Stellarators are magnetic confinement devices under active development to deliver steady-state carbon-free fusion energy. Their design involves a high-dimensional, constrained optimization problem that requires expensive physics simulations and significant domain expertise. Recent advances in plasma physics and open-source tools have made stellarator optimization more accessible. However, broader community progress is currently bottlenecked by the lack of standardized optimization problems with strong baselines and datasets that enable data-driven approaches, particularly for quasi-isodynamic (QI) stellarator configurations, considered as a promising path to commercial fusion due to their inherent resilience to current-driven disruptions. Here, we release an open dataset of diverse QI-like stellarator plasma boundary shapes, paired with their ideal magnetohydrodynamic (MHD) equilibria and performance metrics. We generated this dataset by sampling a variety of QI fields and optimizing corresponding stellarator plasma boundaries. We introduce three optimization benchmarks of increasing complexity: (1) a single-objective geometric optimization problem, (2) a simple-to-build QI stellarator, and (3) a multi-objective ideal-MHD stable QI stellarator that investigates trade-offs between compactness and coil simplicity. For every benchmark, we provide reference code, evaluation scripts, and strong baselines based on classical optimization techniques. Finally, we show how learned models trained on our dataset can efficiently generate novel, feasible configurations without querying expensive physics oracles.

The United States has formed a $1bn partnership with Advanced Micro Devices (AMD) to construct two supercomputers that will tackle large scientific problems ranging from nuclear power to cancer treatments to national security. The Reuters news agency first reported the new partnership, citing Energy Secretary Chris Wright and AMD CEO Lisa Su. The machines can accelerate the process of making scientific discoveries in areas the US is focused on. Energy Secretary Wright said the systems would "supercharge" advances in nuclear power and fusion energy, technologies for defence and national security, and the development of drugs. Scientists and companies are trying to replicate fusion, the reaction that fuels the sun, by jamming light atoms in a plasma gas under intense heat and pressure to release massive amounts of energy.

Tech giant Microsoft is one of the many companies embracing AI. So it's perhaps ironic that Microsoft's co-founder – the multi-billionaire Bill Gates – has given a warning over its potential dangers. Speaking in London this week, Gates called AI a'wonderful' technology that can save humans from climate change and disease. But he warned that it needs to be used'by people with good intent', as it could be used by criminals'engaged in cyber attacks or political interference'. Gates, one of the 10 richest humans in the world, said: 'The defence has to be smarter than the offence.

An internationally leading robotics initiative that enables academia and industry to find innovative solutions to real world challenges, celebrated its success with a Hot Robotics Symposium hosted across three UK regions last week. The National Nuclear User Facility (NNUF) for Hot Robotics is a government funded initiative that supports innovation in the nuclear sector by making world-leading testing facilities, sensors and robotic equipment easily accessible to academia and industry. Ground-breaking, impactful research in robotics and artificial intelligence will benefit the UK's development of fusion energy as safe, low carbon and sustainable energy source in addition to adjacent sectors such as nuclear decommissioning, space, and mobile applications. Visitors to UKAEA's RACE (UK Atomic Energy Authority / Remote Applications in Challenging Environments) in Oxfordshire, the University of Bristol facility in Fenswood Farm (North Somerset), and the National Nuclear Laboratory in Cumbria, were treated to a host of robots in action, tours and a packed speaker programme. A combination of robotic manipulators, ground, aerial and underwater vehicles along with deployment robots, plant mock-ups, and supporting infrastructure, were all showcased to demonstrate the breadth of the scheme.

In 2021, MIT researchers made advances toward fusion energy, confirmed Stephen Hawking's black hole theorem, developed a Covid-detecting face mask, and created a programmable fiber. All were among the year's top research stories on MIT News. The year's popular research stories include a promising new approach to cancer immunotherapy, the confirmation of a 50-year-old theorem, and a major fusion breakthrough. Despite the pandemic's disruptions, MIT's research community still found a way to generate a number of impressive research breakthroughs in 2021. In the spirit of reflection that comes with every new orbit around the sun, below we count down 10 of the most-viewed research stories on MIT News from the past year.

Machine learning, a technique used in the artificial intelligence (AI) software behind self-driving cars and digital assistants, now enables scientists to address key challenges to harvesting on Earth the fusion energy(link is external) that powers the sun and stars. The technique recently empowered physicist Dan Boyer of the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) to develop fast and accurate predictions for advancing control of experiments in the National Spherical Torus Experiment-Upgrade (NSTX-U) -- the flagship fusion facility at PPPL that is currently under repair. Such AI predictions could improve the ability of NSTX-U scientists to optimize the components of experiments that heat and shape the magnetically confined plasma(link is external) that fuels fusion experiments. By optimizing the heating and shaping of the plasma scientists will be able to more effectively study key aspects of the development of burning plasmas -- largely self-heating fusion reactions -- that will be critical for ITER, the international experiment under construction in France, and future fusion reactors. "This is a step toward what we should do to optimize the actuators," said Boyer, author of a paper(link is external) in Nuclear Fusion that describes the machine learning tactics.

Machine learning, a technique used in the artificial intelligence (AI) software behind self-driving cars and digital assistants, now enables scientists to address key challenges to harvesting on Earth the fusion energy that powers the sun and stars. The technique recently empowered physicist Dan Boyer of the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) to develop fast and accurate predictions for advancing control of experiments in the National Spherical Torus Experiment-Upgrade (NSTX-U)--the flagship fusion facility at PPPL that is currently under repair. Such AI predictions could improve the ability of NSTX-U scientists to optimize the components of experiments that heat and shape the magnetically confined plasma that fuels fusion experiments. By optimizing the heating and shaping of the plasma scientists will be able to more effectively study key aspects of the development of burning plasmas--largely self-heating fusion reactions--that will be critical for ITER, the international experiment under construction in France, and future fusion reactors. "This is a step toward what we should do to optimize the actuators," said Boyer, author of a paper in Nuclear Fusion that describes the machine learning tactics.

For more than four decades, University of California, San Diego, Professor of Physics Patrick H. Diamond and his research group have been advancing our understanding of fundamental concepts in plasma physics. Most recently, Diamond worked with graduate student Robin Heinonen on a model reduction study that used the Extreme Science and Engineering Discovery Environment (XSEDE)-allocated Comet supercomputer at the San Diego Supercomputer Center at UC San Diego to showcase how machine learning produced a new model for plasma turbulence. Plasmas have many applications, including fusion energy. When light nuclei fuse together, the mass of the products is less than that of the reactants, and the missing mass becomes energy – hence Albert Einstein's famous E mc2 equation. In order for this to occur, temperatures must literally reach astronomical levels, such as those found in the Sun's core.

This snapshot of turbulence density and vorticity from a simulation using SDSC's'Comet' supercomputer illustrates a notable physics concept: the formation of zonal (i.e. For more than four decades, UC San Diego Professor of Physics Patrick H. Diamond and his research group have been advancing fundamental concepts in plasma physics, which is an important aspect of furthering advancements in fusion energy. Most recently, Diamond worked with graduate student Robin Heinonen on a model reduction study that used the Comet supercomputer at the San Diego Supercomputer Center at the University of California San Diego to show how machine learning produced a novel model for plasma turbulence. Diamond and Heinonen say that advances in machine learning, such as new deep learning techniques, have provided them with new tools to better understand the self-organization process that emerges from what the researchers term as a seemingly chaotic process. "Turbulence and its transport is chaotic in a sense, but this chaos is ordered and constrained," said Heinonen, who co-authored Turbulence Model Reduction by Deep Learning with Diamond in the academic journal entitled Physical Review E. "Moreover, in certain turbulent systems, the chaos conspires to spontaneously form large, long-lived coherent structures and in many cases, we only have a tenuous understanding of why and now. There are definitely aspects of structure formation and self-organization which we do understand, but it's still an active area of research."

For more than four decades, UC San Diego Professor of Physics Patrick H. Diamond and his research group have been advancing fundamental concepts in plasma physics, which is an important aspect of furthering advancements in fusion energy. Most recently, Diamond worked with graduate student Robin Heinonen on a model reduction study that used the Comet supercomputer at the San Diego Supercomputer Center at the University of California San Diego to showcase how machine learning produced a novel model for plasma turbulence. Diamond and Heinonen say that advances in machine learning, such as new deep learning techniques, have provided them with new tools to better understand the self-organization process that emerges from what the researchers term as a seemingly chaotic process. "Turbulence and its transport is chaotic in a sense, but this chaos is ordered and constrained," said Heinonen, who co-authored Turbulence Model Reduction by Deep Learning with Diamond in the academic journal entitled Physical Review E. "Moreover, in certain turbulent systems, the chaos conspires to spontaneously form large, long-lived coherent structures and in many cases, we only have a tenuous understanding of why and now. There are definitely aspects of structure formation and self-organization which we do understand, but it's still an active area of research."