Unravelling carbene moiety role in the N-heterocyclic carbene-phosphinidenes coordination chemistry: bonding and reactivity in gold chemistry

Guillaume Thiam; Leonardo Belpassi; Paola Belanzoni

Home/Archive/arXiv

arXiv · 2608.16472

Unravelling carbene moiety role in the N-heterocyclic carbene-phosphinidenes coordination chemistry: bonding and reactivity in gold chemistry

Guillaume Thiam·Leonardo Belpassi·Paola Belanzoni

View original source ↗Read source document ↗Export metadata ↓

Abstract

The coordination properties of the emerging class of N-heterocyclic carbene-phosphinidene (NHCP) ligands have been reported to be strongly affected by the carbene (NHC) moiety, but a clear understanding remains strikingly limited. In this work the bonding features and reactivity of 13 NHCP gold hydride complexes, [NHCPAuH], bearing different classes of NHCs have been systematically explored and compared to carbene analogues, using an unbiased computational protocol. The analyses reveal that, although the $π$-acceptor ability trend of NHCPs qualitatively parallels that of NHCs, the $σ$-donor ability trend reverses, with more $σ$-donating NHC moieties generating less $σ$-donating NHCP ligands. Concurrently, the nature of the NHC moiety impacts the coordination geometry at the P center, with the CNHC-P-Au bond angle appearing as a structural descriptor to monitor and/or design NHCP ligands with desired $σ$-donor properties. The NHCPs stronger $σ$-donor and weaker $π$-acceptor abilities compared to NHCs and their unique structural flexibility and electronic adaptability have been showcased to be directly controlled by the carbene moiety via modulation of the HOMO lone pair energy and its atomic phosphorous 3p character. The reactivity of [NHCPAuH] complexes with CO2, taken as a probe for potential NHCP applications in small molecule activation processes, demonstrates qualitatively similar mechanisms, with remarkably different activation barriers, which directly correlate with the NHCPs $σ$-donor ability, thus reflecting the high tunability of their bonding properties. This work provides insights and perspectives on the design principle of NHCP ligands, with a spotlight on the pivotal role of NHC moiety in modulating their electronic and steric properties, offering opportunities for burgeoning applications across diverse fields.

Explore related subjects

physics.chem-ph

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Guillaume Thiam, Leonardo Belpassi, Paola Belanzoni. 2026-08-17. Unravelling carbene moiety role in the N-heterocyclic carbene-phosphinidenes coordination chemistry: bonding and reactivity in gold chemistry. https://arxiv.org/abs/2608.16472

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Global approximations to the error function of real argument for vectorized computation

The error function of real argument can be approximated to a given uniform relative accuracy by a single closed-form expression for the whole variable range either in terms of addition, multiplication, division, and square root operations only, or also using the exponential function. The coefficients have been tabulated for up to 128-bit precision. Tests of a computer code implementation using the standard single- and double-precision floating-point arithmetic show good performance and vectorizability. Approximations to the complementary error function have also been found, including those where only additions, multiplications, and one division are needed to reach a uniform absolute accuracy.

physics.chem-ph↗

arXiv2026

From Heuristics to Machine Learning: The Performance Ceiling for Single-Ion Magnets and Its Electronic Origin

Machine learning (ML) is expected to speed up the discovery of single-ion magnets (SIMs), but does the structural information available before synthesis allow such predictions? For 1215 lanthanide complexes from the SIMDAVIS 1.2.1 database we compared three increasing levels of structural description: tabular features of the coordination site, continuous symmetry measures of the coordination polyhedron, and the complete 3D arrangement of atoms. All three converge to an accuracy near 76%, only slightly above the 71% of the single rule "predict SIM for Dy3+". To explain the failures, we combined multireference ab initio calculations with an inspection of the structures behind the high-confidence errors. The SIMs missed by the geometric models are field-induced relaxers whose ground Kramers doublets are prone to tunnelling, a property invisible to geometric descriptors. Many false positives contain several lanthanide centers or radicals, so their relaxation is collective and outside the single-ion picture. The electronic-structure and connectivity information needed to identify SIMs is therefore not accessible to geometric methods alone. Geometric models remain useful: restricting the screening to compounds with high prediction confidence raises the accuracy to 88% while retaining 48% of the dataset. Building on the analysis of the failures, we propose a strategy that combines simple filters for nuclearity and for radicals with ligand-field descriptors from ab initio calculations.

physics.chem-ph↗

arXiv2026

Composition-Dependent Self-Diffusion Coefficients in Liquid Mixtures from Hybrid Machine Learning

Self-diffusion coefficients are key descriptors of molecular mobility, yet experimental data remain scarce, highlighting the need for reliable prediction methods. In previous work, we introduced the hybrid Enhanced Stokes-Einstein (ESE) model, which advanced the state of the art in the physically consistent prediction of self-diffusion coefficients of solutes at infinite dilution in pure solvents by integrating the Stokes-Einstein equation with machine learning (ML). Here, we extend this approach to concentration-dependent self-diffusion coefficients and multicomponent solvents with HADES. This hybrid architecture leverages a deep-set neural network to connect pure-component and mixture prediction within a single framework. HADES predicts self-diffusion coefficients in liquid mixtures with any number of components at any composition and temperature. The only required inputs are SMILES-encoded molecular structures of the components and the pure-component viscosities, making the method broadly applicable. Trained and evaluated on a comprehensive dataset of 2526 data points for 600 systems, HADES significantly outperforms benchmark prediction methods. The trained model and its source code are fully disclosed, and the application is available via an interactive website https://ml-prop.mv.rptu.de/.

physics.chem-ph↗