As demand for clean energy increases, experts at Argonne are helping drive innovation with decision makers.

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 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 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.

Argonne is co-leading efforts to increase U.S. competitiveness in manufacturing fuel cells that can power everything from cars to buildings.

For a study published in Patterns, researchers developed a predictive framework for zinc blende semiconductors and impurity atoms from across the periodic table.

For a study published in ACS Energy Letters, researchers elucidated the pathway for photon upconversion in a perovskite/rubrene bilayer.

In a study published in Nature Communications, researchers report a reinforcement learning-based algorithm for developing molecular models that predict potential energy surfaces of elemental nanoclusters and bulk systems.

For a study published in Science, researchers developed and tested a perovskite nickelate with which computer chips can be designed to reconfigure their circuits when presented with new information.

In a paper published in Nanoscale, researchers report characterizing hot electrons (above the bandgap) by ultrafast optical spectroscopy of optically excited colloidal quantum wells.