Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions
Dylan Anstine, Roman Zubatyuk, Liliana Gallegos, Robert Paton, Olaf Wiest, Benjamin Nebgen, Travis Jones, Gabe Gomes, Sergei Tretiak, Olexandr Isayev
Highlight
Finding efficient substrate-catalyst combinations for palladium-catalyzed cross-coupling reactions remains a critical challenge in synthetic chemistry, with broad implications for pharmaceutical and materials manufacturing.
Abstract
Finding efficient substrate-catalyst combinations for palladium-catalyzed cross-coupling reactions remains a critical challenge in synthetic chemistry, with broad implications for pharmaceutical and materials manufacturing. We report AIMNet2-Pd, a machine learned interatomic potential that enables rapid, accurate computational studies of palladium-catalyzed cross-coupling reactions. AIMNet2-Pd replaces computationally expensive electronic structure calculations with a neural network-based model that performs geometry optimization, transition state searches, and energy calculations in seconds while maintaining accuracy within 1-2 kcal mol⁻¹ and ~0.1 Å compared to the reference QM calculations. AIMNet2-Pd makes computational high-throughput catalyst screening and mechanistic studies of realistic systems feasible by providing on-demand thermodynamic and kinetic predictions for each step of a catalytic cycle. Importantly, the applicability of the systems extends beyond the monophosphine ligands in Pd(0)/Pd(II) cycles for which it has been trained on to chemically diverse Pd complexes. This demonstrates AIMNet2-Pd's utility to serve as a general-purpose and high-throughput tool for studying catalytic reactions.
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Cite This Paper
@article{Anstine2025a,
author = {Anstine, Dylan and Zubatyuk, Roman and Gallegos, Liliana and Paton, Robert and Wiest, Olaf and Nebgen, Benjamin and Jones, Travis and Gomes, Gabe and Tretiak, Sergei and Isayev, Olexandr},
title = {Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions},
year = {2025},
doi = {10.26434/chemrxiv-2025-n36r6},
url = {http://dx.doi.org/10.26434/chemrxiv-2025-n36r6},
publisher = {American Chemical Society (ACS)},
keywords = {neural network, machine learning, transition state, high-throughput},
researchAreas = {ml-potentials, reactions-reactivity, experiment-automation, materials-informatics},
highlight = {Finding efficient substrate-catalyst combinations for palladium-catalyzed cross-coupling reactions remains a critical challenge in synthetic chemistry, with broad implications for pharmaceutical and materials manufacturing.},
citations = {3}
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