RESEARCH
SPARTAN'24 (Spartan’24 V. 1.3; Wavefunction, Inc.: Irvine, CA, USA, 2024 https://wavefun.com/spartan.)
Spartan is a general purpose molecular visualization environment. Molecular mechanics, quantum mechanics, and machine learning models provide calculated molecular data. Data analysis via an included general purpose spreadsheet (with regression), and a host of specialty dialogues for building or modifying molecules and transition states in 2D or 3D, analyzing reactions, spectra display, NMR analysis, graphical model display, and access to an included (and extendable) DFT database of (>300k) precalculated molecules providing structure, energy spectra and properties). An elegantly designed allows for easy specification of task and model as well as multiple customizable calculation options.
Spartan likely is applicable to your group’s research if you do any of the following:
Conformational Searching
Comparing or prediction of heats of formation or electronic energy
Thermochemical properties (Gibbs energy, enthalpy, etc),
Prediction of relative acidity, basicity, bond strengths, hydrogen bond strengths
Excited state chemistry or UV/vis spectra work
Synthesis work, reaction energies and transition state isolation,
NMR assisted structure assignment
Molecular visualization and predictive Graphical models.
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SPARTAN SPECTRA AND PROPERTIES DATABASE (SSPD)
The Spartan Spectra and Properties Database (SSPD) is a collection of C and proton NMR, and infrared spectra, together with a wide range of calculated molecular (and atomic) properties, QSAR descriptors and thermodynamic data for >300,000 small molecules (up to molecular weight ~800 amus).
SOFTWARE COMPARISON CHART (PDF)
In order to best meet your needs, Wavefunction offers three versions of Spartan. Click here to download the Software Comparison Chart (pdf).
JOURNAL ARTICLES CITING SPARTAN (PDF)
Click here to download a list of journal articles citing Spartan (pdf).
Machine Learning models
trained to good quality
(empirically corrected)
DFT GIAO NMR shifts
are now available for
1H and 13C centers.
2025, 90, 32, 11478-11485.
Three new “Est. Density Functional Energy” machine learning models are described, assessed, and benchmarked in our latest paper available now: Journal of Computational Chemistry, 2025, 46(12), e70129. Publication Date: May 14, 2025.
The machine learning model “Est. Density Functional” definition, assessment and benchmarking paper has been released: Journal of Chemical Information and Modeling, 2025, 65, 5, 2314-2321. Publication Date February 17, 2025.
We are pleased to announce the publication defining the machine learning model termed “Corrected MMFF”
in the current Spartan’24 release, available from the:
Journal of Computational Chemistry, 2025, 46(1), e70016. Publication Date: January 5, 2025.
Spartan’s empirical corrections improve accuracy of DFT C shifts to within ≈2 ppm for rigid molecules. The multi-step NMR Spectrum protocol, providing C shifts within ≈3.5 - 4 ppm for conformationally flexible molecules is defined and assessed. Journal of Natural Products, 2019, 82, 8, 2299-2306.
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Featured Article: Total synthesis of Chartelline C. Computer assisted with energy, geometry, and NMR
calculations from Spartan’24.
Journal of the American Chemical Society, 2025, 147, 28, 24921-24931.