Optimizing Computational Parameters for Nuclear Electronic Orbital Density Functional Theory: A Benchmark Study on Proton Affinities

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Raza Ullah Khan, Ralf Tonner-Zech
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引用次数: 0

Abstract

This study benchmarks the nuclear electronic orbital density functional theory (NEO-DFT) method for a set of molecules that is larger than in previous studies. The focus is on proton affinity predictions to assess the influences of computational parameters. NEO-DFT incorporates nuclear quantum effects for protons involved in protonation processes. Using a test set of 72 molecules with experimental proton affinities as reference, we evaluated various exchange-correlation functionals, finding that B3LYP-based functionals deliver the most accurate results. Among the tested functionals, CAM-B3LYP performs the best with an MAD value of 6.2 kJ/mol with respect to experimental data. In NEO-DFT, electron-proton correlation (epc) functionals were assessed, with LDA-type epc17-2 yielding comparable results to the GGA-type epc19 functional. Compared to traditional DFT (MAD value of 31.6 kJ/mol), which treats nuclei classically, NEO-DFT provides enhanced accuracy for proton affinities when electron-proton correlation is included. Regarding basis sets, the def2-QZVP electronic basis set achieved the highest accuracy with an MAD value of 5.0 kJ/mol, though at a higher computational cost compared to def2-TZVP and def2-SVP, while nuclear basis sets showed minimal impact on proton affinity accuracy and no consistent trend. Overall, this study demonstrates NEO-DFT's efficacy in addressing nuclear quantum effects for proton affinity predictions, providing guidance on optimal parameter selection for future NEO-DFT applications.

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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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