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Updated: Feb 17, 2026

Isolating Free Carbenes, their Mixed Dimers and Organic Radicals
Published on: April 19, 2019
Accurate Equilibrium Structures of Organic Radicals Made Accessible with Local-Correlation-Enhanced Pisa Composite
Luigi Crisci1, Federico Lazzari1, Vincenzo Barone2
1Scuola Superiore Meridionale, Largo San Marcellino 10, 80138 Napoli, Italy.
Abstract:
The number of accurate equilibrium structures of organic radicals is still limited, despite the central role of these species in atmospheric and combustion chemistry, spectroscopy, and catalysis. Because equilibrium structures are experimentally inaccessible for all but the smallest radicals, reliable and predictive quantum-chemical protocols are essential. While double-hybrid density functionals can achieve remarkable accuracy for closed-shell molecules, their performance often deteriorates for delocalized open-shell systems, and they may require spin- and multiplicity-dependent bond-length corrections that compromise the smoothness of potential energy surfaces. Here, we show that a local correlation treatment based on pair natural orbitals (PNOs) provides a robust and practical solution to this long-standing problem. In particular, the PNO-LCCSD(F12b)(T*) approach, embedded in an efficient composite framework, enables near-spectroscopic equilibrium geometries for both closed- and open-shell molecular systems containing up to a few dozen atoms. To make this strategy broadly accessible, we extend a previously introduced external utility to PNO-based correlated methods, enabling the automated assembly of composite gradients. Starting from DFT geometries and Hessians, local correlation is combined with hierarchical optimization and an efficient driver in generalized internal coordinates, yielding the PPCS2 protocol. Benchmark tests on a diverse set of σ-, π-, aromatic, and heteroatom-centered radicals demonstrate uniform accuracy across the full data set and deliver high-precision equilibrium structures even for reactive radicals that remain challenging to characterize experimentally.
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