| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-07-09 |
| タイトル |
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タイトル |
An interpretable and transferrable vision transformer model for rapid materials spectra classification |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Chen, Zhenru
Xie, Yunchao
Wu, Yuchao
Lin, Yuyi
冨谷, 茂隆
Lin, Jian
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Rapid analysis of materials characterization spectra is pivotal for preventing the accumulation of unwieldy datasets, thus accelerating subsequent decision-making. However, current methods heavily rely on experience and domain knowledge, which not only proves tedious but also makes it hard to keep up with the pace of data acquisition. In this context, we introduce a transferable Vision Transformer (ViT) model for the identification of materials from their spectra, including XRD and FTIR. First, an optimal ViT model was trained to predict metal organic frameworks (MOFs) from their XRD spectra. It attains prediction accuracies of 70%, 93%, and 94.9% for Top-1, Top-3, and Top-5, respectively, and a shorter training time of 269 seconds ($223C30% faster) in comparison to a convolutional neural network model. The dimension reduction and attention weight map underline its adeptness at capturing relevant features in the XRD spectra for determining the prediction outcome. Moreover, the model can be transferred to a new one for prediction of organic molecules from their FTIR spectra, attaining remarkable Top-1, Top-3, and Top-5 prediction accuracies of 84%, 94.1%, and 96.7%, respectively. The introduced ViT-based model would set a new avenue for handling diverse types of spectroscopic data, thus expediting the materials characterization processes. |
| 書誌情報 |
en : Digital Discovery
巻 3,
号 2,
p. 369-380,
ページ数 12,
発行日 2024-02-01
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| 出版者 |
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出版者 |
Royal Society of Chemistry |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2635-098X |
| 出版者版DOI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1039/D3DD00198A |
| 出版者版URI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
URI |
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関連識別子 |
https://pubs.rsc.org/en/content/articlehtml/2024/dd/d3dd00198a |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/3.0/ |
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権利情報 |
$00A9 2024 The Author(s). Published by the Royal Society of Chemistry. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. |
| 著者版フラグ |
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出版タイプ |
VoR |
| 助成情報 |
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助成機関名 |
National Science Foundation |
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研究課題番号 |
2154428 |
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研究課題名 |
Collaborative Research: A Data-driven Closed-loop Framework for De Novo Generation of Molecules with Targeted Properties |
| 助成情報 |
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助成機関名 |
U.S. Army Corps of Engineers, ERDC |
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研究課題番号 |
W912HZ-21-2-0050 |
| 助成情報 |
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助成機関名 |
DOE National Energy Technology Laboratory |
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研究課題番号 |
DE-FE0031988 |
| 助成情報 |
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助成機関名 |
Sony Group Corporation |
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研究課題名 |
Sony Research Award Program (2020) |
fulltext (2.0 MB)
