An interpretable and transferrable vision transformer model for rapid materials spectra classification-Reference-Cited by-同舟云学术

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An interpretable and transferrable vision transformer model for rapid materials spectra classification

Published:2024 Issue: Volume: Page:
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Chen Zhenru¹,Xie Yunchao¹^ORCID,Wu Yuchao¹,Lin Yuyi¹,Tomiya Shigetaka²,Lin Jian¹^ORCID

Affiliation:

1. Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO 65201, USA

2. Data Science Center, Graduate School of Advanced Science and Technology, Material Science Division, Nara Institute of Science and Technology (NAIST), 8916-5 Takayamacho, Ikoma City, Nara Prefecture 630-0192, Japan

Abstract

An interpretable and transferrable Vision Transformer (ViT) model was developed for classifying individual materials from their XRD and FTIR spectra.

Funder

National Science Foundation

Engineer Research and Development Center

National Energy Technology Laboratory

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D3DD00198A

Reference48 articles.

1. Busting the myths around public investment in clean energy

2. Accelerating the discovery of materials for clean energy in the era of smart automation

3. How to explore chemical space using algorithms and automation

4. Automated Experimentation Powers Data Science in Chemistry

5. Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

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