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Nevin Manimala Statistics

Comprehensive Modeling of Acetone Clusters: QTAIM Analysis and QCE Study

J Comput Chem. 2026 Apr 30;47(11):e70380. doi: 10.1002/jcc.70380.

ABSTRACT

In molecular research, comprehending the microscopic source of the macroscopic characteristics of polar aprotic solvents continues to be a significant difficulty. In order to bridge the gap between cluster-scale interactions and liquid acetone properties, we present a thorough quantum-chemical and statistical modeling of neutral acetone clusters in this work. The ABCluster algorithm was used to thoroughly explore the potential energy surface. High-level density functional theory calculations at the MN12SX-D3/def2-TZVP level were then performed, benchmarked against DLPNO-CCSD(T)/CBS reference energies. A thorough Quantum Theory of Atoms in Molecules (QTAIM) analysis of the nature and hierarchy of intermolecular interactions revealed a cooperative network dominated by dipole-dipole O⋯C and O⋯O interactions, supplemented by numerous weak C-H⋯O, H⋯C, and H⋯H dispersive contacts. The application of the QCE theory predicts a distribution dominated by trimers at low temperatures (T< 200 K), leading to a predominance of monomers above 260-280 K, reflecting the subtle equilibrium between electrostatic stabilization and entropic effects. The model reproduces experimental thermodynamic properties, such as the thermal capacity (Cp) between 200 and 375 K and infrared spectra at 300 K, with the calculated band of elongation C=O (1710 cm-1) being just 5 cm-1 from the experimental value (1715 cm-1). The thermodynamic properties and infrared spectrum of liquid acetone predicted by QCE show excellent agreement with experimental data, thus validating the integrated DFT-QTAIM-QCE approach. This work provides the first complete QCE characterization of pure liquid acetone, demonstrating that its macroscopic properties emerge from a dynamic equilibrium of small, weakly-bound clusters rather than extended hydrogen-bonded networks, and establishes a validated computational framework for predicting liquid-phase properties from ab initio cluster data.

PMID:42007531 | DOI:10.1002/jcc.70380

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