Data driven high quantum yield halide perovskite phosphors design and fabrication

Haoxin Mai; Xiaoming Wen; Xuying Li; Nethmi S.L. Dissanayake; Xueqian Sun; Yuerui Lu; Tu C. Le; Salvy P. Russo; Dehong Chen; David A. Winkler; Rachel A. Caruso

doi:10.1016/j.mattod.2024.02.002

Data driven high quantum yield halide perovskite phosphors design and fabrication

Haoxin Mai, Xiaoming Wen, Xuying Li, Nethmi S.L. Dissanayake, Xueqian Sun, Yuerui Lu, Tu C. Le, Salvy P. Russo, Dehong Chen, David A. Winkler, Rachel A. Caruso

Medicinal Chemistry

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

18 Citations (Scopus)

Abstract

The outstanding emission of halide perovskites make them ideal candidates for white emission light-emitting diodes (LEDs) for lighting applications. However, many perovskites contain toxic or scarce elements and have unsatisfactory stability. Here, we report a target-driven approach, based on active learning (AL) techniques, to discover halide perovskites suitable for commercial LED applications. Based on the similarity between halide and oxide perovskites, a model trained on an oxide perovskite dataset plus six AL-selected halide perovskites exhibited excellent performance for photoluminescence quantum yield (PLQY) predictions of oxide and halide perovskites. The model proposed a strong relationship between ionic radii and PLQY, postulated to be due to the self-trap excitons derived from the Jahn-Teller deformation. A novel halide perovskite phosphor, Cs₄Zn(Bi_0.85Sb_0.15)₂Cl₁₂:0.01Mn, was designed and synthesized with the aid of the model. It exhibited an 88 % PLQY and outstanding thermal and luminescent stability. A simple white LED was fabricated from this material, exemplifying its commercial potential. This study demonstrates how machine learning techniques can accelerate discovery of next-generation phosphors for high performance single emitter-based white-light emitting devices.

Original language	English
Pages (from-to)	12-21
Number of pages	10
Journal	Materials Today
Volume	74
DOIs	https://doi.org/10.1016/j.mattod.2024.02.002
Publication status	Published - May 2024

Keywords

Active learning
Halide perovskite
LED
Machine learning
Photoluminescence

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10.1016/j.mattod.2024.02.002Licence: CC BY

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@article{522d7efb28ed4118827b672152ebd628,

title = "Data driven high quantum yield halide perovskite phosphors design and fabrication",

abstract = "The outstanding emission of halide perovskites make them ideal candidates for white emission light-emitting diodes (LEDs) for lighting applications. However, many perovskites contain toxic or scarce elements and have unsatisfactory stability. Here, we report a target-driven approach, based on active learning (AL) techniques, to discover halide perovskites suitable for commercial LED applications. Based on the similarity between halide and oxide perovskites, a model trained on an oxide perovskite dataset plus six AL-selected halide perovskites exhibited excellent performance for photoluminescence quantum yield (PLQY) predictions of oxide and halide perovskites. The model proposed a strong relationship between ionic radii and PLQY, postulated to be due to the self-trap excitons derived from the Jahn-Teller deformation. A novel halide perovskite phosphor, Cs4Zn(Bi0.85Sb0.15)2Cl12:0.01Mn, was designed and synthesized with the aid of the model. It exhibited an 88 \% PLQY and outstanding thermal and luminescent stability. A simple white LED was fabricated from this material, exemplifying its commercial potential. This study demonstrates how machine learning techniques can accelerate discovery of next-generation phosphors for high performance single emitter-based white-light emitting devices.",

keywords = "Active learning, Halide perovskite, LED, Machine learning, Photoluminescence",

author = "Haoxin Mai and Xiaoming Wen and Xuying Li and Dissanayake, \{Nethmi S.L.\} and Xueqian Sun and Yuerui Lu and Le, \{Tu C.\} and Russo, \{Salvy P.\} and Dehong Chen and Winkler, \{David A.\} and Caruso, \{Rachel A.\}",

note = "Funding Information: This work is supported by an Australian Research Council (ARC) Discovery Project Grant ( DP220100945 ). We acknowledge access to SEM and XPS through the RMIT Microscopy and Microanalysis Facility and support from the technical officers at RMIT University. S. P. R. acknowledges the support of the ARC under the Centre of Excellence scheme (project number CE170100026 ). This work was also supported by computational resources provided by the National Computational Infrastructure Supercomputer Facility (NCI-NF). Publisher Copyright: {\textcopyright} 2024 The Author(s)",

year = "2024",

month = may,

doi = "10.1016/j.mattod.2024.02.002",

language = "English",

volume = "74",

pages = "12--21",

journal = "Materials Today",

issn = "1369-7021",

publisher = "Elsevier",

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TY - JOUR

T1 - Data driven high quantum yield halide perovskite phosphors design and fabrication

AU - Mai, Haoxin

AU - Wen, Xiaoming

AU - Li, Xuying

AU - Dissanayake, Nethmi S.L.

AU - Sun, Xueqian

AU - Lu, Yuerui

AU - Le, Tu C.

AU - Russo, Salvy P.

AU - Chen, Dehong

AU - Winkler, David A.

AU - Caruso, Rachel A.

N1 - Funding Information: This work is supported by an Australian Research Council (ARC) Discovery Project Grant ( DP220100945 ). We acknowledge access to SEM and XPS through the RMIT Microscopy and Microanalysis Facility and support from the technical officers at RMIT University. S. P. R. acknowledges the support of the ARC under the Centre of Excellence scheme (project number CE170100026 ). This work was also supported by computational resources provided by the National Computational Infrastructure Supercomputer Facility (NCI-NF). Publisher Copyright: © 2024 The Author(s)

PY - 2024/5

Y1 - 2024/5

N2 - The outstanding emission of halide perovskites make them ideal candidates for white emission light-emitting diodes (LEDs) for lighting applications. However, many perovskites contain toxic or scarce elements and have unsatisfactory stability. Here, we report a target-driven approach, based on active learning (AL) techniques, to discover halide perovskites suitable for commercial LED applications. Based on the similarity between halide and oxide perovskites, a model trained on an oxide perovskite dataset plus six AL-selected halide perovskites exhibited excellent performance for photoluminescence quantum yield (PLQY) predictions of oxide and halide perovskites. The model proposed a strong relationship between ionic radii and PLQY, postulated to be due to the self-trap excitons derived from the Jahn-Teller deformation. A novel halide perovskite phosphor, Cs4Zn(Bi0.85Sb0.15)2Cl12:0.01Mn, was designed and synthesized with the aid of the model. It exhibited an 88 % PLQY and outstanding thermal and luminescent stability. A simple white LED was fabricated from this material, exemplifying its commercial potential. This study demonstrates how machine learning techniques can accelerate discovery of next-generation phosphors for high performance single emitter-based white-light emitting devices.

AB - The outstanding emission of halide perovskites make them ideal candidates for white emission light-emitting diodes (LEDs) for lighting applications. However, many perovskites contain toxic or scarce elements and have unsatisfactory stability. Here, we report a target-driven approach, based on active learning (AL) techniques, to discover halide perovskites suitable for commercial LED applications. Based on the similarity between halide and oxide perovskites, a model trained on an oxide perovskite dataset plus six AL-selected halide perovskites exhibited excellent performance for photoluminescence quantum yield (PLQY) predictions of oxide and halide perovskites. The model proposed a strong relationship between ionic radii and PLQY, postulated to be due to the self-trap excitons derived from the Jahn-Teller deformation. A novel halide perovskite phosphor, Cs4Zn(Bi0.85Sb0.15)2Cl12:0.01Mn, was designed and synthesized with the aid of the model. It exhibited an 88 % PLQY and outstanding thermal and luminescent stability. A simple white LED was fabricated from this material, exemplifying its commercial potential. This study demonstrates how machine learning techniques can accelerate discovery of next-generation phosphors for high performance single emitter-based white-light emitting devices.

KW - Active learning

KW - Halide perovskite

KW - LED

KW - Machine learning

KW - Photoluminescence

UR - http://www.scopus.com/inward/record.url?scp=85186090939&partnerID=8YFLogxK

U2 - 10.1016/j.mattod.2024.02.002

DO - 10.1016/j.mattod.2024.02.002

M3 - Article

AN - SCOPUS:85186090939

SN - 1369-7021

VL - 74

SP - 12

EP - 21

JO - Materials Today

JF - Materials Today

ER -

Data driven high quantum yield halide perovskite phosphors design and fabrication

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Keywords

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ARC Centre of Excellence in Exciton Science

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Data driven high quantum yield halide perovskite phosphors design and fabrication

Abstract

Keywords

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ARC Centre of Excellence in Exciton Science

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