ArticleChemical science2023
Generative organic electronic molecular design informed by quantum chemistry.
Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Fast and Flexible 3D Molecular Design Framework for Novel Organic Optoelectronic Materials.JACS Au · 2026Article
- Quantum Chemistry-Driven Molecular Inverse Design of Stable Isomers with Data-Free Reinforcement Learning.Journal of chemical theory and computation · 2026Article
- Controlling the fate of two triplet states: solid-state annihilator design for photon upconversion.Chemical science · 2026Review
- Generative design of singlet fission materials leveraging a fragment-oriented database.Chemical science · 2025Article
- Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning.Annual review of materials research · 2025Article
- Inverse Design of Singlet-Fission Materials with Uncertainty-Controlled Genetic Optimization.Angewandte Chemie (International ed. in English) · 2025Article
- Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning.JACS Au · 2024Article
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2 authors.
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Abstract
Generative molecular design strategies have emerged as promising alternatives to trial-and-error approaches for exploring and optimizing within large chemical spaces. To date, generative models with reinforcement learning approaches have frequently used low-cost methods to evaluate the quality of the generated molecules, enabling many loops through the generative model. However, for functional molecular materials tasks, such low-cost methods are either not available or would require the generation of large amounts of training data to train surrogate machine learning models. In this work, we develop a framework that connects the REINVENT reinforcement learning framework with excited state quantum chemistry calculations to discover molecules with specified molecular excited state energy levels, specifically molecules with excited state landscapes that would serve as promising singlet fission or triplet-triplet annihilation materials. We employ a two-step curriculum strategy to first find a set of diverse promising molecules, then demonstrate the framework's ability to exploit a more focused chemical space with anthracene derivatives. Under this protocol, we show that the framework can find desired molecules and improve Pareto fronts for targeted properties
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Registered trials
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