arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2501.18523cond-mat.mtrl-sci
收藏

利用反射高能电子衍射图像深度学习预测Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$薄膜中阳离子比例

Deep learning with reflection high-energy electron diffraction images to predict cation ratio in Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$ thin films

Sumner B. Harris, Patrick T. Gemperline, Christopher M. Rouleau, Rama K. Vasudevan, Ryan B. Comes

AI总结:

本研究利用门控卷积神经网络分析反射高能电子衍射图像,在小数据集上准确预测Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$薄膜阳离子比例,将定性原位诊断转化为定量测量。

摘要:

机器学习(ML)与原位诊断相结合,通过揭示合成条件与材料性质之间的关系,为加速、理解和控制薄膜合成提供了一种变革性方法。在本研究中,我们展示了深度学习在利用脉冲激光沉积过程中采集的反射高能电子衍射图像预测Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$薄膜化学计量比方面的应用。一个用于回归Sr原子分数的门控卷积神经网络在仅含31个样本的小数据集上实现了准确预测。可解释人工智能技术揭示了Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$薄膜中衍射条纹特征与阳离子化学计量比之间此前未知的关联。我们的结果表明,机器学习可以将一种通常仅限于定性评估的普适原位诊断工具转变为对连续值薄膜性质的定量替代测量。此类方法对于实现实时控制、自主工作流程以及加速传统合成方法至关重要。

英文摘要:

Machine learning (ML) with in situ diagnostics offers a transformative approach to accelerate, understand, and control thin film synthesis by uncovering relationships between synthesis conditions and material properties. In this study, we demonstrate the application of deep learning to predict the stoichiometry of Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$ thin films using reflection high-energy electron diffraction images acquired during pulsed laser deposition. A gated convolutional neural network trained for regression of the Sr atomic fraction achieved accurate predictions with a small dataset of 31 samples. Explainable AI techniques revealed a previously unknown correlation between diffraction streak features and cation stoichiometry in Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$ thin films. Our results demonstrate how ML can be used to transform a ubiquitous in situ diagnostic tool, that is usually limited to qualitative assessments, into a quantitative surrogate measurement of continuously valued thin film properties. Such methods are critically needed to enable real-time control, autonomous workflows, and accelerate traditional synthesis approaches.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑