Gaussian 16 y GaussView 6

Gaussian 16 es la última versión de esta serie de programas líderes en QUIMICA COMPUTACIONAL.que proporciona enormes capacidades para modelado electrónico de estructuras.Todas las versiones tienen las características científicas y de modelado, y ninguna impone limitaciones artificiales a los cálculos ,aparte de la potencia de la máquina en la que se use


Nuevas características:

Nuevas capacidades de MODELADO:

Mejoras para mayor Rendimiento

Mejoras para la Usabilidad

What’s New in Gaussian 16

Gaussian 16 brings a variety of new methods, property predictions and performance enhancements. Details about many of them are
given elsewhere in this brochure.
Modeling Excited States
Analytic frequency calculations for the time-dependent (TD) Hartree-Fock and DFT methods, including ONIOM electronic
embedding fully coupled with the environment of the MM region, without additional approximations
Geometry optimizations with the high accuracy EOM-CCSD method (analytic gradients)
Anharmonic analysis for calculating IR, Raman, VCD and ROA spectra
[Bloino12, Bloino12a, Bloino15]
. Calculations in solution take the
interaction between the excitation and the solvent field fully into account


Vibronic spectra prediction
[Barone09, Bloino10, Baiardi13]
Chiral spectroscopies: electronic circular dichroism (ECD) and circularly polarized luminiscence (CPL)
[Barone12, Barone14]
Modeling of resonance Raman spectroscopy
[Egidi14, Baiardi14]
Computation of electronic energy transfer (EET)
Ciofini’s excited state charge transfer diagnostic (
[LeBahers11, Adamo15]


EOM-CCSD solvation interaction models of Caricato
New Methods
Many DFT functionals have been added to Gaussian since the initial release of G09, including APFD
, functionals from the
Truhlar group (most recently MN15 and MN15L
) and PW6B95
Additional double-hybrid methods: DSDPBEP86
[Bremond11, Bremond14]
Empirical dispersion for a variety of functionals, using the schemes of Grimme (GD2, GD3, GD3BJ)
[Grimme06, Grimme10, Grimme11]
and others.
The PM7 semi-empirical method, both in the original formulation
and with modifications for continuous potential energy
Performance Enhancements
Support for NVIDIA K40 and K80 GPUs on Linux systems, for HF and DFT calculations.
Enhanced parallel performance on larger numbers of processors.
Speedups for key parts of several calculation types.
CASSCF improvements and support for active spaces of up to 16 orbitals
Ease of Use Features
Automatically recalculate the force constants every
step of a geometry optimization.
An expanded set of Link 0 commands and corresponding
file directives.
Tools for interfacing Gaussian with external programs in compiled languages such as Fortran and C and/or in interpreted languages
such as Python and Perl.
Generalized internal coordinates: define arbitrary redundant internal coordinates and coordinate expressions for use as geometry
optimization constraints.


Mejoras desde la versión 09

Especificaciones para Cálculos

The following calculation defaults are different in Gaussian 16:

The first two items were changed to ensure accuracy in several new calculation types (e.g., TD-DFT frequencies, anharmonic ROA). For these reasons, Integral=(UltraFine,Acc2E=12) was made the default. Using these settings generally improve the reliability of calculations involving numerical integration, e.g., DFT optimizations in solution. There is a modest increase in the CPU requirements for these options compared to the Gaussian 09 defaults of Integral=(FineGrid,Acc2E=10).

The G09Defaults keyword sets all four of these defaults back to the Gaussian 09 values. It is provided for compatibility with previous calculations, but the new defaults are strongly recommended for new studies.

Default Memory Use

Gaussian 16 defaults memory usage to %Mem=100MW (800MB). Even larger values are appropriate for calculations on larger molecules and when using many processors; refer to the Parallel Jobs tab for details.

TD-DFT Frequencies

TDDFT frequency calculations compute second derivatives analytically by default, since these are much faster than the numerical derivatives (the only choice in Gaussian 09).


Requerimientos Técnicos



GaussView 6

En este link, encontrara numerosos videos para descubrir las nuevas capacidades:

GaussView 6 Supported Computers





GaussView 5:Visualización y Química Extendida:

Gaussview es la más avanzada y potente interfaz gráfica para Gaussian.

Con esta herramienta usted podrá importar o construir estructuras moleculares que le interesen,monitorizar los cálculos de Gaussian, y visualizar los resultados,todo esto sin salir de la aplicación.

Además esta versión 5 incluye muchas nuevas capacidades orientadas al trabajo con grandes volumenes de aplicaciones y datos sobre química.

también proporciona soporte para los nuevos métodos de modelado incluidos en Gaussian 09.

Gaussview porporciona soporte para la importación o el trabajo con estructuras en ficheros PDB, con un método sencillo:

-seleccionar la estructura deseada del fichero multiestructura

-añadir átomos de hidrogeno automaticamente o manualmente de acuerdo a sus preferencias

-añadir átomos de hidrogeno los residuos ó a cadenas, hélices óa otras estructuras

-seleccionar átomos en los residuos o en estructuras secundarias

-determinar el residuo de cualqueir átomo seleccionado con el ratón

-Guardar al informaciónd e l residuo dentro de Gaussian 09 y sacar los resultados.






Gaussian 03 es la ultima version de los programas de series de estructuras de Gaussian.

Habitualmente usado por químicos,ingenieros,bioquimicos,físicos , y otros investigadores pertenecientes al area química.

Desde lso principios básicos de la química cuantica,Gaussian predice las energías,estructuras moleculares, y frecuencias vibracionales de los sistemas moleculares,así como propiedades moleculares.

Puede ser usado para estudiar las moléculas y sus reacciones bajo un amplio abanico de simulación de condiciones, cuya observación seria imposible en vivo, sin un modelo computacional que proporciona Gaussian.


El método gaussian 03 ONIOM proporciona la posiblidad de estudiar proteinas enteras y grandes conjuntos de moléculas,definiendo 2 o 3 layers cuya estructura es tratada a diferentes niveles

El método Onion proporciona mejoras para el estudio geométrico usando algoritmos cuadráticos coplejos y micro iteraciones

Las nuevas características de gaussian 03 repecto al metodo Oniom son:


-Personalización de los campos de fuerza de la mecanica molecular

-Cálculos eficientes de la frecuencia

-Calculo de las propiedades magnéticas y electricas.


Gaussian 2003 puede predecir spin-spin constantes que se añaden al NMR.


Los sistemas periodicos en esta versión se han ampliado, con la posibilidad de sistemas periodicos como los polimeros, cristales bajo metodos PBC,que permiten determinar la estructura y propiedades de estos cristales

Ademas metodos en 2 dimensiones  PBC pueden ser usados para modelar superficies , como reacciones en superficies y catálisis


Tambien metodos en 3 dimensiones PBC le permite ver y prever las estructuras en 3D de los cristales.

Tambien incluye un amplio rango de espectros y propiedades como:


Otra de sus propiedades es el estudio de las propiedades moleculares en las reaccoines entre la fase gaseosa y la de solucion,ofreciendo el PCM(Polarizable Continium Model) para modelizar sistemas en solucion


What's New in Gaussian 03

Cited references were chosen to be representative, accessible overviews. However, the reference list provided should not be considered exhaustive. For full citation lists, consult the printed or online version of the Gaussian 03 User's Reference.

New Chemistry

Enhanced ONIOM Method

The ONIOM facility in Gaussian 03 has been significantly enhanced over that offered by Gaussian 98 [1-2]:

·         The ONIOM facility [42] now supports electronic embedding for ONIOM(MO:MM) calculations: the electrostatic properties of the MM region can be taken into account during computations on the QM region.

o        A quadratic coupled algorithm takes into account the coupling between atoms using internal coordinates (typically, those in the model system) and those in Cartesian coordinates (typically, the atoms only in the MM layer), resulting in more accurate steps.

o        MO/MM optimizations perform micro-iterations for the atoms only in the MM layer between traditional optimization steps on the real system, resulting in faster and more reliable optimizations. Electronic embedding can be combined with micro-iterations.

·         Analytic frequencies are available for ONIOM(MO:MM) calculations, and frequencies for ONIOM(MO:MO) calculations are significantly faster.

·         Gaussian 03 provides support for general molecular mechanics (MM) force fields, including read-in and modified parameters. A standalone MM optimization program is also included.

·         Support for an external program for any ONIOM model (e.g., an external MM program may be used).

Solvent Effects

The Polarizable Continuum Model (PCM) solvation method has been improved and extended [3-8]:

·         The IEFPCM model [3,9] is now the default, and analytic frequencies are now available for this SCRF method. Additional performance improvements include a new cavity generation technique [10].

·         Many additional properties can be modeled in solution (discussed later in this brochure).

·         Gaussian 03 can also produce input for Klamt's COSMO-RS program [11], which computes solvation energies, partition coefficients, vapor pressure and other bulk properties via statistical mechanics techniques.

Periodic Boundary Conditions (PBC)

Gaussian 03 offers PBC calculations for studying periodic systems: e.g., polymers, surfaces and crystals [12-15]. PBC calculations solve the Schrödinger equation subject to the boundary condition that the molecule and the wavefunction repeat indefinitely in one, two or three directions. Hartree-Fock and DFT energies and gradients are available for periodic systems.

Molecular Dynamics

Dynamics calculations can provide qualitative understanding of reaction mechanisms and quantitative details about the reaction such as product distributions. There are two main approaches to performing these calculations:

·         In Born-Oppenheimer Molecular Dynamics (BOMD), classical trajectories are calculated on a local quadratic approximation to the potential energy surface (for a review, see [16]). Our implementation [17] uses a Hessian-based algorithm for the predictor and corrector steps, an approach which results in a factor of 10 or more improvement in the step size over previous implementations. While it can make use of analytic second derivatives, BOMD is available for all theoretical methods having analytic gradients.

·         Gaussian 03 also offers Atom-Centered Density Matrix Propagation (ADMP) method [18-20] molecular dynamics (available for Hartree-Fock and DFT). Drawing on the work of Car and Parrinello [21], ADMP propagates the electronic degrees of freedom rather than solving the SCF equations at each nuclear geometry. Unlike CP, ADMP propagates the density matrix rather than the MOs. This is much more efficient if an atom-centered basis set is being used. This approach overcomes some limitations inherent in the CP implementation: e.g., there is no need to substitute D for H in order to maintain energy conservation, and both pure and hybrid DFT functionals can be used. ADMP calculations can also be performed in the presence of a solvent [22], and ADMP can be used in ONIOM(MO:MM) calculations.

Excited States

There are additions and several enhancements to excited states methods:

·         CASSCF calculations are now more efficient due to a new algorithm for evaluating the CI-vector in the full configuration interaction calculation [23]. Practical active spaces increase to about 14 orbitals for energies and gradients (they remain at about 8 orbitals for frequencies).

·         The Restricted Active Space (RAS) SCF method [24] is also available[25]. RASSCF calculations partition the molecular orbitals into five sections: the lowest lying occupieds (considered inactive in the calculation), the RAS1 space of doubly occupied MOs, the RAS2 space containing the most important orbitals for the problem, the RAS3 space of weakly occupied MOs and the remaining unoccupied orbitals (also treated as frozen by the calculation). Thus, the active space in CASSCF calculations is divided into three parts in a RAS calculations, and allowed configurations are defined by specifying the minimum number of electrons that must be present in the RAS1 space and the maximum number that must be in the RAS3 space, in addition to the total number of electrons in the three RAS spaces.

·         NBO orbitals for may be used for defining CAS and RAS active spaces. These provide good initial guesses for the required antibonding orbitals which correlate with the bonds/lone pairs of interest.

·         The Symmetry Adapted Cluster/Configuration Interaction (SAC-CI) method of Nakatsuji and coworkers is now included in Gaussian. This method has many uses: predicting very accurate excited states of organic systems, studying two-to-many electron excitation processes such as the shake-up in the ionization spectrum, and other problem types. For an overview of the SAC-CI method, see [26-27].

·         Solvent Effects: Excited states can be modeled in the presence of a solvent [28-29] using the CI-Singles and Time Dependent Hartree-Fock and DFT methods.

Molecular Properties

Gaussian 03 provides many new molecular properties:

·         Spin-spin coupling constants [31-34], which can aid in distinguishing conformations in magnetic spectra.

·         g tensors and other hyperfine spectra tensors [49-52]. Gaussian 03 can produce nuclear electric quadrupole constants, rotational constants, the quartic centrifugal distortion terms, the electronic spin rotation terms, the nuclear spin rotation terms, the dipolar hyperfine terms and Fermi contact terms. All tensors can be exported to Pickett's fitting and spectral analysis program [53].

·         Harmonic vibration-rotation coupling [43-44]: A spectroscopic property dependent on the coupling between molecules' vibrational and rotational modes. It is used to analyze detailed rotational spectra.

·         Anharmonic vibration and vibration-rotation coupling [44-48]: Using perturbation theory, these higher order terms are incorporated into frequency calculations in order to produce more accurate results.

·         Pre-resonance Raman spectra which yield information about ground state structures, connectivity, and vibrational states.

·         Optical Rotations/Optical Rotary Dispersion: Used to distinguish enantiomers of chiral systems [39-41] (this property is computed via GIAOs).

·         Electronic Circular Dichroism (ECD): This property is the differential absorption in the visible and ultraviolet regions for optically active molecules, and is used to assign absolute configurations [35-36]. Predicted spectra can also be useful in interpreting existing ECD data and peak assignments.

·         Frequency-dependent polarizabilities and hyperpolarizabilities, which can be used to study how the molecular properties of materials vary with wavelength of the incident light [37-38].

·         Magnetic susceptibilities computed with Gauge-Independent Atomic Orbitals (GIAOs) [30]. This property is the magnetic analogue to the electric polarizability, and it provides insight into the diamagnetic vs. paramagnetic character of molecules.

·         Solvent Effects: Electric and magnetic properties and the various spectra can be predicted for systems in solution as well as ones in the gas phase [54-56].

·         Properties with ONIOM: The ONIOM method may be used with these electric and magnetic properties.

Fundamental Algorithms

·         Much Better Initial Guesses: Gaussian 03 uses the Harris functional for generating initial guesses. This functional [59] is a non-iterative approximation to DFT, and it produces initial guesses which are better than those produced by Gaussian 98: for example, there are modest improvements for organic systems but very substantial improvements for compounds containing metals.

·         New SCF Convergence Algorithm: The default SCF algorithm now uses a combination of two Direct Inversion in the Iterative Subspace (DIIS) extrapolation methods EDIIS and CDIIS. EDIIS [58] uses energies for extrapolation, and it dominates the early iterations of the SCF convergence process. CDIIS, which performs extrapolation based on the commutators of the Fock and density matrices, handles the latter phases of SCF convergence. This new algorithm is very reliable, and previously troublesome SCF convergence cases now almost always converge with the default algorithm. For the few remaining pathological convergence cases, Gaussian 03 offers Fermi broadening and damping in combination with CDIIS (including automatic level shifting).

·         Density Fitting for Pure DFT Calculations: Gaussian 03 provides the density fitting approximation [60,61] for pure DFT calculations. This approach expands the density in a set of atom-centered functions when computing the Coulomb interaction instead of computing all of the two-electron integrals. It provides significant performance gains for pure DFT calculations on medium sized systems too small to take advantage of the linear scaling algorithms without a significant degradation in accuracy. Gaussian 03 can generate an appropriate fitting basis automatically from the AO basis, or you may select one of the built-in fitting sets.

·         Faster and Automated FMM: The fast multipole method (FMM) in Gaussian 98 allowed the computational cost for large DFT calculations to scale linearly with system size. In Gaussian 03, improvements to these algorithms [57] means that their performance gains can be realized for systems of more modest size as well (~100 atoms for pure DFT calculations and ~150 atoms with hybrid functionals). In addition, this feature is now fully automated: the program invokes FMM automatically when appropriate.

·         Coulomb Engine: Gaussian 03 incorporates a faster algorithm for the Coulomb operator for pure DFT calculations. The Coulomb engine produces the exact Coulomb matrix without explicitly forming four center two electron integrals. This substantially reduces the CPU time for the Coulomb problem in pure DFT calculations.

·         O(N) Exact Exchange: A new algorithm for Hartree-Fock and DFT calculations using hybrid functionals implements screening of the exact exchange contribution via the density matrix to eliminate the many zero value terms [62]. This technique results in a linear computational cost for these methods without accuracy loss.

Additional Features

o        B1 [72] and variations, B98 [75, 83], B97-1 [76], B97-2 [77], and PBE1PBE [71] hybrid functionals.

o        The W1 method of Jan Martin [80-81], modified slightly to use the UCCSD method rather than ROCCSD for open shell systems (this method is denoted W1U). Gaussian 03 also includes the related W1BD method, which substitutes the BD method for coupled cluster [84]. This method is both more expensive and more accurate than CBS-QB3 and G3.

·         Douglas-Kroll-Hess scalar relativistic Hamiltonian: This feature allows all electron calculations for heavier atoms (first and second transition rows) when ECPs are not accurate enough [63-66]. For an overview, see [67-68]


1 T. Vreven, K. Morokuma, Ö. Farkas, H. B. Schlegel, and M. J. Frisch, J. Comp. Chem. in press (2003).
2 T. Vreven, I. Komáromi, S. Dapprich, K. S. Byun, J. A. Montgomery Jr., K. Morokuma, and M. J. Frisch, in prep. (2003).
3 B. Mennucci, E. Cancès, and J. Tomasi, J. Phys. Chem. B 101, 10506 (1997).
4 B. Mennucci and J. Tomasi, J. Chem. Phys. 106, 5151 (1997).
5 M. Cossi, N. Rega, G. Scalmani and V. Barone, J. Chem. Phys. 114, 5691 (2001).
6 M. Cossi, G. Scalmani, N. Rega, and V. Barone, J. Chem Phys. 117, 43 (2002).
7 M. Cossi, N. Rega, G. Scalmani, V. Barone, J. Comp. Chem. in press (2003).
8 G. Scalmani, V. Barone, K. N. Kudin, C. S. Pomelli, G. E. Scuseria, and M. J. Frisch, Theo. Chem. Acc., submitted (2003).
9 E. Cancès and B. Mennucci, J. Chem. Phys. 114, 4744 (2001).
10 G. Scalmani, N. Rega, M. Cossi and V. Barone, in prep. (2003).
11 F. Eckert and A. Klamt, AIChE Journal 48, 369 (2002).
12 K. N. Kudin and G. E. Scuseria, Chem. Phys. Lett. 289, 611 (1998).
13 K. N. Kudin and G. E. Scuseria, Chem. Phys. Lett. 283, 61 (1998).
14 K. N. Kudin and G. E. Scuseria, Phys. Rev. B 61, 16440 (2000).
15 O. V. Yazyev, K. N. Kudin, and G. E. Scuseria, Phys. Rev. B 65, art. no. 205117 (2002).
16 K. Bolton, W. L. Hase, and G. H. Peshlherbe, in Modern Methods for Multidimensional Dynamics Computation in Chemistry, Ed. D. L. Thompson (World Scientific, Singapore, 1998) 143.
17 J. M. Millam, V. Bakken, W. Chen, W. L. Hase, and B. H. Schlegel, J. Chem. Phys. 111, 3800 (1999).
18 H. B. Schlegel, J. M. Millam, S. S. Iyengar, G. A. Voth, A. D. Daniels, G. E. Scuseria, and M. J. Frisch, J. Chem. Phys. 114, 9758 (2001).
19 S. S. Iyengar, H. B. Schlegel, J. M. Millam, G. A. Voth, G. E. Scuseria, and M. J. Frisch, J. Chem. Phys. 115, 10291 (2001).
20 H. B. Schlegel, S. S. Iyengar, X. Li, J. M. Millam, G. A. Voth, G. E. Scuseria, and M. J. Frisch, J. Chem. Phys. 117, 8694 (2002).
21 R. Car and M. Parrinello, Phys. Rev. Lett. 55, 2471 (1985).
22 N. Rega, S. S. Iyengar, G. A. Voth, H. B. Schlegel, G. E. Scuseria, and M. J. Frisch, in prep. (2003).
23 M. Klene, M. A. Robb, M. J. Frisch, and P. Celani, J. Chem. Phys. 113, 5653 (2000).
24 J. Olsen, B. O. Roos, P. Jorgensen, and H. J. A. Jensen, J. Chem. Phys. 89, 2185 (1988).
25 M. Klene, M. A. Robb, L. Blancafort, and M. J. Frisch, in prep (2003).
26 H. Nakatsuji, in Computational Chemistry-Reviews of Current Trends, Ed. J. Leszcynski, Vol. 2 (World Scientific, Singapore, 1997) 62-124.
27 M. Ehara, M. Ishida, K. Toyota, and H. Nakatsuji, in Reviews in Modern Quantum Chemistry, Ed. K. D. Sen (World Scientific, Singapore, 2002) 293.
28 B. Mennucci, R. Cammi, and J. Tomasi, J. Chem. Phys. 109, 2798 (1998).
29 M. Cossi and V. Barone, J. Chem. Phys. 115, 4708 (2001).
30 K. Ruud, T. Helgaker, K. L. Bak, P. Jorgensen, and H. J. A. Jensen, J. Chem. Phys. 99, 3847 (1993).
31 V. Sychrovsky, J. Grafenstein, and D. Cremer, J. Chem. Phys. 113, 3530 (2000).
32 T. Helgaker, M. Watson, and N. C. Handy, J. Chem. Phys. 113, 9402 (2000).
33 V. Barone, J. E. Peralta, R. H. Contreras, and J. P. Snyder, J. Phys. Chem. A 106, 5607 (2002).
34 J. E. Peralta, R. H. Contreras, J. R. Cheeseman, M. J. Frisch, and G. E. Scuseria, in prep. (2003).
35 K. L. Bak, P. Jorgensen, T. Helgaker, K. Ruud, and H. J. A. Jensen, J. Chem. Phys. 98, 8873 (1993).
36 J. Autschbach, T. Ziegler, S. J. A. van Gisbergen, and E. J. Baerends, J. Chem. Phys. 116, 6930 (2002).
37 J. E. Rice and N. C. Handy, J. Chem. Phys. 94, 4959 (1991).
38 J. E. Rice and N. C. Handy, Int. J. Quant. Chem. 43, 91 (1992).
39 T. Helgaker, K. Ruud, K. L. Bak, P. Jorgensen, and J. Olsen, Faraday Discuss. 99, 165 (1994).
40 P. J. Stephens, F. J. Devlin, J. R. Cheeseman, M. J. Frisch, and C. Rosini, Organic Letters 4, 4595 (2002).
41 P. J. Stephens, F. J. Devlin, J. R. Cheeseman, M. J. Frisch, O. Bortolini, and P. Besse, Chirality 14, (2002).
42 S. Dapprich, I. Komáromi, K. S. Byun, K. Morokuma, and M. J. Frisch, J. Mol. Struct. (Theochem) 462, 1 (1999).
43 W. H. Miller, in Potential Energy Surfaces and Dynamical Calculations, Ed. D. G. Truhlar (Plenum, New York, 1981) 265.
44 V. Barone, J. Comp. Chem. in prep. (2003).
45 W. H. Miller, N. C. Handy, and J. E. Adams, J. Chem. Phys. 72, 99 (1980).
46 D. A. Clabo, W. D. Allen, R. B. Remington, Y. Yamaguchi, and H. F. Schaefer III, Chemical Physics 123, 187 (1988).
47 C. Minichino and V. Barone, J. Chem. Phys. 100, 3717 (1994).
48 V. Barone and C. Minichino, Theochem 330, 365 (1995).
49 R. F. Curl Jr., Mol. Phys. 9, 585 (1965).
50 J. Gauss, K. Ruud, and T. Helgaker, J. Chem. Phys. 105, 2804 (1996).
51 V. Barone, Chem. Phys. Lett. 262, 201 (1996).
52 F. Neese, J. Chem. Phys. 115, 11080 (2001).
53 H. M. Pickett, J. Mol. Spec. 148, 317 (1991).
54 J. Tomasi, R. Cammi, B. Mennucci, C. Cappelli, and S. Corni, Phys. Chem. Chem. Phys. 4, 5697 (2002).
55 B. Mennucci, J. Tomasi, R. Cammi, J. R. Cheeseman, M. J. Frisch, F. J. Devlin, S. Gabriel, and P. J. Stephens, J. Phys. Chem. A 106, 6102 (2002).
56 C. Cappelli, S. Corni, B. Mennucci, R. Cammi, and J. Tomasi, J. Phys. Chem. A 106, 12331 (2002).
57 K. N. Kudin and G. E. Scuseria, J. Chem. Phys. 111, 2351 (1999).
58 K. N. Kudin, G. E. Scuseria, and E. Cancès, J. Chem. Phys. 116, 8255 (2002).
59 J. Harris, Phys. Rev. B. 31, 1770 (1985).
60 B. I. Dunlap, J. Chem. Phys. 78, 3140 (1983).
61 B. I. Dunlap, J. Mol. Struct. (Theochem) 529, 37 (2000).
62 J. C. Burant, K. Kudin, G. E. Scuseria, G. W. Trucks, and M. J. Frisch, in prep. (2003).
63 M. Douglas and N. M. Kroll, Ann. Phys. (NY) 82, 89 (1974).
64 B. A. Hess, Phys. Rev. A 32, 756 (1985).
65 B. A. Hess, Phys. Rev. A 33, 3742 (1986).
66 G. Jansen and B. A. Hess, Phys. Rev. A 39, 6016 (1989).
67 M. Barysz and A. J. Sadlej, Theochem 573, 181 (2001).
68 W. A. deJong, R. J. Harrison, and D. A. Dixon, J. Chem. Phys. 114, 48 (2001).
69 N. C. Handy and A. J. Cohen, Mol. Phys. 99, 403 (2001).
70 J. P. Perdew, K. Burke, and M. Ernzerhof, Phys. Rev. Lett. 77, 3865 (1996).
71 J. P. Perdew, K. Burke, and M. Ernzerhof, Phys. Rev. Lett. 78, 1396 (1997).
72 A. D. Becke, J. Chem. Phys. 104, 1040 (1996).
73 T. Van Voorhis and G. E. Scuseria, J. Chem. Phys. 109, 400 (1998).
74 A. D. Boese and N. C. Handy, J. Chem. Phys. 114, 5497 (2001).
75 H. L. Schmider and A. D. Becke, J. Chem. Phys. 108, 9624 (1998).
76 F. A. Hamprecht, A. J. Cohen, D. J. Tozer, and N. C. Handy, J. Chem. Phys. 109, 6264 (1998).
77 P. J. Wilson, T. J. Bradley, and D. J. Tozer, J. Chem. Phys. 115, 9233 (2001).
78 L. A. Curtiss, K. Raghavachari, P. C. Redfern, V. Rassolov, and J. A. Pople, J. Chem Phys. 109, 7764 (1998).
79 L. A. Curtiss, P. C. Redfern, K. Raghavachari, V. Rassolov, and J. A. Pople, J. Chem. Phys. 110, 4703 (1999).
80 S. Parthiban and J. M. L. Martin, J. Chem. Phys. 114, 6014 (2001).
81 J. M. L. Martin and G. de Oliveira, J. Chem. Phys. 111, 1843 (1999).
82 E. V. R. de Castro and F. E. Jorge, J. Chem. Phys. 108, 5225 (1998).
83 A. D. Becke, J. Chem. Phys. 107, 8554 (1997).
84 J. A. Montgomery Jr., M. J. Frisch, and J. M. L. Martin, in prep (2003)

GaussView 3.0 Features at a Glance

View GaussView Brochure

New features are indicated in deep indigo.

Build and Examine Molecules in 3 Dimensions

Set Up Gaussian 03 Calculations

Visualize Gaussian 03 Results

Specify and Save User Preferences

Customize many aspects of GaussView functionality: