A machine learning-based framework for mapping hydrogen at the atomic scale.

ABSTRACT: Hydrogen, the lightest and most abundant element in the universe, plays essential roles in a variety of clean energy technologies and industrial processes. For over a century, it has been known that hydrogen can significantly degrade the mechanical properties of materials, leading to issues like hydrogen embrittlement. A major challenge that has significantly limited scientific advances in this field is that light atoms like hydrogen are difficult to image, even with state-of-the-art microscopic techniques. To address this challenge, here, we introduce Atom-H, a versatile and generalizable machine learning-based framework for imaging hydrogen atoms at the atomic scale. Using a high-resolution electron microscope image as input, Atom-H accurately captures the distribution of hydrogen atoms

SUBMITTER: Zhao Q 

PROVIDER: S-EPMC11441556 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature