the way they approach this challenge.

“Now, because of this machine-learning AI breakthrough, instead of deriving all these quantum mechanics from scratch, researchers are taking [the] approach of generating a training set and then letting the machine learning model run,” Nomura said.

This makes their process much faster and more efficient in its technology use. Allegro-FM can accurately predict “interaction functions” between atoms, which would require lots of individual simulations normally.

The traditional approach is to simulate a certain set of materials. However, this new system is also a lot more efficient on the technology side, with AI models making lots of precise calculations that used to be done by a large supercomputer, simplifying tasks and freeing up that supercomputer’s resources for more advanced research.

“[The AI can] achieve quantum mechanical accuracy with much, much smaller computing resources,” Nakano said.

Nomura and Nakano say their work is far from over. They will certainly continue this concrete study research, making more complex geometries and surfaces. This research was published recently in The Journal of Physical Chemistry Letters and was featured as the journal’s cover image.