DATA SHEET [RELEASE 2024-1]

JaguarEver since its inception, Jaguar was designed with
one goal in mind — to help researchers solve
practical, real-world problems. To meet this goal,
Jaguar’s development has focused on performanceand scientific accuracy. Furthermore, Jaguar
is easy to use — with a world-class graphicalinterface and automated workflows that guide
new users through complex ab initio quantum
mechanics (QM) analyses for a vast range ofchemical systems. Jaguar is fully integrated into
Schrödinger’s suites of scientific solutions, making
interoperability with other programs seamless.

Key Advantages
Jaguar specializes in fast electronic structure
predictions for molecular systems of medium andlarge size via the use of the pseudospectral (PS)method1 and computational strategies that scalereasonably as system size grows, in particulardensity functional theory (DFT). Jaguar supports
parallel computation through OpenMP to further
take advantage of modern hardware improvements.
Significant ongoing efforts have been devoted to
improving the accuracy of predictions including thetransition metal initial guess wavefunction algorithm,
recent advances in pKa predictions, and enabling
access to machine learning potentials (MLP).
Schrödinger has devoted and continues to devote
development resources to enhance Jaguar’s
feature set and to improve Jaguar’s robustness
and performance. To date, Jaguar has madesignificant contributions in both life and materials
science research, and we invite you to learn more
about Jaguar in a published review article in theInternational Journal of Quantum Chemistry.2

Automated Workflows for
Advanced Analyses••••••••••Counterpoise calculations
pKa prediction (through separate Jaguar pKaand Macro-pKa modules)
Automated transition state search (through aseparate AutoTS module)3Intermolecular hydrogen bond binding energy
Fukui functions calculations
Heat of formation
Conformationally averaged IR/VCD, UV-vis/ECD,and NMR spectra calculations (available throughJaguar Spectroscopy module)
ΔE, ΔH, ΔG of a chemical reaction
Conformer and tautomer predictor
Estimated computational time throughmachine learning

Molecular Properties
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Electrostatic potential (ESP) surface and analysis
Average local ionization energy (ALIE) surfaceElectron density and spin density surfaces
Dipole, quadrupole, octupole, andhexadecapole moments
Analytic static polarizabilities, first andsecond hyperpolarizabilities
Analytic dynamic (frequency-dependent)
polarizabilities and hyperpolarizabilities
Fukui functions and atomic indices
Visualized noncovalent interactions
Natural bond orbital (NBO) analysis throughthe built-in third-party NBO 7.0 package
Harmonic and anharmonic vibrational frequencies
Mulliken population analysis
Mulliken, ESP, and Stockholder charges
Thermochemical properties: constant volume
heat capacity, internal energy, enthalpy, entropy,
Gibbs free energy at varying temperaturesand pressures, heat of formation
Ab initio pKa prediction (through separate
Jaguar pKa and Macro-pKa modules)
Excited states through CIS, TDDFT, and ΔSCF theories

Geometry Optimization••
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Geometry optimizations for equilibrium structures
and transition states, in gas phase and solution
Geometry optimizations of excited states with TDDFT
Cartesian, redundant, and internal coordinates
Constraints on bond lengths, bondangles, torsional angles
Constraints on Cartesian or internal coordinates, frozen
or harmonicStandard, linear synchronous transit(LST), and quadratic synchronous transit(QST) transition state optimizations
Fischer-Almlöf, Schlegel, user-supplied, and quantummechanical Hessian guesses; BFGS, Powell, andMurtagh-Sargent/Powell Hessian updates
Intrinsic reaction coordinate (IRC) scans
Rigid, relaxed, and dynamic coordinate scans
Post-convergence analysis
Prevent chemical reactions duringgeometry optimizations
Automatically detect and moveaway from saddle points

1.00.8Intensity

Product Details

0.6

Conformer 3

Conformer 21

Conformer 23Boltzmann-averaged across
30 conformers

0.40.20.086420, ppm

A DFT-simulated, Boltzmann-averaged 1H NMR spectrum of an organic
molecule in chloroform together with the spectra of some of its
individual conformers.

Solvation
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Poisson-Boltzmann finite-element (PBF) self-consistent
reaction field (SCRF), energies and geometry optimizations
van der Waals radii- and isodensity-based PBF
Several versions of polarizable continuum model (PCM),
energies and geometry optimizations
SM6 and SM8 energies
Support for multiple solvents
Generation of COSMOTherm input files (.cosmo files)

Spectroscopy••••••••••

Infrared (IR) intensities
Vibrational circular dichroism (VCD) spectraElectronic circular dichroism (ECD) spectra
NMR shielding constants and spin-spin coupling constants
in gas phase and solution
1
H and 13C NMR chemical shifts
UV-vis spectra through CIS or TDDFT calculations
Visualizing conformationally-averaged IR, UV-vis, VCD, ECD,and NMR spectra through Maestro
Automated alignment of theoretical and experimental
IR/VCD spectra in Maestro
Raman intensities
Mössbauer isomer shifts and quadrupole splittings for57
Fe and other isotopes

Parallel•
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Analytic and pseudospectral calculations
HF and DFT energies
HF and DFT geometry optimizations
HF and DFT second derivatives (vibrational frequencies)
HF and DFT VCD spectra
Closed-shell LMP2 and RI-MP2 calculations
CIS and TDDFT energies
NMR shielding constants and spin-spin couplings
Polarizabilities and hyperpolarizabilities
SM6 and SM8 solvation models

An NCI plot for the equilibrium geometry ofN-methylacetamide dimer. Weak internalC – H O hydrogen bonds are indicatedas disks of purple and red, whereas theintermolecular bonding is indicated by theyellow-greenish regions, in which there arebond critical points in the density.
2Levels of TheoryDensity Functional Theory (DFT)••••••••••••

Exchange functionals: HFS, Xalpha, Becke 88, PW91,
Barone-modified PW91, OPTX
Correlation functionals: VWN, VWN5, LYP, P86, PW91,
B95, Perdew-Zunger 81, PBE, HCTH407
GGA functionals: SOGGA, SOGGA11, HCTH407, GAM, N12,BOP, GLYP, KT2, rPBE, revPBEMeta-GGA functionals: TPSS, revTPSS, SCAN, r2SCAN,NPKZB, MGGA-MS0, MGGA-MS1, MGGA-MS2
Hybrid functionals: B3LYP, O3LYP, X3LYP, B3P86, B3PW91,B97-1, B98, SB98, PBE0, PWB6K, PW6B95, MPW1K,
MPWB1K, MPW1PW91, BB1K, BHandH, BHandHLYP, M05,
M05-2X, M06, M06-2X, M06-L, M06-HF, PBE, B3LYP-LOC,M08-HX, M08-SO, MN12-L, MN15-L, and MN15, ωB97X-D3,
ωB97X-D3(BJ), ωB97X-V, ωB97M-V, TPSSh, revTPSSh,SOGGA11-X, SCAN0, MGGA-MS2h, MN12-SX
Dispersion-corrected functionals: B97-D, B3LYP-D3,B3PW91-D3, MPWB1K-D3, M05-D3, M05-2X-D3, M06-D3,M06-HF-D3, M06-2X-D3, PBE0-D3, B1B95-D3, BP86-D3,BLYP-D3, OLYP-D3, PBE-D3, B97-D3, B3LYP-MM, PBE-ulg,CAM-B3LYP-D3, ωPBE-D3, B3LYP-D3(BJ), B3PW91-D3(BJ),
PW6B95-D3(BJ), PBE0-D3(BJ), B1B95-D3(BJ), BP86-D3(BJ),BLYP-D3(BJ), OLYP-D3(BJ), PBE-D3(BJ), B97-D3(BJ), CAMB3LYP-D3(BJ), ωPBE-D3(BJ), ωB97X-D3, ωB97X-D3(BJ) andtheir D4-analogs
Long range-corrected functionals: CAM-B3LYP, LRC-BLYP,uPBE, uPBE0, ωPBE, ωPBEh, HSE03, HSE06, M11, M11-L,
ωB97, ωB97x, BNL, CAM-B3LYP-D3, ωPBE-D3, ωPBE-D3(BJ),ωB97X-D3, ωB97X-D3(BJ), MN12-SX, ωB97X-V, ωB97M-V
Dispersion-corrected long-range corrected functionals:
CAM-B3LYP-D3, ωPBE-D3, ωB97X-D
3c composite methods: HF-3c, PBEh-3c, B97-3c,
r2SCAN-3c, ωB97X-3c
Restricted (RHF), unrestricted (UHF), and spin-restricted(ROHF) wave functions
Energies and gradients are available for all, and second
derivatives for the vast majority of the functionals(including D3- and D4-corrected)
ΔSCF theory compatible with most density functionals

Hartree-Fock (HF)••

RHF, UHF, and ROHF wave functions
Energies, gradients, and second derivatives

Møller-Plesset Perturbation Theory•••

Local MP2 (LMP2) based on RHF and ROHF
wave functions
LMP2 energies, gradients, and numericalsecond derivatives
Resolution of identity MP2 (RI-MP2) energies
and gradients

Time-Dependent Density Functional Theory (TDDFT)
and Configuration Interaction Singles (CIS)••

RHF wave functions
Energies, gradients, and second derivatives

Zeroth Order Regular Approximation (ZORA)•••

Energies
Scalar and spin-orbit Hamiltonians
Can be combined with TDDFTOther••

Third-party GFN2-xTB semiempirical theory
Machine learning potential QRNN4Basis Sets
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Gaussian-type orbitals (GTO); s-, p-, d-, f-, and g-functions
with up to 2nd derivatives
Analytic STO-3G, 3-21G, 4-21G, 4-31G, 6-21G, 6-31G,6-311G(3df, 3pd), 6-31G(TM), D95, D95V, MSV, (aug-)
cc-pV[D,T,Q]Z, d-aug-cc-pv[D,T]Z, t-aug-cc-pv[D,T]Z, (aug-)
cc-pVQZ-pp, MIDIX, TZV, def2-svp, def2-svpd, def2-tzvp,def2-tzvpp, def2-tzvppd, def2-tzvpd, def2-qzvp, def2qzvpd, def2-qzvpp, def2-qzvppd, NLO-V, ANO-VT, PolX
Pseudospectral 3-21G, 6-31G, 6-311G, 6-31G(TM), D95,(aug-)cc-pV[D,T,Q]Z, (aug-)cc-pVTZ-PP, MIDIX, def2-svpd,def2-tzvp, and def2-tzvpd families
Effective core potential (ECP) LAV1S, LAV2D, LAV2P,LAV3D, LAV3P, LACVD, LACVP, LACV3P, cc-pVTZ-pp, CSDZ,ERMLER2, LANL2DZ, LANL2TZ
Relativistic sarc-zora, dyall-v2z_zora-j-pt-gen, and dyall2zcvp_zora-j-pt-gen
Diffuse and/or polarization functions are available formost basis sets
Custom basis set and automatic conversion from Gaussianto Jaguar format
Automated counterpoise calculations

3References
1.

R. A. Friesner, R. B. Murphy, M. D. Beachy, M. N. Ringnalda, W. T. Pollard, B. D. Dunietz, Y. Cao, J. Phys. Chem.
A, 1999, 103, 1913.

2.

A. D. Bochevarov, E. Harder, T. F. Hughes, J. R. Greenwood, D. A. Braden, D. M. Philipp, D. Rinaldo, M. D.
Halls, J. Zhang, R. A. Friesner, Int. J. Quantum Chem., 2013, 113, 2110.

3.

L. D. Jacobson, A. D. Bochevarov, M. A. Watson, T. F. Hughes, D. Rinaldo, S. Ehrlich, T. B. Steinbrecher, S.
Vaitheeswaran, D. M. Philipp, M. D. Halls, R. A. Friesner, J. Chem. Theory Comput. 2017, 13, 11, 5780–5797.

4.

L. D. Jacobson, J. M. Stevenson, F. Ramezanghorbani, D. Ghoreishi, K. Leswing, E. D. Harder, R. Abel,
J. Chem. Theory Comput. 2022, 18, 4, 2354–2366.

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