Journal of Materials Chemistry C

Multi-level Δ-learning for Predicting Radiative Decay Rate Constant of Phosphorescent Platinum(II) Complexes

Shuai Wang ¹

ChiYung Yam ^{2, 3}

Lihong Hu ⁴

Faan-Fung Hung ^{1, 2}

shuguang Chen ^{1, 2}

Chi Ming Che ^{1, 2}

GUANHUA CHEN ^{1, 2}

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Department of Chemistry, The University of Hong Kong, Pokfulam, Hong Kong SAR, China |

Hong Kong Quantum AI Lab Limited, Pak Shek Kok, Hong Kong SAR, China |

Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, 518000, China |

⁴

School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China |

Publication type: Journal Article

Publication date: 2025-01-15

Royal Society of Chemistry (RSC)

Journal: Journal of Materials Chemistry C

scimago Q1

SJR: 1.358

CiteScore: 10.8

Impact factor: 5.7

ISSN: 20507526, 20507534

DOI: 10.1039/D4TC04046E

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Abstract

The multi-level Δ-learning method significantly advances k_r prediction by bridging TDDFT results with experimental values. This approach improves prediction reliability, making it essential for accurate material evaluation.

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Journal of Materials Chemistry C	Journal of Materials Chemistry C, 1, 100% Journal of Materials Chemistry C 1 publication, 100%
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Royal Society of Chemistry (RSC)	Royal Society of Chemistry (RSC), 1, 100% Royal Society of Chemistry (RSC) 1 publication, 100%
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Publisher

Royal Society of Chemistry (RSC)

Journal

Journal of Materials Chemistry C

scimago Q1

SJR

1.358

CiteScore

10.8

Impact factor

5.7

ISSN

20507526 (Print)

20507534 (Electronic)

Profiles

Che, Chi-Ming M

Multi-level Δ-learning for Predicting Radiative Decay Rate Constant of Phosphorescent Platinum(II) Complexes

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