Computational workflows are a common class of application on supercomputers, yet the
loosely coupled and heterogeneous nature of workflows often fails to take full advantage of …

Materials development is slow and expensive, taking decades from inception to fielding. For
materials research at extremes, the situation is even more demanding, as the desired …

Autonomous experimentation (AE) is an emerging paradigm that seeks to automate the
entire workflow of an experiment, including—crucially—the decision-making step. Beyond …

Machine learning interatomic potentials have emerged as a powerful tool for bypassing the
spatiotemporal limitations of ab initio simulations, but major challenges remain in their …

Unraveling the liquid structure of multicomponent molten salts is challenging due to the
difficulty in conducting and interpreting high-temperature diffraction experiments. Motivated …

Increasingly, scientific discovery requires sophisticated and scalable workflows. Workflows
have become the``new applications,''wherein multi-scale computing campaigns comprise …

Recent years have seen a surge in deep learning approaches to accelerate numerical
solvers, which provide faithful but computationally intensive simulations of the physical …

Artificial intelligence (AI) methods are poised to revolutionize intellectual work, with
generative AI enabling automation of text analysis, text generation, and simple decision …

The transformative impact of modern computational paradigms and technologies, such as
high-performance computing (HPC), quantum computing, and cloud computing, has opened …

Just like so many other topics that have been adopted into the realm of machine learning
and AI—deep learning, digital twins, active learning, and so on—Autonomous …