Predictions of photophysical properties of phosphorescent platinum(II)
complexes based on ensemble machine learning approach
- URL: http://arxiv.org/abs/2301.05639v1
- Date: Sun, 8 Jan 2023 04:54:33 GMT
- Title: Predictions of photophysical properties of phosphorescent platinum(II)
complexes based on ensemble machine learning approach
- Authors: Shuai Wang (1), ChiYung Yam (2,3), Shuguang Chen (1,2), Lihong Hu (4),
Liping Li (2), Faan-Fung Hung (1,2), Jiaqi Fan (2), Chi-Ming Che (1,2), and
GuanHua Chen (1,2) ((1) Department of Chemistry, The University of Hong Kong,
Pokfulam, Hong Kong SAR, China (2) Hong Kong Quantum AI Lab Limited, Pak Shek
Kok, Hong Kong SAR, China (3) 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)
- Abstract summary: Phosphorescent metal complexes have been under intense investigations as emissive dopants for energy efficient organic light emitting diodes (OLEDs)
To render their practical applications as OLED emitters, it is in great need to develop Pt(II) complexes with high radiative decay rate constant ($k_r$) and photoluminescence (PL) quantum yield.
Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Phosphorescent metal complexes have been under intense investigations as
emissive dopants for energy efficient organic light emitting diodes (OLEDs).
Among them, cyclometalated Pt(II) complexes are widespread triplet emitters
with color-tunable emissions. To render their practical applications as OLED
emitters, it is in great need to develop Pt(II) complexes with high radiative
decay rate constant ($k_r$) and photoluminescence (PL) quantum yield. Thus, an
efficient and accurate prediction tool is highly desirable. Here, we develop a
general protocol for accurate predictions of emission wavelength, radiative
decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters
based on the combination of first-principles quantum mechanical method, machine
learning (ML) and experimental calibration. A new dataset concerning
phosphorescent Pt(II) emitters is constructed, with more than two hundred
samples collected from the literature. Features containing pertinent electronic
properties of the complexes are chosen. Our results demonstrate that ensemble
learning models combined with stacking-based approaches exhibit the best
performance, where the values of squared correlation coefficients ($R^2$), mean
absolute error (MAE), and root mean square error (RMSE) are 0.96, 7.21 nm and
13.00 nm for emission wavelength prediction, and 0.81, 0.11 and 0.15 for PL
quantum yield prediction. For radiative decay rate constant ($k_r$), the
obtained value of $R^2$ is 0.67 while MAE and RMSE are 0.21 and 0.25 (both in
log scale), respectively. The accuracy of the protocol is further confirmed
using 24 recently reported Pt(II) complexes, which demonstrates its reliability
for a broad palette of Pt(II) emitters.We expect this protocol will become a
valuable tool, accelerating the rational design of novel OLED materials with
desired properties.
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