Work

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

Year: 2023

Type: article

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

Source: Digital Discovery

DOI: 10.1039/d3dd00198a

Authors: Zhenru Chen, Yunchao Xie, Yuchao Wu, Yuyi Lin, Shigetaka Tomiya, Jian Lin

Institutions: University of Missouri, Nara Institute of Science and Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, U.S. Department of Energy, National Energy Technology Laboratory, U.S. Army Corps of Engineers, Engineer Research and Development Center

Awards: G340683733, G4801813398, Collaborative Research: A Data-driven Closed-loop Framework for De Novo Generation of Molecules with Targeted Properties, G913618753

OpenAlex ID: W4390427478

FWCI: 2.213

Cites: 47

Cited by: 30

Related to: 10