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
Abstract
An assessment of modifying the SchNET model for the predictions of experimental molecular photophysical properties, including absorption energy (Δ E abs ), emission energy (Δ E emi ), 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 Δ E abs and Δ E emi with errors less than 0.1 eV. The corresponding predictions of PLQY were shown to be comparable to the previous graph convolution network model.