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PDDG/PM3 and PDDG/MNDO: Improved semiempirical methods
Abstract
Two new semiempirical methods employing a Pairwise Distance Directed Gaussian modification have been developed: PDDG/PM3 and PDDG/MNDO; they are easily implemented in existing software, and yield heats of formation for compounds containing C, H, N, and O atoms with significantly improved accuracy over the standard NDDO schemes, PM5, PM3, AM1, and MNDO. The PDDG/PM3 results for heats of formation also show substantial improvement over density functional theory with large basis sets. The PDDG modifications consist of a single function, which is added to the existing pairwise core repulsion functions within PM3 and MNDO, a reparameterized semiempirical parameter set, and modified computation of the energy of formation of a gaseous atom. The PDDG addition introduces functional group information via pairwise atomic interactions using only atom‐based parameters. For 622 diverse molecules containing C, H, N, and O atoms, mean absolute errors in calculated heats of formation are reduced from 4.4 to 3.2 kcal/mol and from 8.4 to 5.2 kcal/mol using the PDDG modified versions of PM3 and MNDO over the standard versions, respectively. Several specific problems are overcome, including the relative stability of hydrocarbon isomers, and energetics of small rings and molecules containing multiple heteroatoms. The internal consistency of PDDG energies is also significantly improved, enabling more reliable analysis of isomerization energies and trends across series of molecules; PDDG isomerization energies show significant improvement over B3LYP/6‐31G* results. Comparison of heats of formation, ionization potentials, dipole moments, isomer, and conformer energetics, intermolecular interaction energies, activation energies, and molecular geometries from the PDDG techniques is made to experimental data and values from other semiempirical and ab initio methods. © 2002 Wiley Periodicals, Inc. J Comput Chem 23: 1601–1622, 2002
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Cited by
Riley, K. E., Pitoňák, M., Jurečka, P., & Hobza, P. (2010) · Chemical Reviews · doi:10.1021/cr1000173 · Cited by 759
Brunk, E., & Rothlisberger, U. (2015) · Chemical Reviews · doi:10.1021/cr500628b · Cited by 494
Christensen, A. S., Kubař, T., Cui, Q., & Elstner, M. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00584 · Cited by 446
Seabra, G. D. M., Walker, R. C., Elstner, M., Case, D. A., & Roitberg, A. E. (2007) · The Journal of Physical Chemistry A · doi:10.1021/jp070071l · Cited by 257
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
Kranz, J. J., Kubillus, M., Ramakrishnan, R., von Lilienfeld, O. A., & Elstner, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00933 · Cited by 63
Metadata
- Authors
- Repasky, M. P., Chandrasekhar, J., & Jorgensen, W. L.
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 2002-10-18
- Volume
- 23
- Issue
- 16
- Pages
- 1601-1622
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- OpenAlex
- W2158384190
- Cited by
- 295
- References
- 0
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- closed
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- Repasky, M. P., Chandrasekhar, J., & Jorgensen, W. L. (2002). PDDG/PM3 and PDDG/MNDO: Improved semiempirical methods. Journal of Computational Chemistry. https://doi.org/10.1002/jcc.10162
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