Work

Physics-driven discovery and bandgap engineering of hybrid perovskites

Year: 2024

Type: article

Abstract: Discovery of physical models of binary compositions using structured Gaussian Process (sGP) with physics-informed mean functions, optimizing materials post-discovery to enhance design and application efficiency.

Source: Digital Discovery

DOI: 10.1039/d4dd00080c

Authors: Sheryl L. Sanchez, Elham Foadian, Maxim Ziatdinov, Jonghee Yang, Sergei V. Kalinin, Yongtao Liu, Mahshid Ahmadi

Institutions: University of Tennessee at Knoxville, Oak Ridge National Laboratory, Center for Nanophase Materials Sciences, Yonsei University

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, Tennessee Higher Education Commission, Office of Science, Oak Ridge National Laboratory

Award: CAREER: Guiding automated synthesis of hybrid perovskites towards stability via knowledge of nanoscale ionic mechanisms

OpenAlex ID: W4400426506

FWCI: 1.137

Cites: 80

Cited by: 12

Related to: 10