Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts

Baicheng Weng; Zhilong Song; Rilong Zhu; Qingyu Yan; Qingde Sun; Corey G. Grice; Yanfa Yan; Wan Jian Yin

doi:10.1038/s41467-020-17263-9

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts

Baicheng Weng, Zhilong Song, Rilong Zhu, Qingyu Yan, Qingde Sun, Corey G. Grice, Yanfa Yan^*, Wan Jian Yin^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

270 Citations (Scopus)

Abstract

Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs_0.4La_0.6Mn_0.25Co_0.75O₃, Cs_0.3La_0.7NiO₃, SrNi_0.75Co_0.25O₃, and Sr_0.25Ba_0.75NiO₃, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.

Original language	English
Article number	3513
Journal	Nature Communications
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41467-020-17263-9
Publication status	Published - Dec 1 2020
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).

ASJC Scopus Subject Areas

General Chemistry
General Biochemistry,Genetics and Molecular Biology
General Physics and Astronomy

Access to Document

10.1038/s41467-020-17263-9

Cite this

@article{58ce1dd1c99247efb8895c868b4db818,

title = "Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts",

abstract = "Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and Sr0.25Ba0.75NiO3, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.",

author = "Baicheng Weng and Zhilong Song and Rilong Zhu and Qingyu Yan and Qingde Sun and Grice, {Corey G.} and Yanfa Yan and Yin, {Wan Jian}",

note = "Publisher Copyright: {\textcopyright} 2020, The Author(s).",

year = "2020",

month = dec,

day = "1",

doi = "10.1038/s41467-020-17263-9",

language = "English",

volume = "11",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

number = "1",

}

TY - JOUR

T1 - Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts

AU - Weng, Baicheng

AU - Song, Zhilong

AU - Zhu, Rilong

AU - Yan, Qingyu

AU - Sun, Qingde

AU - Grice, Corey G.

AU - Yan, Yanfa

AU - Yin, Wan Jian

PY - 2020/12/1

Y1 - 2020/12/1

N2 - Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and Sr0.25Ba0.75NiO3, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.

AB - Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and Sr0.25Ba0.75NiO3, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.

UR - http://www.scopus.com/inward/record.url?scp=85087970291&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85087970291&partnerID=8YFLogxK

U2 - 10.1038/s41467-020-17263-9

DO - 10.1038/s41467-020-17263-9

M3 - Article

C2 - 32665539

AN - SCOPUS:85087970291

SN - 2041-1723

VL - 11

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 3513

ER -

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts

Abstract

Bibliographical note

ASJC Scopus Subject Areas

Access to Document

Other files and links

Fingerprint

Cite this