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Discovering novel molecules with targeted properties remains a formidable challenge in materials science, often likened to finding a needle in a haystack. Traditional experimental approaches are slow, costly, and inefficient. In this study, we present an inverse design framework based on a molecular graph conditional variational autoencoder (CVAE) that enables the generation of new molecules with user-specified optical properties, particularly molar extinction coefficient ($$\varepsilon$$). Our model encodes molecular graphs, derived from SMILES strings, into a structured latent space, and then decodes them into valid molecular structures conditioned on a target $$\varepsilon$$ value. Trained on a curated dataset of known molecules with corresponding extinction coefficients, the CVAE learns to generate chemically valid structures, as verified by RDKit. Subsequent Density Functional Theory (DFT) simulations confirm that many of the generated molecules exhibit the electronic structures similar to those molecules with desired $$\varepsilon$$ values. We have also verified the $$\varepsilon$$ values of the generated molecules using a graph neural network (GNN) and the synthesizability of those molecules using an open-source module named ASKCOS. This approach demonstrates the potential of CVAEs to accelerate molecular discovery by enabling user-guided, property-driven molecule generation -- offering a scalable, data-driven alternative to traditional trial-and-error synthesis.
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This content will become publicly available on June 23, 2026
Potency of Latent Spaces in Inverse Quantum Dye Design
The discovery of functional dye materials with superior optical properties is crucial for advancing technologies in biomedical imaging, organic photovoltaics, and quantum information systems. Recent advancements highlight the need to accelerate this discovery process by integrating computational strategies with experimental methods. In this regard, we have employed a computational approach to explore the latent space of dye materials, utilizing swarm optimization techniques to efficiently navigate complex chemical spaces and identify optimal values of molecular properties using machine learning methods based on target properties, such as high extinction coefficients ($$\varepsilon$$). The latent space based evaluation outperformed all available features of a domain. This approach enhances inverse material design by systematically correlating molecular parameters with desired optical characteristics by implementing VAEs. In this process, by defining target properties as inputs, the model effectively determines the key molecular features necessary for engineering high-performance dye compounds.
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- PAR ID:
- 10631942
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400714627
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Location:
- Columbus USA
- Sponsoring Org:
- National Science Foundation
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