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.
Keywords
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}
} Copied to clipboard!
Related Research Areas
Related Publications
Discovery of Novel Celecoxib Polymorphs Using AIMNet2 Machine Learning Interatomic Potential
Polymorphism plays a pivotal role in defining the solid-state properties of pharmaceutical compounds, yet the discovery and accurate energy ranking of polymorphs remain a challenge.
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
Nature Chemistry 16 , 727–734
Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery.
Teaching a neural network to attach and detach electrons from molecules
Nature Communications 12
Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations.
AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry
Angewandte Chemie International Edition
Abstract Open‐shell systems such as radical intermediates are central to radical polymerization (RP), combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges.
AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry
Angewandte Chemie
Abstract Open‐shell systems such as radical intermediates are central to radical polymerization (RP), combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges.