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

Qingkun Zhao1,2, Qi Zhu2, Zhenghao Zhang1

  • 1Department of Engineering Mechanics, State Key Laboratory of Fluid Power and Mechatronic Systems, Center for X-mechanics, Zhejiang University, Hangzhou 310027, People's Republic of China.

Proceedings of the National Academy of Sciences of the United States of America
|September 16, 2024
PubMed
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A new machine learning framework, Atom-H, enables atomic-scale imaging of hydrogen atoms. This breakthrough provides crucial insights into hydrogen embrittlement and material degradation in metals.

Keywords:
atomic scale imaginghydrogen embrittlementlattice defectmachine learning

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Computational Science

Background:

  • Hydrogen is vital for clean energy and industry but causes material degradation like hydrogen embrittlement.
  • Imaging light atoms like hydrogen at the atomic scale is a significant scientific challenge.
  • Understanding hydrogen's interaction with materials is crucial for developing robust technologies.

Purpose of the Study:

  • To introduce Atom-H, a machine learning framework for atomic-scale hydrogen imaging.
  • To enable visualization of hydrogen distribution and local stresses at material defects.
  • To provide atomic-level insights into hydrogen-induced mechanical behaviors.

Main Methods:

  • Developed a versatile and generalizable machine learning framework named Atom-H.
  • Utilized high-resolution electron microscope images as input data.
  • Applied the framework to analyze hydrogen distribution and stress in metallic materials.

Main Results:

  • Atom-H accurately images hydrogen atoms and local stresses at lattice defects (dislocations, grain boundaries, cracks, phase boundaries).
  • The framework provides atomic-scale insights into hydrogen-governed mechanical behaviors in pure metals (Ni, Fe, Ti) and alloys (FeCr).
  • Demonstrated the capability to map "invisible" atoms with high precision.

Conclusions:

  • Atom-H offers a powerful new tool for studying hydrogen embrittlement.
  • The framework has immediate applications in materials science and engineering.
  • Atom-H is expected to advance the mapping of "invisible" atoms across scientific disciplines.