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

Sheng-Hsuan HungZong-Rong YeChi-Feng ChengBerlin ChenMing-Kang Tsai
Published in: Journal of chemical theory and computation (2023)
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.
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