Argonne maintains a wide-ranging science and technology portfolio that seeks to address complex challenges in interdisciplinary and innovative ways. Below is a list of all articles, highlights, profiles, projects, and organizations related specifically to nanoscience and nanotechnology.
In a Nature Communications article, a team led by Center for Nanoscale Materials researchers introduces a machine learning workflow of models for water transformations that increases accuracy at lower computational cost.
Two new methods reduce noise and remove errors in quantum observables by focusing on individual noise sources. They add little qubit overhead and can be used in quantum sensing and general quantum experimentation, as well as quantum computing.
Is it possible to predict what type of material an unidentified element will be in bulk quantities solely based on the properties it exhibits over a limited range of the subnano to nano size régime? It is, according to Argonne scientists.