воскресенье, 20 ноября 2016 г.

Integration of Chemcraft with programs such as Excel, Origin, etc.

  Sometimes users ask us to implement a feature like plotting multiple spectra (from different files) in a single graph. We do not plan to implement such features, because:
1) This will require a reconstruction of the user interface, and old users will have to learn how to use Chemcraft again;
2) For building graphs of any type, powerful software packages already exist – Excel, Origin, etc. We do not want to compete with them. But Chemcraft allows one to export any graph, spectrum, diagram in text format, copying its data to Excel via Clipboard. So, multiple spectra in one picture can be easily obtained as follows: you should open several output files in a row, copy the data with spectra to Excel and then combine these spectra in Excel. Note that the broadened spectrum (Doppler, Lorentzian broadening of bands) can be exported too.
  See, for example, this graph built in Chemcraft:

  This is a Gaussian09 Born-Oppenheimer Molecular Dynamics (BOMD) computation, and the graph shows the distance between atoms C1 and H1 versus time in femtoseconds. There seems to be a problem, or a bug, with visualization of such files – Chemcraft reads the first 3 points with zero time. We found the format of the output file quite difficult. Maybe this problem will be fixed in future, but we are not even sure that this is necessary: if you need to plot a good graph “Distance vs Time”, click on the “Copy” button, insert the data into Excel and manually delete the invalid lines. Of course you should check your graph against the output file – if you work with BOMD computations, you should understand them well enough.
  We always give our users the possibility to verify the data visualization in Chemcraft – for example, when you visualize the molecular orbitals, you can click the “Check orbitals” button to check whether Chemcraft extracted all orbitals correctly. When we implement visualization of MOS from output files of new software, it is sometimes difficult to thoroughly verify whether the MOS are visualized correctly (probably free software is characterized with such problems to a greater extent). And don’t forget to check the data shown by Chemcraft using the Source mode.

вторник, 21 июня 2016 г.

DFT advices

We have gathered some information from computational chemistry forums, which can be helpful for you. It should be noted that the following text cannot be considered as a professional guide; such guides will possibly appear in the future.

1. The choice of a DFT functional

First of all, you should read this manual:

  One of the thesises in this book is that the GGA functionals are ususally more universal (let’s say, closer to “Ab initio”) than the hybrid functionals (this statement, however, has some weak points). This also means that the errors with these functionals are more systematic: for example, the PBE functional usually overestimates the bond lengths and underestimates the vibrational frequencies. In our opinion, if you choose between, e.g., the PBE and B3LYP functionals, you should note that the latter should be more accurate for most organic molecules, but it should be less accurate in some problematic cases; so, the PBE functional is more reliable. Because of that, at one forum we found the following advice (written in 2010): always use the PBE functional and don’t worry. It is CGA, so it must be more universal than, e.g., the B3LYP functional.
  It is written in this manual that the B3LYP functional has shown good results for organic molecules, but it is worse for transition metal compounds and for large molecules. TPSSh probably is a good functional for transition metal compounds (according to this manual).
  The B3LYP functional is commonly used in chemistry, while the PBE, PBE0 functionals are commonly used in applications to extended systems (materials) [13].

Here is another compilation on the subject:

Here is a screenshot from this paper:

It is not clear from this list, whether the dispersion correction should be always used. However, at other forums we found the advices to use the dispersion correction always if possible. In Ref. [1] you can see that the ωB97X-D is the best single-component functional, while PBE0-D3 perform almost as well. Besides that, on the CCL list one can read that B3LYP-D3 is usually better than B3LYP.
Dispersion correction is the interaction of induced dipoles. This correction becomes important if two parallel benzene rings interact (stacking). So, the dispersion correction is important for computing such molecules as tetraphenylporphyrin, bilirubin, etc.
Here is a list of favorable and non-favorable DFT functionals from the DFT 2015 poll for computing particular properties:

  With all due respect to the creators of the above list, we must mention that we tried to compute the properties of bilirubin molecule (having intermolecular H-bonds) using the PBE, B3LYP and wB97XD functionals, and we found that the PBE functional is the worst at describing intermolecular H bonds (the PMR spectra computed using the PBE/6-311G(D,P) method are in poorer agreement with the experimental ones than the PMR spectra computed using the B3LYP/6-311G(D,P) or wB97XD/6-311G(D,P) methods). So, we found that the PBE functional is not good at describing H-bonds, in contrast to the conclusions drawn above. So, we think that you should not fully trust these tables.
Another post from CCL states the following:

 - Recommended GGA methods: revPBE-D3, B97-D3

 - Recommended meta-GGA methods: oTPSS-D3, TPSS-D3

 - Hybrid functionals: PW6B95-D3, M062X-D3

 - Double-hybrids are the most accurate DFT methods on the market: DSD-BLYP-D3, DSD-PBEP86-D3, PWPB95-D3
  In Ref. [2], a thorough energy benchmark study of various density functionals (DFs) was carried out. The authors write:
“In summary, we recommend on the GGA level the B97-D3 and revPBE-D3 functionals. The best meta-GGA is oTPSS-D3 although meta-GGAs represent in general no clear improvement compared to numerically simpler GGAs. Notably, the widely used B3LYP functional performs worse than the average of all tested hybrids and is also very sensitive to the application of dispersion corrections.”
“The ωB97X-D functional seems to be a promising method. The most robust hybrid is Zhao and Truhlar's PW6B95 functional in combination with DFT-D3”.
“If higher accuracy is required, double-hybrids should be applied. The corresponding DSD-BLYP-D3 and PWPB95-D3 variants are the most accurate and robust functionals of the entire study.”

The tests in this paper were performed on GMTKN30 set – this set covers mainly molecules containing main group elements, mostly organic (link).

  So, the double-hybrids seem to be the best DFT methods at the moment. This is illustrated by the following chart from the aforementioned paper:

Another advantage of PBE is that this functional is “cheap”.
Note that the PBE and PBE0 methods are quite different: PBE is a CGA, while PBE0 is a hybrid method. However, if one compares e.g.  BP86, BLYP, BPW91 functionals (GGA) with PBE0, he finds that PBE0 is "less semi-empirical".
  Here is another comparison of DFT functionals. In Ref. [3], a few DFT functionals were benchmarked for 14 compounds (calculation of vertical excitation energies by TDDFT and their comparison to experiment). Here are two pictures from this paper:

Ref. [4] reports that the CAM-B3LYP and BHandH functionals yield the best agreement between computed and experimental vertical absorption energies for a set of some simple organic molecules (involving first and second row atoms).

  The combination of these advices can confuse an inexperienced user. As for us, we decided that we should use PBE-D3 for inorganic molecules and ωB97X-D or B3LYP-D3 for organic ones, since we deal with the Gaussian09A package. Such an advice should be useful only for “amateurs” who are unable to gather more information.
  Anyway, it is better to use several functionals to ensure that they produce similar results. MP2 should not also be forgotten (SCS-MP2 seems to be better than conventional MP2, as written in the paper above; as far as we know, SOS-MP2 is better too).
  Recently, the B3LYP/6-31G(D,P) method has been quite popular. We think that using this method for computing organic molecules (not containing d and f elements) is still rather adequate, but the snobs can interpret the use of this method as the sign of amateurishness (at least, if you don’t employ different functionals and/or basis sets in the same study). See, for example, this and this posts on the CCL list.

  The flaws of this famous B3LYP/6-31G* model chemistry are discussed in Ref. [5]:
  The authors write that the relatively good performance of B3LYP/6-31G*, which made it so popular, is caused by a hidden error cancellation. The B3LYP-gCP-D3/6-31G* method, according to the authors, is much better (it removes the two major deficiencies: missing London dispersion effects and basis set superposition error). The B3LYP-D3/6-31G* method is slightly worse as it does not provide a BSSE elimination. This picture illustrates the aforesaid:

  As far as we know, the density fitting / RI (Resolution of the Identity) approximation is usually a good thing, as it speeds up your calculations without significant loss of accuracy (it least, this is written in Orca manual). However, in some cases it can lead to bad SCF convergence or give the error of 1-2 kcal/mol in energies.
  Here is a picture from Ref. [13] illustrating the availability of DFT functionals:

2. The choice of basis set

  As far as we know, at the moment the optimal basis sets for high-accuracy computations are Dunning family sets: cc-pVnZ, aug-cc-pVnZ, cc-pCVnZ, cc-pwCVnZ (n=2,3.4,5, etc). These basis sets are correlation consistent; this means, that they were optimized using correlated methods, unlike the 6-31G** basis sets. In Ref. [6] the following is stated:
  "One of the primary reasons for the cc basis set family’s lasting
popularity is due to a series of empirical observations that as
the cardinal number (n in cc-pVnZ) of the basis set is increased,
energies and various properties converge smoothly toward the
complete basis set (CBS) limit."
  The so-called complete basis set (CBS) limit means that you first compute with cc-pVDZ, then cc-pVTZ, then cc-pVQZ, then cc-pV5Z, etc., and the energy should converge to a hypothetical “complete” basis set limit. At the same time, there are more than 10 extrapolation schemes which give nearly the same result after performing only 2-3 computations (however, these extrapolation schemes are empirical to some extent).
  For heavy elements (Z>29), relativistic effects are strong and must be taken into account either using the methods like ZORA, DKH, or using effective core potentials (ECPs, PPs). The main relativistic effects include relativistic contraction and spin-orbit interaction. For many tasks, even such elements as Fe, Co, Ni do not require including relativistic effects in the computation (you will have a lot of problems besides relativism with these atoms).
  The Ref. [6] provides an overview of the development of Gaussian basis sets for molecular calculations, with a focus on four popular families of modern bases ("Gaussian basis set" means any basis set with Gaussian (not Slater) functions, not a specific set for the GAUSSIAN program). The authors write about the cases when using ECPs is not advisable (in particular, electron paramagnetic resonance), and it is written that using the DFT-based ZORA or DKH models with segmented all-electron relativistic contracted (SARC) basis sets produce good agreement with experiment and higher level ab initio computations.
  One interesting point is mentioned in Ref. [7]: the authors report that the computations with 6-311++G** basis set gave better molecular geometries than the more costly aug-cc-pVDZ (the methods used were MP2 and CCSD). In addition, the smaller 6-311++G** invariably leads to lower calculated total energies than aug-cc-pVDZ. So, it seems that the aug-cc-pVDZ can be worse than the 6-311++G** set (nevertheless, we suppose that if you need an expensive basis set or CBS (complete basis set) extrapolation, you should use cc-pVTZ, cc-pVQZ, cc-pV5Z, etc).

  Some people say that it is not actual to use basis sets larger than cc-pVTZ with DFT. However, in Ref. [14] the authors performed energy computations of 211 small first and second row compounds (mostly organic), and they concluded that the 5Z basis set (aug-cc-pV5Z) is required to get the MAE of atomization energies below 1 kcal/mol. See this blog for more information.
  The same is written at this handbook “Practical Advice for Quantum Chemistry Computations”:

  For some small organic molecules, we have found that the basis sets 6-31++G(D,P) and AUG-cc-pVDZ give almost identical results (protonation energies of 16 amide-containing molecules computed with wB97XD/6-31++G(D,P) and wB97XD/AUG-cc-pVDZ methods correlate with R= 0,99966; this difference is almost negligible for our applied tasks). In contrast to the results reported in the aforementioned paper, the total energies computed with wB97XD/AUG-cc-pVDZ method are 3-30 kJ/mol lower than the energies computed with wB97XD/6-31++G(D,P).
  At the same time, with the basis set AUG- cc-pVDZ the computation time was 3-6 times higher than with the 6-31++G(D,P) basis set. So, the 6-31++G** basis set should be still considered good enough.
  It is usually considered that the computation of anions or significantly electronegative atoms (which show big negative Mulliken charge) requires the use of diffuse functions (“++” for 6-31G or “aug” for cc-pVnZ). However, in the paper [8] this conclusion is criticized to a significant extent. The authors write:
“We conclude that the use of diffuse functions for calculating geometrical parameters for PAH anions in general is unnecessary and does not improve the calculated results significantly. Energy calculations are affected in much the same way.”.
  As the authors write, the only case when the diffuse functions are important are the computations of absolute values of chemical shifts; however, in most cases, when the experimental data are available, it is no necessary to obtain their absolute values as the correlations between the computed and experimental values can be built instead.
  On the other hand, D. Truhlar who investigated the use of diffuse functions writes here:
"How should one add diffuse functions to the basis set? Diffuse functions are known to be critical in describing the electron distribution of anions (as discussed in my book), but they are also quite important in describing weak interactions, like hydrogen bonds, and can be critical in evaluating activation barriers and other properties."
  The Truhlar group recommends using the "jun-" basis sets (see below).
  One more source of information is the review "Basis sets in quantum chemistry" by C. David. Sherill. The author writes in this review about the diffuse functions:

  Our knowledge of the subject and our personal experience says that the diffuse functions indeed should be used when calculating anions. We have computed the energies of deprotonation of 12 carbon acids (with PCM solvation model), both with diffuse functions and without them (wB97XD/6-31++G(D,P) method and the wB97XD/6-31G(D,P) method), and the values calculated by the first method correlate much better with experimental PKa values than the values computed without diffuse function (the correlation coefficients R are correspondingly 0,99522 for wB97XD/6-31++G(D,P) and 0,98884 for wB97XD/6-31G(D,P)).
Some recommendations concerning the choice of basis sets can be found on Orca input library:. These recommendations are:
 - Rule of thumb: Energies and geometries are usually fairly converged at the DFT level when using a balanced polarized triple-zeta basis set (such as def2-TZVP) while MP2 and other post-HF methods converge slower w.r.t. the basis set. Ab initio methods are much more basis set sensitive than DFT methods

 - Stick with one family of basis sets that is available for all the elements of your system. Mixing and matching basis sets from different families can lead to problems.

 - Calculations on heavy elements can either be performed using an all-electron approach or effective core potentials (ECPs).

Here is a picture from the Orca input library:

So, it seems that diffuse functions are really important for computing electron affinities.

  As far as we know, usually it is not needed to use a larger basis set than cc-pVTZ with DFT: further increasing basis set size will not improve the accuracy of the computation.  In contrast, this is not true for ab initio computations, which will benefit from using larger basis sets, such as cc-pVQZ, cc-pV5Z, etc.
  Some papers, in which the results of DFT computations are compared to those of ab initio methods and to the experimental data, conclude that DFT performs not worse (or even slightly better) [10, 11, 12]. This is caused by employing modest basis sets (not larger than cc-pVTZ) in these papers.
  So, the choice between DFT or ab initio methods depends on which properties are calculated and what accuracy is required.
  The larger the basis set, the more difficult the SCF convergence is (especially if diffuse-augmented basis sets are used). We recommend to always specify SCF=XQC in GAUSSIAN input files. With this keyword, the scf is firstly converged using the default DIIS algorithm, and if the convergence is not achieved, Gaussian switches to more reliable and costly quadratically convergent SCF procedure.
  Ref. [9] describes the role of diffuse functions in computations. It is known, that for many tasks using the diffuse functions will not lead to significant increase of computational accuracy, but will increase the cost of the calculation; besides that, using the diffuse functions can lead to SCF convergence problems and can increase the basis set superposition error (BSSE). The authors write: “We conclude that much current practice includes more diffuse functions than are needed. Often, better accuracy could be achieved if the additional cost were invested in higher-ζ basis set or more polarization functions.”
  The popular basis set family cc-pVnZ (of Dunning and co-workers) comprises the diffuse functions, if  “aug-” prefix is used. The authors notice that chemists usually utilize “fully augmented” basis sets, and this may not be optimal for large molecules. For example, the cc-pVTZ basis set for methane has s, p, d, and f functions on C and s, p, and d functions on H; aug-cc-pVTZ contains diffuse s, p, d, and f functions on C and diffuse s, p, and d functions on H atoms.
  In contrast, the earlier “plus” basis sets originally systematized by Pople and co-workers contained only diffuse s and p functions on non-hydrogen atoms and no diffuse functions on hydrogen atoms. In Ref. [9] this is called “minimal augmentation”. The maug-cc-pVTZ basis set retains the diffuse s and p functions on carbon with the exponential parameters optimized for the aug case but deletes all other diffuse functions.
  So, the authors (Truhlar et al.) conclude that using the minimal augmentation is usually more optimal than using the full augmentation (particularly with DFT). The authors recommend the so-called “calendar” basis sets, in particular the “jun” level of augmentation – for example, the jun-cc-pVTZ set is recommended in comparison to aug-cc-pVDZ or cc-pVTZ. When increasing the zeta number in Dunning basis sets (i.e. switching from cc-pVDZ to cc-pVTZ, then to cc-pVQZ, etc), augmentation becomes less important, and using the “calendar” basis sets provides a more efficient sequence of basis sets (than unaugmented, minimally augmented, or fully augmented sets) for basis set extrapolation to the complete basis set limit. We know, however, that many researchers have criticized the approach proposed by the authors.

3. DFT quackery

  Anyway, density functional theory is a “black box”. Look at this picture from Ref. [13]:

  Our comment on this picture:
  First and second points: In contrast to ab initio methods, DFT is not hierarchical. Ab initio (non-empirical) methods are hierarchical: this means that if we increase basis set size, level of taking into account the electronic correlation (excitation rank), and possibly the level of taking into account the relativistic effects (for heavy elements), we approach the exact solution (within the Born–Oppenheimer approximation). More specifically, if we go, e.g., through CCSD/cc-pVDZ ->  CCSDT/cc-pVDZ ->  CCSDTQ/cc-pVDZ -> CCSDTQ5/cc-pVDZ, etc., the results of the computation systematically approach some limit; if we go through CCSD/cc-pVDZ ->  CCSD/cc-pVTZ -> CCSD/cc-pVQZ -> CCSD/cc-pV5Z -> CCSD/cc-pV6Z, etc., the results systematically approach the complete basis set (CBS) limit. For the first row, the improvement can be non-monotonic, while for the second case the improvement seems to be always monotonic.
  So, we can verify the accuracy of an ab initio method by comparing its results with the results of a higher level computation. For DFT, this possibility is much less available.
  The points mentioned below are mostly our private opinion, maybe not fully right.
  As far as we know, DFT is often used to “confirm” an experiment. This means that if the experiment and a DFT computation lead to similar conclusions, this increases the reliability of the investigation. On the contrary, if the experiment and the DFT calculation give different results, this can be either a discovery or a failure (inaccuracy of the computation, or maybe the experiment).
  Speaking of “confirming” an experiment, it should be noted that this approach is only good with an independent experiment. We know some cases when the experiment was “adjusted” for better agreement with the computation (both at DFT and ab initio levels).
  As mentioned above, it is a good practice to perform the computation with several different DFT functionals, to ensure that they all give the same results. And as far as we know, some researchers, being not honest enough, meaningly avoid using more than one functional, because if different functionals give contradictory results in their work, this makes this whole work less “publishable”. 

  Here you can read an ironical essay “Obituary : Density Functional Theory. 1927-1993”:

  The author claims that the density functional theory in current implementation is not a mathematically correct approach:

  “The Hohenberg-Kohn argument is what mathematicians call an existence proof, as opposed to a constructive proof. That is, although we now know that, in theory, DFT can extract as much information from r(r) as her brother can from  Y  ( r 1,  r 2, ... ,  r n) , no-one knew how to dress her so that she could achieve this in practice. All quantum mechanical theories are created equal, but some are more equal than others.”

  The hybrid functionals, which appeared in 1993, are even more unreliable and not correct from the theoretical point of view; in other words, using such functionals may be a kind of “shamanism”, or maybe even “scientific charlatanism”. The author thinks that the density functional theory finally died (we should add, it died as a well-grounded scientific theory) in 1993, after the spreading of hybrid functionals.

  On the other hand, in Ref. [13] the author states the following:

"I believe that a fundamental principle underlies the success 
of DFT, which is that local approximations are a peculiar type of semiclassical approximation to the many-electron problem. For the last 6 years, with both my group and many collaborators, I have been trying to uncover this connection, and make use of it. The underlying math is very challenging, and some must be invented."

  The "DFT shamanism" can exist in the following form: if different functionals are applied to the same object, the user may select any results consistent with experimental data (even the latter are invalid or erroneous) and explain them. We suggest calling such practive "DFT quackery".

  In Ref. [13] the following is proposed: "Users should stick to the standard functionals (as most do, according to Fig. 1), or explain very carefully why not."

4. DFT future

  Here is a picture from Ref. [13]:
  A fragment of the paper [13]:


  So, where does this leave us? It is clearly both the best and worst of times for DFT. More calculations, both good and bad, are being performed than ever. One of the most frequently asked questions of developers of traditional approaches to electronic structure is: “When will DFT go away?.” Judging from Fig. 1, the answer is clearly no time soon. Although based on exact theorems, as shown in Fig. 2, these theorems give no simple prescription for constructing approximations. This leads to the many frustrations of the now manifold users listed in Table I.Without such guidance, the swarm of available approximations of Fig. 3 will continue to evolve and reproduce, perhaps ultimately undermining the entire field. But I expect that some of the many excellent ideas being developed by the DFT community will come to fruition, i.e., produce new and more general standard approximations, well before that happens."


[3] S.S.Leang, F.Zahariev, M.S.Gordon, J.Chem.Phys.,  136, 104101 (2012)

[4] G.Garcı´a, C.Adamo, I.Ciofini, Phys. Chem. Chem. Phys., 2013, 15, 20210--20219

[8] Calculations of PAH anions: When are diffuse functions necessary? Noach Treitel1, Roy Shenhar, Ivan Aprahamian, Tuvia Sheradsky and Mordecai Rabinovitz. P h y s . C h e m . C h e m . P h y s . , 2 0 0 4 , 6 , 1 1 1 3 – 1 1 2 1

[10] Do Practical Standard Coupled Cluster Calculations
Agree Better than Kohn–Sham Calculations with
Currently Available Functionals When Compared
to the Best Available Experimental Da...
Article in Journal of Chemical Theory and Computation · May 2015
Impact Factor: 5.5 · DOI: 10.1021/acs.jctc.5b00081

[11] On the dissociation energy of Ti(OH,)+.

An MCSCF, CCSD(T), and DFT study
A. Irigoras, J.M. Ugalde, X. Lopez, and C. Sarasola
Can. J. Chem. 74: 1824-1829 (1996). Printed in Canada / Imprimt au Canada

[13] J. Chem. Phys. 136, 150901 (2012). Perspective on density functional theory. Kieron Burke.

Jensen, Stig Rune; Saha, Santanu; Flores-Livas, José Abdenago; Huhn, William; Blum, Volker; Goedecker, Stefan; Frediani, Luca, 2017, "GGA-PBE and hybrid-PBE0 energies and dipole moments with MRChem, FHI-aims, NWChem and ELK", doi:10.18710/0EM0EL, UiT Open Research Data Dataverse, V3

пятница, 1 апреля 2016 г.

Some useful links for people dealing with quantum chemistry

At this page we are collecting links we find useful in our own research work.

1) Gaussian Error Messages

Here 26 types of Gaussian error messages are explained (e.g., memory allocation problems, change in point group or standard orientation, convergence failure, etc.).

2) Practical Advice for Quantum Chemistry Computations, by C. David Sherrill.

Here many simple hints are provided:
 - The choice of basis set;
 - The choice of ab initio/DFT methods for different tasks.
The author writes that standard HF, semiempirical, and DFT techniques are not appropriate for van der Waals complexes or systems dominated by London dispersion forces. However, Ref. reported that for the compounds under investigation the B3LYP-gCP-D3/6-31G* method could correctly compute London dispersion effects (and it does not have the basis set superposition error);
 - Hints on the problem of SCF convergence;
 - Common hints on imaginary frequencies and saddle points;

 - Hints on optimization with symmetry.

суббота, 13 февраля 2016 г.

Why we haven’t yet implemented a GUI for creating input files in Chemcraft

Sometimes the Chemcraft users ask me to implement a GUI for creating input files. GaussView has such a GUI.
I don’t plan to implement such a GUI in Chemcraft, because I never had the need to use it in my research work. To create input files for new jobs, I use my archive of computed jobs; when I need to create a new input file, I take an input file from this archive and modify it.
You can download such an archive here:

This archive contains more than 150 jobs (both input and output files) with different types of computation. Besides these files, the archive contains text files with comments. It also contains some examples of interesting computations with comments, which illustrate the use of quantum chemistry.
This archive is protected with password; to get the password, you need to purchase Chemcraft (the password will be shown in the “Help/License information” window).
I think that implementing a GUI for creating input files can be even harmful, because it will give you a false feeling of easy creation of new jobs; but when you need to create a non-standard job (e.g., a PES scan), this GUI will become useless.
I wrote this statement in our Facebook group, and two persons fully agreed with my conclusion (none disagreed).

суббота, 12 декабря 2015 г.

Correlations between computed and experimental properties

When the results of quantum chemistry computations are directly compared with experiment, often quite poor agreement is obtained. However, this agreement often becomes much better, if a series of homologues is taken and a correlation is built between the computed and experimental values. Here I present some samples of such correlations:

1)      Correlation between QC (T1(1)) and experimental heat of formation for a set of 1800 diverse organic molecules from NIST thermochemical database (from Wikipedia):

The mean absolute and RMS errors are 8.5 and 11.5 kJ/mol, respectively.

2) Correlation between computed and experimental NMR spectra.
We have found that usually the computed NMR chemical shifts are rather far from the experimental ones; maybe the physical meaning of the chemical shifts is not clear enough. At the same time, the computed values correlate well with the experimental ones
a) Such correlation for several Shiff bases:

The calculations were performed with B3LYP/6-311G(D,P) method, but the B3LYP functional is considered obsolete at the moment. You should better use PBE, wB97XD or B3LYP-D3 instead. 6-311 G is not a good basis set too, you should better use, e.g., cc-pVDZ.

b) A bodipy molecule:

A similar correlation can be obtained for H1 chemical shifts (several bodipy molecules):
I have found that a good correlation is usually obtained for C13 chemical shifts; for H1 chemical shifts the correlation is rather good too, but only for hydrogen atoms attached to carbons.

c) One more correlation from this site:


3) An important correlation can be obtained between computed energies, or maybe Gibbs energies, and experimental reaction constants.

a) Here is one such correlation for a set of 9 carboxylic acids:

Computed energies of deprotonation vs experimental pKa

The energies were computed at PCM wB97XD/6-311++G(DP) level (the solvent is water), with two water molecules added to the model for taking into account the specific solvation.

The correlation between QC energies and experimental reaction constants is based on the assumption that the entropy contribution to the Gibbs energy within a row of compounds is small relative to the energy contribution (if not, one can compute the Gibbs energies as well).

The difference between  and  within a row of homologues is relatively small, so

b) Some more correlations between the energy and PKa for 5 sets of compounds, taken from paper [1]:

The theoretical constants were calculated from the Gibbs energies of deprotonation computed at PCM B3LYP/6-311+G(d,p) level (the solvent was DMSO).

4) A similar correlation can be obtained between the computed energies and experimental rate constants. Here are some samples from our work:
I have studied the reaction of C-Cl bond elimination of some anion radicals:

The computed energies of the C-Cl bonds correlate with the experimental rate constants of the bond elimination reaction:

I have studied the reaction of substitution of alcohol ligands by imidazole using QC and experimental methods:

Apart from methanol, the data for other alcohols were also obtained:

The alcohol substitution was studied using electronic spectra. The experiment showed that this reaction has two stages, each stage having its own rate constant. Then I computed the energies Cr-Alcohol, and plotted the graph “Computed energies vs. experimental constants of correlation (first stage)”:

Note that in both cases no solvation modeling was used; this means that if we investigate the tendency within a series of homologues, the accuracy of a simple quantum chemistry computation (gas phase modeling) is sufficient to make important conclusions.

5) We have found a correlation between the computed and experimental components of polarizability tensor for some Shiff bases:

6) For computation of electronic spectra using the TDDFT method, the correlation seems to be worse:
a) [2]:
b) [3]:

c) [4]:

d) [7]

FIG. 7. Accuracy plots for TDDFT calculated excitation energies for metaGGAs: (a) VS98, (b) PKZB, (c) TPSS, (d) M06-L, (e) TPSSm, (f) revTPSS, (g) TPSSh, (h) M05, (j) M06, (k) M06-2X, (l) M06-HF, (m) M08-HX, and (n) M08-SO. Points above the line indicate positive errors while points below the line indicate negative errors.

e) [8]

f) For some Bodipy molecules, the correlation seems to be better []:

7) Here is the correlation between computed and experimental collision diameters of some molecules (mostly organic):

The experimental collision diameters were taken from [5,6]. The computed ones were obtained with Chemcraft: firstly the molecular geometries were obtained at B3LYP/6-31G(D,P) level (again, we don’t recommend using this method, we just don’t want to repeat the jobs with another level of theory), and then the diameters were computed by Chemcraft via “Tools/Calculate collision diameters” menu item.

8) The following graph should not be called a “correlation”, but it illustrates some practical use of quantum chemistry. The X values correspond to the anisotropy of molecular polarizability of some nematic liquid crystals (Shiff bases), the Y values correspond to the temperatures of phase transitions (nematic-isotrope). The first ones were computed with DFT, the latter (experimental values) were taken from literature:

  The symbols above the points represent fragments of molecules which vary among series.
  This correlation is so poor not because the computation give wrong results; as it can be seen above, the computed components of molecular polarizability correlate well with experimental ones. So, the reason of such bad correlation is the imperfection of this approach (that the thermal stability of liquid crystal depends on their molecular polarizability).

9) Vibrational frequencies
   We have computed the vibrational spectra of some simple organic compounds (toluene, propene, acetone, benzoic acid, etc) using wB97XD/aug-cc-pVTZ method, and compared the mode frequencies with the experimental values:

  The blue line indicates full theory/experiment match. Some improper attribution of the bands is possible.
  We have also computed the same frequencies at a lower level (wB97XD/6-31G(D,P)). The standard deviation turned out to be 29 cm-1 for wB97XD/6-31G(D,P) and 24 cm-1 for wB97XD/aug-cc-pVTZ. The job CPU time with the latter method was 50-100 times higher than with the former method.


[1] Sergey L. Khursana and Mikhail Yu. Ovchinnikova. The pKa theoretical estimation of C―H, N―H, O―H and S―H acids in dimethylsulfoxide solution. Journal of physical organic chemistry, 9/24/2014, DOI 10.1002/poc.3371.
[2] Nesrin Tokay, Zeynel Seferoğlu, Cemil Öğretir and Nermin Ertan. Quantum chemical studies on the structures of some heterocyclic azo disperse dyes.ARKIVOC 2008 (xv) 9-20
[3] S. Kawauchi, L. Antonov, Y. Okuno. Prediction of the color of dyes by using time-dependent density functional theory (TD-DFT). Bulgarian Chemical Communications, Volume 46, Special Issue A (pp. 228 – 237) 2014.
[4] Denis Jacquemin, Eric A. Perpe`te, Gustavo E. Scuseria, Ilaria Ciofini, and Carlo Adamo. TD-DFT Performance for the Visible Absorption Spectra of Organic Dyes: Conventional versus Long-Range Hybrids. J. Chem. Theory Comput. 2008, 4, 123-135
[5] H. Wang, M. Frenklach, Combust. Flame 96, 163 (1994)
[6] R.J. Kee et al.Chemkin Collection, Release 3.6, Reaction design, Inc., San Diego, CA (2000)
[7]Sarom S. Leang, Federico Zahariev, and Mark S. Gordon.THE JOURNAL OF CHEMICAL PHYSICS 136, 104101 (2012).
[8] K. Okuno et al. J.Photochem.Photobiol A: Chemistry 235 (2012) 29– 34
[9] J. Chem. Theory Comput. 2014, 10, 4574−4582