ArticleDigital discovery2025
Coupling causality and interpretable machine learning to reveal the reaction coordinate of C-N coupling with a supramolecular Cu-calix[8]arene catalyst.
Article in Digital discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Learning the reaction coordinate: collective variables from physical intuition to generative models.Digital discovery · 2026Review
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Authors and funding
5 authors.
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Abstract
Supramolecular 3d transition-metal catalysts are large, flexible systems with intricate interactions, resulting in complex reaction coordinates. To capture their dynamic nature, we developed a broadly applicable, high-throughput workflow, that leverages quantum mechanics/molecular mechanics molecular dynamics (QM/MM MD) in explicit solvent, to investigate a Cu(i)-calix[8]arene-catalysed C-N coupling reaction. The system complexity and high amount of data generated from sampling the reaction requires automated analyses. To identify and quantify the reaction coordinate from noisy simulation trajectories, we applied interpretable machine learning techniques (Lasso, Random Forest, Logistic Regression) in a consensus model, alongside dimensionality reduction methods (PCA, LDA, tICA). By employing a Granger Causality model, we move beyond the traditional view of a reaction coordinate, by defining it instead as a sequence of molecular motions leading up to the reaction.
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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.