Simulation has become a key part of scientific discovery – complementing experiment, helping guide engineering designs and providing insight into complex problems infeasible in the lab.

In a paper appearing in Nature Communications, researchers report a machine learning-based approach for producing phase diagrams, which are applicable to metastable materials that are far from equilibrium.

In a paper published in Advanced Materials, researchers report on a molecular additive that improves the performance of stretchable electronics made with a well-known conjugated polymer.

Ongoing research will help make it easier for power system operators and major regulatory bodies to understand the impact of distributed energy resources.

In a study published in Science Advances, researchers revealed unconventional arrested-motility states in ensembles of active discoidal particles powered by induced-charge electrophoresis.

In a report published in Small, researchers introduce ​“ingrained”, an automated framework for the fusion of atomic-resolution materials imaging simulations into the experimental images to which they correspond.

In a report published in PNAS, researchers report on a newly developed laser-pumped X-ray nanodiffraction imaging capability with 25 nm and 100 ps resolutions.

In a study published in Advanced Materials, researchers imaged for the first time the conduction channel formed in a memristor using an X-ray microscope at the Advanced Photon Source.

In a study published in Communications Physics, researchers reveal the presence of three types of magnetic order in an iron superconductor (LaFeAs_1-ₓPₓO).

In a study published in Proceedings of the National Academy of Sciences, researchers report that machine learning can reveal all the critical features within X-ray scattering ​“big data” required to determine the origin of structural correlations.