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

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 and local stresses at lattice atomic- scale insights into hydrogen- governed mechanical behaviors in metallic materials, an immediate impact on current research into hydrogen embrittlement and is expected to have far- reaching implications for mapping "invisible" atoms in other scientific disciplines.