A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A2B+B3+X6-Reference-Cited by-同舟云学术

A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A₂B⁺B³⁺X₆

Published:2023 Issue:37 Volume:11 Page:20193-20205
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Wei Xiao¹,Zhang Yunong¹,Liu Xi¹,Peng Junjie¹,Li Shengzhou²,Che Renchao³^ORCID,Zhang Huiran¹⁴^ORCID

Affiliation:

1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

2. Department of Computer Science, University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan

3. Laboratory of Advanced Materials, Shanghai Key Lab of Molecular Catalysis and Innovative Materials, Department of Materials Science, Fudan University, Shanghai 200438, China

4. Key Laboratory of Silicate Cultural Relics Conservation (Shanghai University), Ministry of Education, Shanghai University, Shanghai, 200444, China

Abstract

Material datasets are high-dimensional and high-noise, which makes most machine learning (ML) methods inefficient. We present a new framework which embeds material domain knowledge into the ML method. By doing so, we illustrate its role and improve the prediction accuracy of 540 perovskite materials.

Funder

National Key Research and Development Program of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/TA/D3TA03600F

Reference55 articles.

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2. Mapping the Room-Temperature Dynamic Stabilities of Inorganic Halide Double Perovskites

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4. First-principles study of electronic and optical properties of lead-free double perovskites Cs2NaBX6 (B = Sb, Bi; X = Cl, Br, I)

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