Spartan’24 is the latest release of the Spartan molecular modelingapplication and is a culmination of visualization, computational capabilities,
and data interpretation tools, developed and refined since 1991. New andimproved features in Spartan’24 include: extension to the Spartan Spectra& Properties Database (>317,000 molecules and growing), as well as a
new SSPD compatibility task, enabling a single submitted calculation sousers can easily expand their local SSPD to include molecules of specificinterest. This release is also the first to incorporate computational models
developed using AI technology. This release introduces a set of 5 uniqueneural network models, supporting a growing set of applications, including:
IMPROVED CONFORMER ENERGY CALCULATIONS:
MOLECULAR MECHANICS
Assessment of the widely used MMFF molecular mechanics approachin conformational searching suggests that resulting conformer energy
differences are not very accurate. Spartan’24 employs a neural networkmodel that has been trained on wB97X-V/6-311+G(2df,2p)[6-311G*]
energy calculations from underlying MMFF geometries. In practice, aseach MMFF conformer is generated its energy is corrected with a neuralnet to obtain a “better” energy. This significantly improves accuracy andreduces the number of conformers that need to be considered with better
approaches (in Spartan’s multi-step protocols for conformer distribution
and the NMR spectra task).
ESTIMATED DENSITY FUNCTIONAL GEOMETRIES
Spartan’24 provides a neural network routine trained to more than 6 milion
energy and force calculation from wB97X-D/6-31G* for molecules ranging
from 200-600 AMU. Results return several orders of magnitues faster thanthe underlying quantum chemical model and assessment suggests a highlevel of agreement. This routine is also available for use within the multistep NMR Spectrum task and utilized in conformational searching.
IMPROVED CONFORMER ENERGY CALCULATIONS:BOLTZMANN WEIGHTS
The penultimate and most costly step in the Spartan’s multi-step protocols
for conformer searching and for calculating the NMR spectrum of aflexible molecule involve determining Boltzmann weights for all accessible
conformers using the wB97X-V/6-311+G(2df,2p)//wB97X-D/6-31G* model.
As even simple organic molecules may access hundreds or thousandsof conformers, practical time concerns discourage and limit application.
Spartan’24 allows replacement of this step with a neural net trained
on wB97X-V/6-311+G(2df,2p) energy calculations from underlying
wB97X-D/6-31G* structures, cutting the overall computation cost for an
NMR calculation by more than half.

IMPROVED ENERGY CALCULATIONS
It is generally accepted that better (more complicated) functionals
and larger (more flexible) basis sets will lead to significantly improved
reaction energies, but at a significant increase in computation cost. Threefunctionals wB97X-V, wB97M-V and wB97M(2) with 6-311+G(2df,2p) using
wB97X-D/6-31G* geometries, closely approach the results of CCSD(T)calculations, the “gold standard”. Spartan’24 incorporates three neuralnets that estimate total energies from the above models starting from
wB97X-D/6-31G* geometries with rms errors of only a few kJ/mol. Practicallyspeaking, this extends the size of systems that can be routinely submitted
from a few hundred to well over molecular weight 1000 amu.
MULTI-STEP WORKFLOWS
Collectively, the machine learning models are available in most of
Spartan’24’s available computational tasks, including: Energy, Equilibrium
Geometry, Equilibrium Conformer, Conformer Distribution, and the Boltzmann
Weighted NMR Spectrum task1. In particular, the multi-step conformational
searching components are available at orders of magnitude faster than
the same tasks run with pure quantum chemical models.
TRAINING DATA
SpookyNet2 has been used to generate the initial five neural networks.
Data sets comprise ≈150k molecules with ≈1.8 million conformers obtained
from wB97X-V/6-311+G(2df,2p)[6-311G*]//MMFF calculations for the MMFF
correction neural net. Collections of ≈120,000 molecules each withequilibrium geometries and 50 distorted geometries (a total of ≈6.1 million
structures) obtained from wB97X-D/6-31G* calculations for the estimated
wB97X-D/6-31G* geometry neural net, ≈250,000 molecules obtained from
wB97X-V/6-311+G(2df,2p)//wB97X-D/6-31G* calculations for the estimated
wB97X-V/6-311+G(2df,2p) energy neural net, ≈250,000 molecules obtained
from wB97M-V/6-311+G(2df,2p)//wB97X-D/6-31G* calculations for theestimated wB97M-V/6-311+G(2df,2p) energy neural net, and ≈200,000
molecules obtained from wB97M(2)/6-311+G(2df,2p)//wB97X-D/6-31G*
calculations for the estimated wB97M(2)/6-311+G(2df,2p) energy neuralnet. Training sets for all three of the final energy neural networks wB97XV/6-311+G(2df,2p), wB97M-V/6-311+G(2df,2p), wB97M(2)/6-311+G(2df,2p)
will soon be searchable from within Spartan from the Databases dialog(searches available via substructure, formula, isomer, molecular weight,
and name).
1. J. Nat. Prod. 2019, 82 8, 2299-2306.
2. Nat. Commun. 2021, 12(1), 1-14.

SPARTAN’24 FEATURE SET
GRAPHICAL INTERFACE
Sketch organic, inorganic, organometallic molecules in 2D and automaticallyconvert to 3D structures. Groups, rings and ligands templates are available
Build organic, inorganic and organometallic molecules, peptides andnucleotides, and systematically substituted molecules in 3D
Auto-convert 3D structures to 2D sketches
Link seamlessly to ChemDraw® (Win Only)
Display/query molecules in a variety of model styles
Display dipole vector, hydrogen bonds, points and planes
Display and customize chemical function descriptors
Display user-defined annotations
New dual (2D/3D) visualization
Align molecules using structure,chemical function descriptors, or atom labels
Align molecules to pharmacophores
Build transition states using reaction arrows
Generate transition states from an extensive reaction library
Generate lists of regio and stereoisomers and lists of tautomers
Generate and display molecular orbitals, electron densities, spin densities
electrostatic potentials, electrostatic potential maps, orbital maps, and local
ionization potential maps
Optionally silhouette mesh and transparent surfaces for improved visualization
Generate and display orbital energy diagrams on-the-fly
Input experimental proton and 13C chemical shifts
Input observed 3-bond HH couplings and 2 and 3-bond CH couplings forsuperposition onto calculated COSY and HMBC plots
Toggle between default and absolute property ranges for property maps
Display electron density based on % of enclosed electrons
Highlight solvent accessible regions on surfaces and property maps
Display R/S chirality markers, invert chiral centers and absolute chirality
Automated estimated energies from machine learning routines
Integrated reaction energy calculator
Organize data in spreadsheets
Print and save calculated spectra tables as PDF files
Perform regression analysis and make, save and print 2D or 3D plots
Plot, print, and save calculated and experimental IR, Raman, and UV/vis spectra
Display calculated 1D (proton,13C, DEPT) and 2D (COSY, HSQC, HMBC) andexperimental 1D proton and 13C NMR spectra
Overlay experimental HMBC and COSY 2D NMR spectra with calculated results
Automatically check and optionally install updates

TASKS
Calculate strain energy, total energy and heat of formation
Determine gas-phase equilibrium and transition-state geometries
Determine geometries and IR spectra in the presence of solvent
Identify global minimum; calculate Boltzmann weights to obtain
conformer energy distributions
Build libraries of diverse conformers for use in similarity analysis
Perform similarity analysis on the basis of structure or chemical functionality
Scan geometrical coordinates and generate reaction sequences
Calculate reaction and activation energies
Calculate IR, Raman, UV/vis, and NMR spectra
Determine NMR spectra for flexible molecules
Compare calculated and experimental spectra
Search SSPD and NIST experimental database for match to calculated IR spectra
Mine databases of calculated molecular, atomic, and reaction properties

COMPUTATIONAL METHODS
Molecular Mechanics. SYBYL, MMFF94, MMFF(aq)Corrected MMFF (neural network energy)
Semi-Empirical. MNDO, AM1, RM1, PM3 (with transition metal parameters), PM6
Hartree-Fock molecular orbital theory
Density Functional Theory (functionals available from menus):
GGA functionals: B86PW91, BLYP, BPW91, B97-D2, SOGGA11, PBE-D3, VV10
GH-GGA functionals: B3LYP, B3LYP-D3, EDF2, B3PW91, B97-3, SOGGA11-X
RSH-GGA functionals: ωB97X-D, ωB97X-V, ωB97X, CAM-B3LYP, N12-SX, LC-VV10
mGGA functionals: B97M-V, M06-L, BMK, M11-L, TPSS-D3
GH-mGGA functionals: M06-2X, M06, M08-HX, M08-SO, MPW1B95
RSH-mGGA functionals: M11, ωB97M-V, MN12-SX
DH-RSH-mGGA functionals: ωB97M(2)Estimated ωB97X-D/6-31G* geometries (neural network structure)
Energies from neural networks trained to ωB97X-D, ωB97M-V, ωB97M(2)Møller Plesset. MP2, MP3, MP4, and RI-MP2
Wave function based advanced correlated models:G3(MP2)elect, G3elect, G4elect, QCISD, QCISD(T), CCSD, CCSD(T)
Wave function based advanced correlated methods include CCSD, CCSD(T), OD,
OD(T), QCCD, VOD, and VQCCD
Excited-state methods. CIS, CIS(D), RI-CIS(D), QCIS(D), QCISD(T) and TDDFT
Gradients available for CIS, CIS(D) and TDDFT
Thermochemical Recipes. T1, G3(MP2), G3, G4
Basis Sets (available from menus):
Pople: STO-3G, 3-21G, 6-31G*, 6-31G**, 6-31+G**, 6-311G**, 6-311+G**,
6-311G(2d,p), 6-311+G(2d,p), 6-311+G(2df,2p), 6-311+G(3df,2p); other variations
on 6-31G and 6-311G may be specified
Dunning: cc-pVDZ, aug-cc-pVDZ, cc-pVTZ, aug-cc-pVTZ, cc-pVQZ, aug-cc-pVQZ
Ahlrichs/Weigend: def2-SV(P), def2-SVPD, def2-TZVP, def2-TZVPPD, def2-QZVP,
def2-QZVPPD
Automatic use of pseudopotentials for elements >Kr
Dual basis set approximation available

PROPERTIES AND QSAR DESCRIPTORS
Mulliken, natural, and electrostatic-fit charges
Dipole and higher moments, polarizabilities and hyperpolarizabilities
Enthalpies, entropies and free energies
Solvation energies from C-PCM and a number of Truhlar models
Statistical tools for comparing calculated and experimental 1H and 13C NMR shifts
HOMO, LUMO and SOMO energies
Areas, polar surface areas and volumes based on space-filling models
Areas, accessible areas, polar areas, and volumes based on the electron density
Min/Max of electrostatic potential and Min of local ionization potential
Number of conformers and tautomers
Number of hydrogen bond acceptors and donors

SPECTRA
NMR, IR, Raman and UV/visible spectra may be calculated using a variety oftheoretical models including Hartree-Fock and density functional models. IR
spectra of an unknown molecule may be searched against the SSPD entries forbest match.
NMR
Corrected, Boltzmann-weighted NMR shifts are available for flexible molecules.
Spartan’24 provides neural network routines that significantly reduce computational
time.
Three-bond HH, CH, and CC coupling constants [using the B3LYP functional with
PCJ-0 (PCJ-1 and PCJ-2 basis sets are also available)] allow for 1H, 13C and DEPT 1D
spectra and COSY, HSQC and HMBC 2D spectra. Proton spectra may be displayedwith or without HH coupling. Observed HH and CH couplings may be inputted toconstruct “experimental” COSY and HMBC plots which can then be superimposed
onto calculated plots.
I R and raman
Infrared (and Raman) frequencies calculated from ωB97X-D/6-31G*, B3LYP/6‑31G*
and EDF2/6-31G* density functional models are scaled to account for systematic
errors associated with the harmonic approximation. Frequencies and intensities
are fit to a Lorentzianfunction with a line widthparameter. Alternatively,
scale and line width
may be adjusted tobest fit a spectrum
calculated using any
theoretical model to an
experimental spectrum.
	uv / visibl e
UV/visible spectra are obtained by explicit calculation of the ground stateenergies and the low-lying excited states. CIS models are paired with HartreeFock models. TDDFT (time dependent density functional) models are paired withdensity functional models.

ADDITIONAL FEATURES
Multi-core parallel processing for Hartree-Fock, density functional, RI-MP2, andthermochemical recipes.
Automatic processing of groups of molecules
Automatic use of molecular symmetry
View recent documents from File menu
Optimize using constraints and/or frozen atoms
NOEs for conformational searching
Identify tautomers and generate tautomer lists
Import experimental IR, Raman, and NMR spectra
Import structures in InChI, SMILES, CDX, CIF, SKC, SDF, TGF, XYZ, Macromodel, PDB,SYBYL MOL and MOL2 format
Retrieve structures from Cambridge Structural Database and Protein Data Bank
Extract ligands and binding sites from proteins (PDB files)

DATABASES
Spartan Spectra and Properties Database (SSPD)® comprises a collection of
317,000 entries, primarily obtained from the wB97X-D/6-31G* model. Included are
the optimized geometry, the energy and a selection of molecular properties, thewave function (allowing on-the-fly generation of graphical surfaces), and the
NMR spectrum. The infrared spectrum is provided from EDF2/6-31G*. SSPD entries
can replace user-built structures, both collections are searchable by substructure,
name, formula, and isomer.
Spartan Reaction Database (SRD)® will comprise transition states for ≈8,000
reactions searchable by combination of substructure and “reaction arrows” fromeither 2D sketches or 3D models.

6-311+G(2df,2p) ENERGY DATABASES
SSPD entries now include three highly accurate density functional databases,
comprised of ≈250,000 wB97X-V/6-311+G(2df,2p) and wB97M-V/6-311+G(2df,2p),
and ≈200,000 wB97M(2)/6-31+G(2df,2p) energies, from underlying wB97X-D/6-31G*
geometries.

CORRECTED MMFF ENERGIES
Access extremely fast wB97X-D/6-311+G(2df,2p)[6-311G*]//MMFF energy
approximation for use in conformational energy comparisons from a newmachine learning routine.

ESTIMATED DENSITY FUNCTIONAL STRUCTURES
Machine learning routine provides estimated wB97X-D/6-31G* structures from
MMFF geometries, at something like 1-2 orders of magnitude faster than structuresderived from quantum mechanics.

ESTIMATED QM ENERGIES
Machine learning routines start from wB97X-D/6-31G* structures and instantly
provides estimated wB97X-V, wB97M-V, and wB97M(2) [all trained with the
6-311+G(2df,2p) basis set] for improved energies at several orders of magnitude
faster than they can be calculated from quantum mechanics.

SPARTAN’24 LICENSING *Academic Government Commercial
Spartan’24 (up to 16 cores)**

Spartan’24 (greater than 16 cores)**$ 1,800$ 3,600$ 5,400$ 2,700$ 5,400$ 8,100

Spartan’24 Parallel Suite

Academic Lab Pricing

5 Seat Lab License		

$ 7,650

10 Seat Lab License		$ 13,500

20 Seat Lab License		$ 18,000

New licensing includes one year of maintenance, providing for the full SSPD, all minor and major
version updates, any necessary license transfers, and priority technical support.
* Annual campus-wide site license available, contact sales@wavefun.com for pricing.
** Spartan’24 provides parallel processing for select methods and tasks and is available for up
to 16 cores (standard), or as a greater than 16 core license (for HPC systems). Spartan also
includes access to the Spartan Spectra and Properties Database (SSPD), and the ability to
act as a computation server for other Spartan licenses or the iSpartan app.

MINIMUM SYSTEM REQUIREMENTS
WINDOWS• Intel or AMD (64-bit only)• Windows 10 or 11• 128 GB disk space or higher(SSD recommended)• 4 GB of RAM (at least 2GB RAM per core)
MACINTOSH• Intel or M1 chips (only)• OS X 10.12 (Sierra) - 14.X (Sonoma)• 128 GB disk space or higher(SSD recommended)• 4 GB RAM (at least 2GB of RAM per core)
LINUX• Modern Intel or AMD Processors (64-bit only)• Linux RHEL 8, CentOS 8, Ubuntu 20.04 LTS• 128 GB disk space or higher (SSD recommended)• 4 GB RAM (at least 2GB of RAM per core)is a collaboration withWAVEFUNCTION, INC.
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