ArticleChemical science2024
Artificial design of organic emitters
Article in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- AI-driven drug design: a comprehensive review.Journal of computer-aided molecular design · 2026Review
- Inverse Design of Metal-Organic Polyhedra through Molecular Fragmentation and Evolutionary Optimisation.Journal of chemical information and modeling · 2026Article
- Discovery of tunable and soluble organic emitters for solid-state lasers with a self-driving laboratory.Nature communications · 2026Article
- Inverse Molecular Design for the Discovery of Organic Energy Transfer Photocatalysts: Bridging Global and Local Chemical Space Exploration.Journal of the American Chemical Society · 2026Article
- Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional Materials.Journal of chemical theory and computation · 2026Article
- Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors.Nature biotechnology · 2025Article
- Donor-Acceptor-Substituted 5-Azaazulenes.The Journal of organic chemistry · 2025Article
- Toward Circularly Polarized Luminescence from Inherently Chiral Inverted Singlet-Triplet Chromophores.ACS materials letters · 2025Article
- Leveraging the Bias-Variance Tradeoff in Quantum Chemistry for Accurate Negative Singlet-Triplet Gap Predictions: A Case for Double-Hybrid DFT.Journal of computational chemistry · 2025Article
- Parametrization of κACS catalysis · 2025Article
- Singlet-Triplet Inversions in Through-Bond Charge-Transfer States.The journal of physical chemistry letters · 2024Article
- Ultrafast Computational Screening of Molecules with Inverted Singlet-Triplet Energy Gaps Using the Pariser-Parr-Pople Semiempirical Quantum Chemistry Method.The journal of physical chemistry. A · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The design of molecules requires multi-objective optimizations in high-dimensional chemical space with often conflicting target properties. To navigate this space, classical workflows rely on the domain knowledge and creativity of human experts, which can be the bottleneck in high-throughput approaches. Herein, we present an artificial molecular design workflow relying on a genetic algorithm and a deep neural network to find a new family of organic emitters with inverted singlet-triplet gaps and appreciable fluorescence rates. We combine high-throughput virtual screening and inverse design infused with domain knowledge and artificial intelligence to accelerate molecular generation significantly. This enabled us to explore more than 800 000 potential emitter molecules and find more than 10 000 candidates estimated to have inverted singlet-triplet gaps (INVEST) and appreciable fluorescence rates, many of which likely emit blue light. This class of molecules has the potential to realize a new generation of organic light-emitting diodes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.