ArticleChemical science2018
Machine learning meets volcano plots: computational discovery of cross-coupling catalysts.
Article in Chemical science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
48 citing papers in PubMed.
- Boosting COJournal of computational chemistry · 2026Article
- Reaction-conditioned generative model for catalyst design and optimization with CatDRX.Communications chemistry · 2025Article
- Developing a Machine Learning Model for Hydrogen Bond Acceptance Based on Natural Bond Orbital Descriptors.The Journal of organic chemistry · 2025Article
- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
- A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes.JACS Au · 2025Article
- Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models.Journal of cheminformatics · 2025Article
- SPOCK Tool for Constructing Empirical Volcano Diagrams from Catalytic Data.ACS catalysis · 2025Article
- Article
- Using Classifiers To Predict Catalyst Design for Polyketone Microstructure.Journal of the American Chemical Society · 2025Article
- AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning.Journal of chemical information and modeling · 2024Article
- Discovery of molybdenum based nitrogen fixation catalysts with genetic algorithms.Chemical science · 2024Article
- Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions.ACS catalysis · 2024Article
- A bridge between trust and control: computational workflows meet automated battery cycling.Journal of materials chemistry. A · 2024Article
- Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability.JACS Au · 2024Article
- Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis.Beilstein journal of organic chemistry · 2024Review
- Machine Learning and Quantum Calculation for Predicting Yield in Cu-Catalyzed P-H Reactions.Molecules (Basel, Switzerland) · 2023Article
- Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions.Digital discovery · 2023Article
- Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au-water interfaces.Chemical science · 2023Article
- Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery.Chemical science · 2023Review
- Constructing and interpreting volcano plots and activity maps to navigate homogeneous catalyst landscapes.Nature protocols · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C-C cross-coupling reactions. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots. The versatility of this approach is illustrated for vast libraries of organometallic catalysts based on Pt, Pd, Ni, Cu, Ag, and Au combined with 91 ligands. Out-of-sample machine learning predictions were made on a total of 18 062 compounds leading to 557 catalyst candidates falling into the ideal thermodynamic window. This number was further refined by searching for candidates with an estimated price lower than 10 US$ per mmol. The 37 catalyst finalists are dominated by palladium phosphine ligand combinations but also include the earth abundant transition metal (Cu) with less common ligands. Our results indicate that modern statistical learning techniques can be applied to the computational discovery of readily available and promising catalyst candidates.
Identifiers
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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.