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一个基于机器学习的框架,用于在原子尺度上绘制的地图

Qingkun Zhao^1,2, 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

概括

一个新的机器学习框架,Atom-H,使得原子的原子尺度成像. 这一突破为的脆化和金属中的物质降解提供了关键的见解.

科学领域:

材料科学材料科学材料科学
纳米技术纳米技术
计算科学计算科学

背景情况:

对于清洁能源和工业至关重要,但会导致材料降解,如脆性.
在原子尺度上像气这样的光原子的成像是一个重要的科学挑战.
了解与材料的相互作用对于开发强大的技术至关重要.

研究的目的:

介绍Atom-H,一种用于原子规模气成像的机器学习框架.
为了可视化的分布和材料缺陷的局部应力.
为引发的机械行为提供原子层面的洞察力.

主要方法:

开发了一种通用的机器学习框架,名为Atom-H.
使用高分辨率电子显微镜图像作为输入数据.
应用框架来分析金属材料中的分布和应力.

主要成果:

原子-H精确地对原子和局部应力在晶格缺陷 (位移,粒边界,裂,相边界) 的图像.
该框架提供了对纯金属 (Ni,Fe,Ti) 和合金 (FeCr) 的控制机械行为的原子级洞察力.
证明了能够高精度地绘制"隐形"原子的能力.

结论:

原子-H为研究缩提供了一个强大的新工具.
该框架在材料科学和工程领域有直接的应用.
预计Atom-H将推进跨科学学科的"隐形"原子的绘制.

关键词:

原子尺度成像原子尺度成像气的化物是化物.格子缺陷格子缺陷机器学习是机器学习.

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