Enhanced Predictions for the Experimental Photophysical Data Using the Featurized Schnet-Bondstep Approach

Sheng Hsuan Hung, Zong Rong Ye, Chi Feng Cheng, Berlin Chen*, Ming Kang Tsai*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

An assessment of modifying the SchNET model for the predictions of experimental molecular photophysical properties, including absorption energy (ΔEabs), emission energy (ΔEemi), and photoluminescence quantum yield (PLQY), was reported. The solution environment was properly introduced outside the interaction layers of SchNET for not overly amplifying the solute-solvent interactions, particularly being supported by the changes of prediction errors between the presence and absence of the solvent effect. Two featurization schemes under the framework of the Schnet-bondstep approach, with featuring the concepts of reduced-atomic-number and reduced-atomic-neighbor, were demonstrated. These featurized models can consequently provide fine predictions for ΔEabs and ΔEemi with errors less than 0.1 eV. The corresponding predictions of PLQY were shown to be comparable to the previous graph convolution network model.

原文英語
頁(從 - 到)4559-4567
頁數9
期刊Journal of Chemical Theory and Computation
19
發行號14
DOIs
出版狀態已發佈 - 2023 7月 25

ASJC Scopus subject areas

  • 電腦科學應用
  • 物理與理論化學

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