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Category: supercomputing – Page 2

New AI model advances fusion power research by predicting the success of experiments

Practical fusion power that can provide cheap, clean energy could be a step closer thanks to artificial intelligence. Scientists at Lawrence Livermore National Laboratory have developed a deep learning model that accurately predicted the results of a nuclear fusion experiment conducted in 2022. Accurate predictions can help speed up the design of new experiments and accelerate the quest for this virtually limitless energy source.

In a paper published in Science, researchers describe how their AI model predicted with a probability of 74% that ignition was the likely outcome of a small 2022 fusion experiment at the National Ignition Facility (NIF). This is a significant advance as the model was able to cover more parameters with greater precision than traditional supercomputers.

Currently, nuclear power comes from nuclear fission, which generates energy by splitting atoms. However, it can produce radioactive waste that remains dangerous for thousands of years. Fusion generates energy by fusing atoms, similar to what happens inside the sun. The process is safer and does not produce any long-term radioactive waste. While it is a promising energy source, it is still a long way from being a viable commercial technology.

A smart accelerator for qubits: Spin-orbit approach boosts both speed and stability

There are high hopes for quantum computers: they are supposed to perform specific calculations much faster than current supercomputers and, therefore, solve scientific and practical problems that are insurmountable for ordinary computers. The centerpiece of a quantum computer is the quantum bit, qubit for short, which can be realized in different ways—for instance, using the energy levels of atoms or the spins of electrons.

When making such qubits, however, researchers face a dilemma. On the one hand, a qubit needs to be isolated from its environment as much as possible. Otherwise, its quantum superpositions decay in a short time and the quantum calculations are disturbed. On the other hand, one would like to drive qubits as fast as possible in analogy with the clocking of classical bits, which requires a strong interaction with the environment.

Normally, these two conditions cannot be fulfilled at the same time, as a higher driving speed automatically entails a faster decay of the superpositions and, therefore, a shorter coherence time.

New AI Model May Predict Success Of Future Fusion Experiments, Saving Money And Fuel

What this means in real time is that researchers using these maps do not know if there are any errors or issues ahead of them, nor do they know if these errors are part of the research design. Nevertheless, this is all they have to work with, so they have to make a decision based on this limited information, and doing so will always have high costs in terms of the ignition attempt, which is expensive.

To overcome this, the team at the NIF created a new way to create these “maps” by merging past data with high-fidelity physics simulations and the knowledge of experts. This was then fed into a supercomputer that ran statistical assessments in the course of over 30 million CPU hours. Effectively, this allows the researchers to see all the ways that things can go wrong and to pre-emptively assess their experimental designs. This saves a lot of time and, more importantly, money.

The team tested this approach on an experiment they ran in 2022, and, after a few changes to the model’s physics, was able to predict the outcome with an accuracy above 70 percent.

Tesla Kills Dojo for AI6! Here’s Why

Questions to inspire discussion.

🚗 Q: How will AI6 be used in Tesla vehicles? A: AI6 will be used for FSD inference, with two chips in every car, enabling advanced autonomous driving capabilities.

🤖 Q: What role will AI6 play in Optimus? A: AI6 will enable on-device learning and reinforced learning in Optimus, enhancing its AI capabilities.

🔋 Q: Will AI6 be used in other Tesla products? A: AI6 will be integrated into every edge device produced by Tesla, including Tesla Semi, Mega Pack, and security cameras.

Technical Specifications.

💻 Q: What is the architecture of AI6? A: AI6 will use a cluster model of individual chips with a software layer on top, similar to Dojo 3 for training.

Continue reading “Tesla Kills Dojo for AI6! Here’s Why” | >

Quantum precision reached in modeling molten salt behavior

Scientists have developed a new machine learning approach that accurately predicted critical and difficult-to-compute properties of molten salts, materials with diverse nuclear energy applications.

In a Chemical Science article, Oak Ridge National Laboratory researchers demonstrated the ability to rapidly model salts in liquid and solid state with quantum chemical accuracy. Specifically, they looked at thermodynamic properties, which control how molten salts function in high-temperature applications. These applications include dissolving nuclear fuels and improving reliability of long-term reactor operations. The AI-enabled approach was made possible by ORNL’s supercomputer Summit.

“The exciting part is the simplicity of the approach,” said ORNL’s Luke Gibson. “In fewer steps than existing approaches, machine learning gets us to higher accuracy at a faster rate.”

Scientists explore real-time tsunami warning system on world’s fastest supercomputer

Scientists at Lawrence Livermore National Laboratory (LLNL) have helped develop an advanced, real-time tsunami forecasting system—powered by El Capitan, the world’s fastest supercomputer—that could dramatically improve early warning capabilities for coastal communities near earthquake zones.

The exascale El Capitan, which has a theoretical peak performance of 2.79 quintillion calculations per second, was developed at the National Nuclear Security Administration (NNSA). As described in a preprint paper selected as a finalist for the 2025 ACM Gordon Bell Prize, researchers at LLNL harnessed the machine’s full computing power in a one-time, offline precomputation step, prior to the system’s transition to classified national-security work. The goal: to generate an immense library of physics-based simulations, linking earthquake-induced seafloor motion to resulting .

The paper is published on the arXiv preprint server.

US finds missing particle that makes quantum computing fully possible

Researchers at the University of Southern California (USC) in the US turned to an often overlooked particle for storing and processing quantum information to overcome the fragility of quantum computers and make them more universal in the near future.

Positioning one such particle in a quantum computer can help overcome errors in quantum computing, a university press release said.

The age of quantum computing promises computations at speeds that will make even the fastest supercomputers of today appear like snails. These computers leverage quantum properties of materials to store information in quantum bits or ‘qubits’

“They’re Handing Supercomputer Power to Everyone”: Nexus Ignites Fierce Debate as Georgia Tech’s 400 Quadrillion-Operation AI Revolution Promises Unmatched Access for US Scientists

IN A NUTSHELL 🚀 Nexus aims to democratize access to advanced computing with its groundbreaking capabilities. 🔬 The supercomputer is set to revolutionize scientific research with over 400 quadrillion operations per second. 🌍 Nexus’s accessibility philosophy ensures researchers nationwide can utilize its powerful AI tools. 🔗 Georgia Tech collaborates with the University of Illinois for

Discarded particles dubbed ‘neglectons’ may unlock universal quantum computing

Quantum computers have the potential to solve problems far beyond the reach of today’s fastest supercomputers. But today’s machines are notoriously fragile. The quantum bits, or “qubits,” that store and process information are easily disrupted by their environment, leading to errors that quickly accumulate.

One of the most promising approaches to overcoming this challenge is topological quantum computing, which aims to protect by encoding it in the geometric properties of exotic particles called anyons. These particles, predicted to exist in certain two-dimensional materials, are expected to be far more resistant to noise and interference than conventional qubits.

“Among the leading candidates for building such a computer are Ising anyons, which are already being intensely investigated in condensed matter labs due to their potential realization in exotic systems like the fractional quantum Hall state and topological superconductors,” said Aaron Lauda, professor of mathematics, physics and astronomy at the USC Dornsife College of Letters, Arts and Sciences and the study’s senior author.

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