Work

Machine learning assisted modeling of amorphous TiO 2 -doped GeO 2 for advanced LIGO mirror coatings

Year: 2025

Type: article

Source: Physical Review Materials

DOI: 10.1103/rbbq-qcb3

Authors: Jun Jiang, Rui Zhang, Kiran Prasai, Riccardo Bassiri, James N. Fry, Martin M. Fejer, Hai-Ping Cheng

Institutions: Northeastern University, University of Florida, Kennesaw State University, Stanford University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: closed

Funders: National Science Foundation, University of Florida Foundation, National Energy Research Scientific Computing Center

Awards: Collaborative Research: LSC Center for Coatings Research, Collaborative Research: Center for Coatings Research, Collaborative Research: Stanford-Florida program in Support of LIGO on Coatings and Core Optics, Collaborative Research: Center for Coatings Research, Collaborative Research: Stanford-Florida program in Support of LIGO on Coatings and Core Optics

OpenAlex ID: W4415739626

FWCI: 0.208

Cites: 63

Cited by: 1

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