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Research Article Open access CC BY 4.0

Contribution of the HF (Hartree-Fock) Method to Predicting the First Reduction Potential of TCNQ (Tetracyanoquinodimethane) Derivatives: A Comparative Study with the DFT (Density Functional Theory) Method

Fatogoma Diarrassouba, Kafoumba Bamba, Nahossé Ziao

Chemical Science International Journal · pp. 169–187 · Published 11 Sep 2026

10.9734/CSJI/2026/v35i51069

Abstract

This investigation examined whether the first reduction potential of forty (40) Tetracyanoquinodimethane (TCNQ) derivatives could be described using a single quantum-chemical descriptor. Molecular geometries were optimised and frequency calculations were undertaken at the HF/6-31G(d,p) level of theory. Electron affinity (EA) alone was used to construct the QSPR model. The resulting statistical and validation indicators were highly satisfactory: R2=0.9245; R2adjusted=0.9218; s=0.2063; F=342.7424; QLOO2=0.9161; =0.9161; Δrm2 (LOO)= 0,0000; Qext2=0.9348; =0.9026; Δrm2 (test)= 0.0110. Collectively, these values support the validity of the QSPR (Quantitative Structure-Property Relationship) model and its ability to estimate first reduction potentials within this molecular family. As observed for the DFT model reported in our previous work, the HF-based model may therefore be applied to future TCNQ derivatives that lie within its applicability domain at a 95% confidence level. Although the HF method omits electron correlation and is consequently often regarded as less predictive in quantum chemistry, the present results show that it can sometimes provide performance comparable with the DFT method. The preferred theoretical approach should ultimately be judged by agreement with experiment, because HF may reproduce experimental results better than DFT in some cases and the reverse may occur in others.

Tetracyanoquinodimethane (TCNQ) Hartree-Fock method Density Functional Theory (DFT) first reduction potential electron affinity quantitative structure-property relationship (QSPR) simple linear regression model validation applicability domain redox prediction

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