Published June 30, 2025
| Version v1
Journal article
Exploring Optimized Organic Fluorophore Search through Experimental Data-Driven Adaptive β-VAE
Creators
- 1. New York University
- 2. Chinese Academy of Sciences
- 3. QuanMol Tech, Inc.
- 4. Nankai University
- 5. National University of Singapore
- 6. Shanghai Jiao Tong University
- 7. Department of Florida Atlantic University Engineering and Computer Science
- 8. University of Illinois at Urbana−Champaign
- 9. University of Chicago
- 10. Shenzhen University of Advanced Technology
Description
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.
Additional details
Identifiers
- DOI
- 10.1021/jacsau.5c00052
- Other
- oai:uchicago.tind.io:16214