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 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.

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

Argonne researchers are helping to lower the cost and increase the efficiency of pumping hydrogen at refueling stations.

Wind farms are increasingly gaining attention as an alternative energy source. But the wakes generated by the wind turbines lead to energy losses, significantly degrading the efficiency of wind power plants.

H2@Scale is a vision to enable affordable, large-scale hydrogen production, storage, distribution, and utilization to advance decarbonization efforts.

Today’s computers are providing enormous power, enabling scientists to tackle complex problems previously considered intractable.

The final workshop report for the 2021 Randomized Algorithms for Scientific Computing (RASC) workshop has been released.

Argonne National Laboratory has a long and distinguished record of working with special functions.