The physical sciences community is increasingly taking advantage of the possibilities
offered by modern data science to solve problems in experimental chemistry and potentially …

Accelerator physics relies on numerical algorithms to solve optimization problems in online
accelerator control and tasks such as experimental design and model calibration in …

Transformer models coupled with a simplified molecular line entry system (SMILES) have
recently proven to be a powerful combination for solving challenges in cheminformatics …

The directed self-assembly (DSA) of block copolymers (BCPs) is a powerful approach to
fabricate complex nanostructure arrays, but finding morphologies that emerge with changes …

Powerful detectors at modern experimental facilities routinely collect data at multiple GB/s.
Online analysis methods are needed to enable the collection of only interesting subsets of …

The unparalleled success of artificial intelligence (AI) in the technology sector has catalyzed
an enormous amount of research in the scientific community. It has proven to be a powerful …

Research process automation–the reliable, efficient, and reproducible execution of linked
sets of actions on scientific instruments, computers, data stores, and other resources–has …

We demonstrate the first live, autonomous control over neutron diffraction experiments by
develo** and deploying ANDiE: the autonomous neutron diffraction explorer. Neutron …

Both experimental and computational methods for the exploration of structure, functionality,
and properties of materials often necessitate the search across broad parameter spaces to …

Physics‐driven discovery in an autonomous experiment has emerged as a dream
application of machine learning in physical sciences. Here, this work develops and …