Research Highlights

New streaming connection leads to quicker data processing between Argonne’s Advanced Photon Source and Argonne Leadership Computing Facility.

Researchers use machine learning to study plasma instabilities in fusion experiments.

Argonne researchers create QuantEM, a modular compiler that automatically adds error detection to quantum programs, improving reliability while reducing manual effort.

PETSc (Portable, Extensible Toolkit for Scientific Computing) has been helping scientists solve complex problems for over 30 years.

Argonne helps develop innovative ways to design experiments and evaluate methods efficiently.

New method uses automated continual learning and smart data generation.

Insights from Argonne researchers on utilizing LLMs and AI for problem-solving and code development.

Scientists develop a better way to see chemical structures in three dimensions.

The active learning framework accelerates identification of anomalies in code and strengthens scientific workflows without the need for large sets of training data.

SQUEEZE (SZ) compresses data while preserving quality. Argonne researchers and SZ developers, Sheng Di and Franck Cappello, share development insights.

A new system from Argonne and Berkeley Laboratories helps scientists manage complex, AI-driven workflows—without modifying their code.

Argonne researcher Jeffrey Larson helped certify randomness from quantum computing, advancing privacy and fairness in technology.

Recent advances in strategies provide new capabilities for large language models.

Model uses historical data and patterns formed within the first minute of failure.

Multiobjective hyperparameter optimization can dramatically improve performance for ocean dynamics modeling.

QuCLEAR, a new framework, greatly reduces the gate count and circuit depth.

Researchers show that the quantum approximate optimization algorithm (QAOA) can achieve quantum speedups of a classically intractable problem

SZ3’s innovative approach to data compression wins IEEE best paper on big data award. Explore how it can transform scientific research.

Shadows and cutting may sound ominous, but they are being used to provide a more efficient method for the estimation of quantum observables.

Argonne researchers give future-focused flash talks on energy-efficient computing at critical Department of Energy workshop.

A team of researchers from Argonne National Laboratory has devised a new approach that significantly reduces the computational cost of x-ray ptychography, while maintaining high resolution.

In a recent article in Significance magazine, researchers addressed some of the factors affecting energy demands, with a particular focus on the role of statistics in energy forecasting.

Particle accelerators are vital tools for studying the structure and behavior of the particles that make up matter.

Computation has become an essential part of scientific research. Yet numerous questions remain about the role of the research software engineer (RSE) in that research.

The ever-increasing volume of scientific data necessitates new approaches to compress the data enough to fit simulation and experiment user constraints in terms of storage space, I/O speed, memory size.