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 …

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 …

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

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

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

A growing disparity between supercomputer computation speeds and I/O rates means that it
is rapidly becoming infeasible to analyze supercomputer application output only after that …

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

Workflows are critical for scientific discovery. However, the sophistication, heterogeneity,
and scale of workflows make building, testing, and optimizing them increasingly challenging …

Scientific computing communities increasingly run their experiments using complex data-
and compute-intensive workflows that utilize distributed and heterogeneous architectures …

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