Exploring Optimized Organic Fluorophore Search through Experimental Data-Driven Adaptive β‑VAE.

ABSTRACT: Designing organic fluorescent molecules with tailored optical properties has been a long-standing challenge. Recently, statistical models have opened new avenues for tackling this problem. Inverse design has attracted considerable attention in organic materials science; however, most existing approaches focus on arbitrary design or theoretical properties. Here, we introduce a strategy that enables the direct optimization of specific experimental properties during the inverse design process. Our method employs an adaptive β-variational autoencoder (adaptive β-VAE) combined with a latent vector-based prediction model. By dynamically tuning the Kullback-Leibler divergence scaling factor (β) and employing a separate training strategy, we enhance both the robustness of the generator and the div

SUBMITTER: Xu Y 

PROVIDER: S-EPMC12308382 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature