Work

Machine-Learning-Enabled Discovery of Coexisting Phases through Nanospectroscopy of a Wide-Bandgap Semiconductor

Year: 2025

Type: article

Source: Nano Letters

DOI: 10.1021/acs.nanolett.5c04478

Authors: Alyssa Bragg, Fengdeng Liu, Zhifei Yang, Donghwan Kim, Nitzan Hirshberg, Madison Garber, Brayden Lukaskawcez, Liam Thompson, Shane MacDonald, Hayden Binger, Devon Uram, Ashley Bucsek, Bharat Jalan, Alexander S. McLeod

Institutions: University of Minnesota, University of Michigan

Language: English

Topic: Electronic and Structural Properties of Oxides

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: closed

Funders: Office of the Under Secretary of Defense for Research and Engineering, Division of Materials Research, Division of Civil, Mechanical and Manufacturing Innovation

Awards: G403942265, G4058263891, G5876792733, G6405038140, G8085104557

OpenAlex ID: W4417497910

FWCI: 0.212

Cites: 36

Cited by: 1

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