Deep learning with reflection high-energy electron diffraction images to predict cation ratio in Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$ thin films
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909571099918336 |
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| author | Harris, Sumner B. Gemperline, Patrick T. Rouleau, Christopher M. Vasudevan, Rama K. Comes, Ryan B. |
| author_facet | Harris, Sumner B. Gemperline, Patrick T. Rouleau, Christopher M. Vasudevan, Rama K. Comes, Ryan B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18523 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep learning with reflection high-energy electron diffraction images to predict cation ratio in Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$ thin films Harris, Sumner B. Gemperline, Patrick T. Rouleau, Christopher M. Vasudevan, Rama K. Comes, Ryan B. Materials Science 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. |
| title | Deep learning with reflection high-energy electron diffraction images to predict cation ratio in Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$ thin films |
| topic | Materials Science |
| url | https://arxiv.org/abs/2501.18523 |