ArticleNature chemistry2024
Sequential closed-loop Bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery.
Article in Nature chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Implementing modular design principles for molecular homogeneous catalyst discovery.Catalysis science & technology · 2026Review
- An autonomous lab for data-driven homogeneous catalysis.Nature communications · 2026Article
- Bayesian Optimization of Catalysis with In-Context Learning.ACS central science · 2026Article
- Efficient preparation of size-controlled sodium alginate microspheresRSC advances · 2026Article
- Highly flexoelectric response for uranium removal and recovery using the organic-inorganic hybrid material.Nature communications · 2026Article
- Fingerprinting organic molecules for the inverse design of two-dimensional hybrid perovskites with target energetics.Science advances · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Sequential Optimization of Multivariate Metal-Organic Framework Based Biocatalysis.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- 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
- Data-Driven Discovery of Polar Organic Cocrystals: Integration of Machine Learning and Automated Screening.Journal of the American Chemical Society · 2025Article
- Discovery of highly fluorescent covalent organic frameworks through AI-assisted iterative experiment-learning cycles.Nature chemistry · 2025Article
- Considerations in Pursuing Reaction Scope Generality.Angewandte Chemie (International ed. in English) · 2025Review
- Transfer learning from custom-tailored virtual molecular databases to real-world organic photosensitizers for catalytic activity prediction.Communications chemistry · 2025Article
- Machine learning-assisted Ru-N bond regulation for ammonia synthesis.Nature communications · 2025Article
- Bayesian Meta-Learning for Few-Shot Reaction Outcome Prediction of Asymmetric Hydrogenation of Olefins.Angewandte Chemie (International ed. in English) · 2025Article
- Machine-Learning-Guided Design of Nanostructured Metal Oxide Photoanodes for Photoelectrochemical Water Splitting: From Material Discovery to Performance Optimization.Nanomaterials (Basel, Switzerland) · 2025Review
- Bayesian molecular optimization for accelerating reverse intersystem crossing.Chemical science · 2025Article
- Discovery of Crystalline Inorganic Solids in the Digital Age.Accounts of chemical research · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conjugated organic photoredox catalysts (OPCs) can promote a wide range of chemical transformations. It is challenging to predict the catalytic activities of OPCs from first principles, either by expert knowledge or by using a priori calculations, as catalyst activity depends on a complex range of interrelated properties. Organic photocatalysts and other catalyst systems have often been discovered by a mixture of design and trial and error. Here we report a two-step data-driven approach to the targeted synthesis of OPCs and the subsequent reaction optimization for metallophotocatalysis, demonstrated for decarboxylative sp
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Registered trials
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