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

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 diversity of the generated molecules. We demonstrate that latent vectors from the adaptive β-VAE serve as powerful inputs for downstream prediction models of experimental properties, such as fluorescence energy and quantum yield. Our optimized search framework for organic fluorescent materialsguided by gradients in latent space and validated by newly synthesized molecules sampled from optimal regions in the high-dimensional spaceshows strong potential for broader applications in the design of diverse organic materials.

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Publication Details

Journal
JACS Au
Published
2025-06-30
DOI
https://doi.org/10.1021/jacsau.5c00052
Citations
5
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
2.59
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article

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

Cheng‐Wei Ju, Jiangbo Wei, Yifeng Tang, John Z. H. Zhang et al.
5 citations
JACS Au
Machine Learning in Materials Science
2.59
article

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

Cheng‐Wei Ju, Jiangbo Wei, Yifeng Tang, John Z. H. Zhang, Jiankai Ge, Weikang Jiang, Yongrui Luo, Hanzhi Bai, Bo Li, Haozhe Zhang, Michael S. Jones, Ruiming Lin, Yuzhi Xu, Zhiqiang Wang, Zehan Mi, Xiao-Tian Li, Jinyu Zhang
article en
5 citations

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 diversity of the generated molecules. We demonstrate that latent vectors from the adaptive β-VAE serve as powerful inputs for downstream prediction models of experimental properties, such as fluorescence energy and quantum yield. Our optimized search framework for organic fluorescent materialsguided by gradients in latent space and validated by newly synthesized molecules sampled from optimal regions in the high-dimensional spaceshows strong potential for broader applications in the design of diverse organic materials.

JACS AuVol. 5(7)
University of Illinois Urbana-Champaign (US), National University of Singapore (SG), Shenzhen University (CN), Shanghai Jiao Tong University (CN), Chinese Academy of Sciences (CN), Nankai University (CN), New York University Shanghai (CN), University of Chicago (US), Quchan University of Advanced Technology (IR), Shanghai Institute of Organic Chemistry (CN), Institute of Organic Chemistry (HU), New York University (US), Florida Atlantic University (US)
Openalex Percentile: Top 10%
Machine Learning in Materials Science
2.59
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