Integrating machine learning and generative models for the intelligent design of TADF materials with circularly polarized luminescence

Abstract

Unlocking the potential of circularly polarized thermally activated delayed fluorescence (CP-TADF) molecules for advanced optoelectronic applications necessitates an accurate understanding of the luminescent dissymmetry factor (g_lum). To develop a robust predictive framework, we first conducted a comprehensive statistical analysis of reported experimental |g_lum| values and their theoretical predictions, which revealed significant discrepancies between experimental and calculated results. Based on these findings, we employ machine learning (ML) models with Morgan fingerprints to predict |g_lum|, complemented by Klekota-Roth fingerprints and Shapley additive explanations for detailed structural insights. Using this predictive model, we perform inverse design of CP-TADF materials through a generative model based on a modified variational autoencoder, with |g_lum| as the objective function. This integrative approach successfully identifies CP-TADF molecules with both high |g_lum| values and favorable synthetic accessibility. Our framework serves as a powerful tool for the intelligent design of CP-TADF materials, bridging theoretical predictions with experimental realization and accelerating the discovery of next-generation CP-TADF materials.