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

• 成果类型: Article
• 署名作者: Zhao, Qingkun; Zhu, Qi; Zhang, Zhenghao; Yin, Binglun; Gao, Huajian; Zhou, Haofei
• 署名单位: Zhejiang University; Nanyang Technological University; Tsinghua University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9289
• DOI: 10.1073/pnas.2410968121
• 发表日期: 2024-09-24
• 关键词: in-situ; grain-boundaries; embrittlement; interstitials

摘要

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.