# Category Archives: Tricks

## Fixing the error: Bad data into FinFrg

I found this error in the calculation of two interacting fragments, both with unpaired electrons. So, two radicals interact at a certain distance and the full system is deemed as a singlet, therefore the unpaired electron on each fragment have opposite spins. The problem came when trying to calculate the Basis Set Superposition Error (BSSE) because in the Counterpoise method you need to assign a charge and multiplicity to each fragment, however it’s not obvious how to assign opposite spins.

The core of the problem is related to the *guess *construction; normally a Counterpoise calculation would look like the following example:

#p B3LYP/6-31G(d,p) counterpoise=2 -2,1 -1,2 -1,2 C(Fragment=1) 0.00 0.00 0.00 O(Fragment=2) 1.00 1.00 1.00 ...

In which the first pair of charge-multiplicity numbers correspond to the whole molecule and the following to those of each fragment in increasing order of *N* (in this case, *N* = 2). So for this hypothetical example we have two anions (but could easily be two cations) each with an unpaired electron, yielding a complex of charge = -2 and a singlet multiplicity which implies those two unpaired electrons have opposite spin. But if the *guess *(the initial trial wavefunction from which the SCF will begin) has a problem understanding this then the title error shows up:

Bad data into FinFrg Error termination via Lnk1e ...

The solution to this problem is as simple as it may be obscure: Create a convenient guess wavefunction by placing a negative sign to the multiplicity of one of the fragments in the following example. You may then use the guess as the starting point of other calculations since it will be stored in the checkpoint file. By using this negative sign we’re not requesting a *negative multiplicity*, but a given multiplicity of *opposite spin *to the other fragment.

#p B3LYP/6-31G(d,p) guess=(only,fragment=2) -2,1 -1,2 -1,-2 C(Fragment=1) 0.00 0.00 0.00 O(Fragment=2) 1.00 1.00 1.00 ...

This way, the second fragment will have the opposite spin (but the same *multiplicity*) as the first fragment. The only keyword tells gaussian to only calculate the guess wave function and then exit the program. You may then use that guess as the starting point for other calculations such as my failed Counterpoise one.

## Density Keyword in Excited State Calculations with Gaussian

I have written about extracting information from excited state calculations but an important consideration when analyzing the results is the proper use of the keyword *density*.

This keyword let’s Gaussian know which density is to be used in calculating some results. An important property to be calculated when dealing with excited states is the change in dipole moment between the ground state and any given state. The Transition Dipole Moment is an important quantity that allows us to predict whether any given electronic transition will be allowed or not. A change in the dipole moment (i.e. non-zero) of a molecule during an electronic transition helps us characterize said transition.

Say you perform a TD-DFT calculation without the *density* keyword, the default will provide results on the lowest excited state from all the requested states, which may or may not be the state of interest to the transition of interest; you may be interested in the dipole moment of all your excited states.

Three separate calculations would be required to calculate the change of dipole moment upon an electronic transition:

1) A regular DFT for the ground state as a reference

2) TD-DFT, to calculate the electronic transitions; request as many states as you need/want, analyze it and from there you can see which transition is the most important.

3) Request the density of the Nth state of interest to be recovered from the checkpoint file with the following route section:

# TD(Read,Root=N)LOTDensity=Current Guess=Read Geom=AllCheck

replace *N* for the *N*th state which caught your eye in step number 2) and *LOT* for the *Level of Theory* you’ve been using in the previous steps. That should give you the dipole moment for the structure of the *N*th excited state and you can compare it with the one in the ground state calculated in 1). Again, if density=current is not used, only properties of *N*=1 will be printed.

## Natural Transition Orbitals (NTOs) Gaussian

The canonical molecular orbital depiction of an electronic transition is often a messy business in terms of a ‘*chemical*‘ interpretation of ‘*which electrons*‘ go from ‘*which occupied orbitals*‘ to ‘*which virtual orbitals*‘.

**Natural Transition Orbitals** provide a more intuitive picture of the orbitals, whether mixed or not, involved in any *hole-particle *excitation. This transformation is particularly useful when working with the excited states of molecules with extensively delocalized chromophores or multiple chromophoric sites. The elegance of the NTO method relies on its simplicity: separate unitary transformations are performed on the *occupied *and on the *virtual *set of orbitals in order to get a localized picture of the *transition density matrix*.

[1] R. L. Martin, *J. Chem. Phys*., 2003, DOI:10.1063/1.1558471.

In Gaussian09:

After running a TD-DFT calculation with the keyword TD(Nstates=n) (where n = number of states to be requested) we need to take that result and launch a new calculation for the NTOs but lets take it one step at a time. As an example here’s phenylalanine which was already optimized to a minimum at the B3LYP/6-31G(*d*,*p*) level of theory. If we take that geometry and launch a new calculation with the TD(Nstates=40) in the route section we obtain the UV-Vis spectra and the output looks like this (only the first three states are shown):

Excitation energies and oscillator strengths: Excited State 1: Singlet-A 5.3875 eV 230.13 nm f=0.0015 <S**2>=0.000 42 -> 46 0.17123 42 -> 47 0.12277 43 -> 46 -0.40383 44 -> 45 0.50838 44 -> 47 0.11008 This state for optimization and/or second-order correction. Total Energy, E(TD-HF/TD-KS) = -554.614073682 Copying the excited state density for this state as the 1-particle RhoCI density. Excited State 2: Singlet-A 5.5137 eV 224.86 nm f=0.0138 <S**2>=0.000 41 -> 45 -0.20800 41 -> 47 0.24015 42 -> 45 0.32656 42 -> 46 0.10906 42 -> 47 -0.24401 43 -> 45 0.20598 43 -> 47 -0.14839 44 -> 45 -0.15344 44 -> 47 0.34182 Excited State 3: Singlet-A 5.9254 eV 209.24 nm f=0.0042 <S**2>=0.000 41 -> 45 0.11844 41 -> 47 -0.12539 42 -> 45 -0.10401 42 -> 47 0.16068 43 -> 45 -0.27532 43 -> 46 -0.11640 43 -> 47 0.16780 44 -> 45 -0.18555 44 -> 46 -0.29184 44 -> 47 0.43124

The oscillator strength is listed on each Excited State as “f” and it is a measure of the probability of that excitation to occur. If we look at the third one for this phenylalanine we see f=0.0042, a very low probability, but aside from that the following list shows what orbital transitions compose that excitation and with what energy, so the first line indicates a transition from orbital 41 (HOMO-3) to orbital 45 (LUMO); there are 10 such transitions composing that excitation, visualizing them all with canonical orbitals is not an intuitive picture, so lets try the NTO approach, we’re going to take excitation #10 for phenylalanine as an example just because it has a higher oscillation strength:

%chk=Excited State 10: Singlet-A 7.1048 eV 174.51 nm f=0.3651 <S**2>=0.000 41 -> 45 0.35347 41 -> 47 0.34685 42 -> 45 0.10215 42 -> 46 0.17248 42 -> 47 0.13523 43 -> 45 -0.26596 43 -> 47 -0.22995 44 -> 46 0.23277

Each set of NTOs for each transition must be calculated separately. First, copy you *filename.chk* file from the TD-DFT result to a new one and name it after the Nth state of interest as shown below (state 10 in this case). **NOTE**: In the route section, replace N with the number of the excitation of interest according to the results in * filename.log*. Run separately for each transition your interested in:

#chk=state10.chk #p B3LYP/6-31G(d,p) Geom=AllCheck Guess=(Read,Only) Density=(Check,Transition=N) Pop=(Minimal,NTO,SaveNTO) 0 1 --blank line--

By requesting SaveNTO, the canonical orbitals in the state10.chk file are replaced with the NTOs for the 10th excitation, this makes it easier to plot since most visualizers just plot whatever set of orbitals they read in the chk file but if they find the canonical MOs then one would need to do some re-processing of them. This is much more straightforward.

Now we format our chk files into fchk with the formchk utility:

formchk -3 filename.chk filename.fchk

formchk -3 state10.chk state10.fchk

If we open filename.fchk (the file where the original TD-DFT calculation is located) with GaussView we can plot all orbitals involved in excited state number ten, those would be seven orbitals from 41 (HOMO-3) to 47 (LUMO+2) as shown in figure 1.

If we now open state10.fchk we see that the numbers at the side of the orbitals are not their energy but their occupation number particular to this state of interest, so we only need to plot those with highest occupations, in our example those are orbitals 44 and 45 (HOMO and LUMO) which have occupations = 0.81186; you may include 43 and 46 (HOMO-1 and LUMO+1, respectively) for a much more complete description (occupations = 0.18223) but we’re still dealing with 4 orbitals instead of 7.

The NTO transition 44 -> 45 is far easier to conceptualize than all the 10 combinations given in the canonical basis from the direct TD-DFT calculation. TD-DFT provides us with the correct transitions, NTOs just paint us a picture more readily available to the chemist mindset.

**NOTE**: for G09 revC and above, the %OldChk option is available, I haven’t personally tried it but using it to specify where the excitations are located and then write the NTOs of interest into a new chk file in the following way, thus eliminating the need of copying the original chk file for each state:

%OldChk=filename.chk

%chk=stateN.chk

NTOs are based on the Natural Hybrid orbitals vision by Löwdin and others, and it is said to be so straightforward that it has been re-discovered from time to time. Be that as it may, the NTO visualization provides a much clearer vision of the excitations occurring during a TD calculation.

Thanks for reading, stay home and stay safe during these harsh days everyone. Please share, rate and comment this and other posts.

## Using PDB files for Electronic Structure Calculations

#### Quick Post on preparing Gaussian input files from PDB files.

If you’re modeling biological systems chances are that, more often than not, you start by retrieving a PDB file. The Protein Data Bank is a repository for all things biochemistry – from oligo-peptides to full DNA sequences with over 140,000 available files encoding the corresponding structure obtained by various experimental means ranging from X-Ray diffraction, NMR and more recently, Cryo Electron Microscopy (CEM).

The PDB file encodes the Cartesian coordinates for each atom present in the structure as well as their in the same way molecular dynamics codes -like AMBER or GROMACS- code the parameters for a force field; this makes the PDB a natural input file for MD.

There are however some considerations to have in mind for when you need to use these coordinates in electronic structure calculations. Personally I give it a pass with OpenBabel to add (or possibly just re-add) all Hydrogen atoms with the following instruction:

$>obabel -ipdb filename.pdb -ogjf -Ofilename.gjf -hAlternatively, you can select a pH value, say 7.5 with:

$>obabel -ipdb filename.pdb -ogjf -Ofilename.gjf -h -p7.5

You may also use the GUI if by any chance you’re working in Windows:

This sends all H atoms to the end of the atoms list. Usually for us the next step is to optimize their positions with a partial optimization at a low level of theory for which you need to use the ReadOptimize ReadOpt or RdOpt in the route section and then add the atom list at the end of the input file:

Atomic coordinates

--blank line--

noatoms atoms=H

--blank line--

Finally, visual inspection of your input structure is always helpful to find any meaningful errors, remember that PDB files come from experimental measurements which are not free of problems.

As usual thanks for reading, commenting, and sharing.

## Calculating NMR shifts – Short and Long Ways

Nuclear Magnetic Resonance is a most powerful tool for elucidating the structure of diamagnetic compounds, which makes it practically universal for the study of organic chemistry, therefore the calculation of ^{1}H and ^{13}C chemical shifts, as well as coupling constants, is extremely helpful in the assignment of measured signals on a spectrum to an actual functional group.

Several packages offer an additive (group contribution) empirical approach to the calculation of chemical shifts (ChemDraw, Isis, ChemSketch, etc.) but they are usually only partially accurate for the simplest molecules and no insight is provided for the more interesting effects of long distance interactions (*vide infra*) so quantum mechanical calculations are really the way to go.

With Gaussian the calculation is fairly simple just use the NMR keyword in the route section in order to calculate the NMR shielding tensors for relevant nuclei. Bear in mind that an optimized structure with a large basis set is required in order to get the best results, also the use of an implicit solvation model goes a long way. The output displays the value of the total isotropic magnetic shielding for each nucleus in ppm (image taken from the Gaussian website):

Magnetic shielding (ppm): 1 C Isotropic = 57.7345 Anisotropy = 194.4092 XX= 48.4143 YX= .0000 ZX= .0000 XY= .0000 YY= -62.5514 ZY= .0000 XZ= .0000 YZ= .0000 ZZ= 187.3406 2 H Isotropic = 23.9397 Anisotropy = 5.2745 XX= 27.3287 YX= .0000 ZX= .0000 XY= .0000 YY= 24.0670 ZY= .0000 XZ= .0000 YZ= .0000 ZZ= 20.4233

Now, here is why this is the long way; in order for these values to be meaningful they need to be contrasted with a reference, which experimentally for ^{1}H and ^{13}C is tetramethylsilane, TMS. This means you have to perform the same calculation for TMS at -preferably- the same level of theory used for the sample and substract the corresponding values for either H or C accordingly. Only then the chemical shifts will read as something we can all remember from basic analytical chemistry class.

GaussView 6.0 provides a shortcut; open the Results menu, select NMR and in the new window there is a dropdown menu for selecting the nucleus and a second menu for selecting a reference. In the case of hydrogen the available references are TMS calculated with the HF and B3LYP methods. The SCF – GIAO plot will show the assignments to each atom, the integration simulation and a reference curve if desired.

The chemical shifts obtained this far will be a good approximation and will allow you to assign any peaks in any given spectrum but still not be completely accurate though. The reasons behind the numerical deviations from calculated and experimental values are many, from the chosen method to solvent interactions or basis set limitations, scaling factors are needed; that’s when you can ask the Cheshire Cat which way to go

If you don’t know where you are going any road will get you there.

Lewis Carroll – Alice in Wonderland

Well, not really. The Chemical Shift Repository for computed NMR scaling factors, with Coupling Constants Added Too (aka CHESHIRE CCAT) provides with straight directions on how to correct your computed NMR chemical shifts according to the level of theory without the need to calculate the NMR shielding tensor for the reference compound (usually TMS as pointed out earlier). In a nutshell, the group of Prof. Dean Tantillo (UC Davis) has collected a large number of isotropic magnetic shielding values and plotted them against experimental chemical shifts. Just go to their scaling factors page and check all their linear regressions and use the values that more closely approach to your needs, there are also all kinds of scripts and spreadsheets to make your job even easier. Of course, if you make use of their website **don’t forget** to give the proper credit by including these references in your paper.

We’ve recently published an interesting study in which the 1H – 19F coupling constants were calculated via the long way (I was just recently made aware of CHESHIRE CCAT by Dr. Jacinto Sandoval who knows all kinds of web resources for computational chemistry calculations) as well as their conformational dependence for some substituted 2-aza-carbazoles (fig. 1).

The paper is published in the Journal of Molecular Structure. In this study we used the GIAO NMR computations to assign the peaks on an otherwise cluttered spectrum in which the signals were overlapping due to conformational variations arising from the rotation of the C-C bond which re-orients the F atoms in the fluorophenyl grou from the H atom in the carbazole. After the calculations and the scans were made assigning the peaks became a straightforward task even without the use of scaling factors. We are now expanding these calculations to more complex systems and will contrast both methods in this space. Stay tuned.

## Post Calculation Addition of Empirical Dispersion – Fixing interaction energies

Calculation of interaction energies is one of those things people are more concerned with and is also something mostly done wrong. The so called ‘*gold standard*‘ according to Pavel Hobza for calculating supramolecular interaction energies is the CCSD(T)/CBS level of theory, which is highly impractical for most cases beyond 50 or so light atoms. Basis set extrapolation methods and inclusion of electronic correlation with MP2 methods yield excellent results but they are not nonetheless almost as time consuming as CC. DFT methods in general are terrible and still are the most widely used tools for electronic structure calculations due to their competitive computing times and the wide availability of schemes for including terms which help describe various kinds of interactions. The most important ingredients needed to get a decent to good interaction energies values calculated with DFT methods are correlation and dispersion. The first part can be recreated by a good correlation functional and the use of empirical dispersion takes care of the latter shortcoming, dramatically improving the results for interaction energies even for lousy functionals such as the infamous B3LYP. The results still wont be of benchmark quality but still the deviations from the *gold standard* will be shortened significantly, thus becoming more quantitatively reliable.

There is an online tool for calculating and adding the empirical dispersion from Grimme’s group to a calculation which originally lacked it. In the link below you can upload your calculation, select the basis set and functionals employed originally in it, the desired damping model and you get in return the corrected energy through a geometrical-Counterpoise correction and Grimme’s empirical dispersion function, D3, of which I have previously written here.

The gCP-D3 Webservice is located at: http://wwwtc.thch.uni-bonn.de/

The platform is entirely straightforward to use and it works with xyz, turbomole, orca and gaussian output files. The concept is very simple, a both gCP and D3 contributions are computed in the selected basis set and added to the uncorrected DFT (or HF) energy (eq. 1)

(**1**)

If you’re trying to calculate interaction energies, remember to perform these corrections for every component in your supramolecular assembly (eq. 2)

(**2**)

Here’s a screen capture of the outcome after uploading a G09 log file for the simplest of options B3LYP/6-31G(*d*), a decomposed energy is shown at the left while a 3D interactive Jmol rendering of your molecule is shown at the right. Also, various links to the literature explaining the details of these calculations are available in the top menu.

I’m currently writing a book chapter on methods for calculating ineraction energies so expect many more posts like this. A special mention to Dr. Jacinto Sandoval, who is working with us as a postdoc researcher, for bringing this platform to my attention, I was apparently living under a rock.

## fchk file errors (Gaussian) Missing or bad Data: RBond

We’ve covered some common errors when dealing with formatted checkpoint files (*.fchk) generated from Gaussian, specially when analyzed with the associated GaussView program. (see here and here for previous posts on the matter.)

Prof. Neal Zondlo from the University of Delaware kindly shared this solution with us when the following message shows up:

CConnectionGFCHK::Parse_GFCHK() Missing or bad data: Rbond Line Number 1234

The Rbond label has to do with the connectivity displayed by the visualizer and can be overridden by close examination of the input file. In the example provided by Prof. Zondlo he found the following line in the connectivity matrix of the input file:

2 9 0.0

which indicates a zero bond order between atoms 2 and 9, possibly due to their proximity. He changed the line to simply

2

So editing the connectivity of your atoms in the input can help preventing the Rbond message.

I hope this helps someone else.

## Quick note on WFN(X) files and MP2 calculations #G09 #CompChem

A few weeks back we wrote about using WFN(X) files with MultiWFN in order to find σ-holes in halogen atoms by calculating the maximum potential on a given surface. We later found out that using a chk file to generate a wfn(x) file using the guess=(read,only) keyword didn’t retrieve the MP2 wavefunction but rather the HF wavefunction! Luckily we realized this problem very quickly and were able to fix it. We tried to generate the wfn(x) file with the following keywords at the route section

#p guess=(read,only) density=current

but we kept retrieving the HF values, which we noticed by running the corresponding HF calculation and noticing that every value extracted from the WFN file was exactly the same.

So, if you want a WFN(X) file for post processing an MP2 (or any other post-HartreFock calculation for that matter) ask for it from the beginning of your calculation in the same job. I still don’t know how to work around this or but will be happy to report it whenever I do.

PS. A sincere apology to all subscribers for getting a notification to this post when it wasn’t still finished.

## The “art” of finding Transition States Part 2

Last week we posted some insights on finding Transitions States in Gaussian 09 in order to evaluate a given reaction mechanism. A stepwise methodology is tried to achieve and this time we’ll wrap the post with two flow charts trying to synthesize the information given. It must be stressed that knowledge about the chemistry of the reaction is of paramount importance since G09 cannot guess the structure connecting two minima on its own but rather needs our help from our chemical intuition. So, without further ado here is the remainder of Guillermo’s post.

**METHOD 3. **QST3. For this method, you provide the coordinates of your reagents, products and TS (in that order) and G09 uses the QST3 method to find the first order saddle point. As for QST2 the numbering scheme must match for all the atoms in your three sets of coordinates, again, use the connection editor to verify it. Here is an example of the input file.

link 0 --blank line-- #p b3lyp/6-31G(d,p)opt=(qst3,calcfc)geom=connectivity freq=noraman --blank line--Charge MultiplicityCoordinates of reagents --blank line—Charge MultiplicityCoordinates of products --blank line--Charge MultiplicityCoordinates of TS --blank line---

As I previously mentioned, it happens that you find a first order saddle point but does not correspond to the TS you want, you find an imaginary vibration that is not the one for the bond you are forming or breaking. For these cases, I suggest you to take that TS structure and manually modify the region that is causing you trouble, then use method 2.

**METHOD 4. **When the previous methods fail to yield your desired TS, the brute force way is to acquire the potential energy surface (PES) and visually locate your possible TS. The task is to perform a rigid PES scan, for this, the molecular structure must be defined using z-matrix. Here is an example of the input file.

link 0 --blank line-- #p b3lyp/6-31G(d,p)scan testgeom=connectivity --blank line--Charge MultiplicityZ-matrix of reagents (or products) --blank line--

In the Z-matrix section you must specify which variables (B, A or D) you want to modify. First, locate the variables you want to modify (distance B, angle A, or dihedral angle D). Then modify those lines within the Z-matrix, here is an example.

B1 1.41 3 0.05 A1 104.5 2 1.0

What you are specifying with this is that the variable B1 (a distance) is going to be stepped 3 times by 0.05. Then variable A1 (an angle) is going to be stepped 2 times by 1.0. Thus, a total of 12 energy evaluations will be performed. At the end of the calculation open the .log file in gaussview and in Results choose the Scan… option. This will open a 3D surface where you should locate the saddle point, this is an educated guess, so take the structure you think corresponds to your TS and use it for method 2.

I have not fully explored this method so I encourage you to go to Gaussian.com and thoroughly review it.

Once you have found your TS structure and via the imaginary vibration confirmed that is the one you are looking for the next step is to verify that your TS connects both your reagents and products in the potential energy surface. For this, an Intrinsic Reaction Coordinate (IRC) calculation must be performed. Here is an example of the input file for the IRC.

link 0 --blank line-- #p b3lyp/6-31G(d,p)irc=calcfcgeom=connectivity --blank line--Charge MultiplicityCoordinates of TS --blank line--

With this input, you ask for an IRC calculation, the default numbers of steps are 20 for each side of your TS in the PES; you must specify the coordinates of your TS or take them from the .chk file of your optimization. In addition, an initial force constant calculation must be made. It often occurs that the calculation fails in the correction step, thus, for complicated cases I hardly suggest to use **irc=calcall**, this will consume very long time (even days) but there is a 95% guaranty. If the number of points is insufficient you can put more within the route section, here is such an example for a complicated case.

link 0 --blank line-- #p b3lyp/6-31G(d,p)irc=(calcall,maxpoints=80)geom=connectivity --blank line--Charge MultiplicityCoordinates of TS --blank line--

With this route section, you are asking to perform an IRC calculation with 80 points on each side of the PES, calculating the force constants at every point. For an even complicated case try adding the **scf=qc** keyword in the route section, quadratic convergence often works better for IRC calculations.

## The ‘art’ of finding Transition States Part 1

Guillermo Caballero, a graduate student from this lab, has written this two-part post on the nuances to be considered when searching for transition states in the theoretical assessment of reaction mechanisms. He’s been quite successful in getting beautiful energy profiles for organic reaction mechanisms, some of which have even explained why some reactions do not occur! A paper in Tetrahedron has just been accepted but we’ll talk about it in another post. I wanted Guillermo to share his insight into this hard practice of computational chemistry so he wrote the following post. Enjoy!

Yes, finding a transition state (TS) can be one of the most challenging tasks in computational chemistry, it requires both a good choice of keywords in your route section and all of your chemical intuition as well. Herein I give you some good tricks when you have to find a transition state using Gaussian 09 Rev. D1

**METHOD 1. **The first option you should try is to use the **opt=qst2** keyword. With this method you provide the structures of your reagents and your products, then the program uses the quadratic synchronous transit algorithm to find a possible transition state structure and then optimize it to a first order saddle point. Here is an example of the input file.

link 0 --blank line-- #p b3lyp/6-31G(d,p)opt=qst2geom=connectivity freq=noraman --blank line--Charge MultiplicityCoordinates of reagents --blank line--Charge MultiplicityCoordinates of products --blank line---

It is mandatory that the numbering must be the same in the reagents and the products otherwise the calculation will crash. To verify that the label for a given atom is the same in reagents and products you can go to * Edit*, then

*This opens a new window were you can manually modify the numbering scheme. I suggest you to work in a split window in gaussview so you can see at the same time your reagents and products.*

**Connection.**The keyword freq=noraman is used to calculate the frequencies for your optimized structure, it is important because for a TS you must only observe one imaginary frequency, if not, then that is not a TS and you have to use another method. It also occurs that despite you find a first order saddle point, the imaginary frequency does not correspond to the bond forming or bond breaking in your TS, thus, you should use another method. I will give you advice later in the text for when this happens. When you use the noraman in this keyword you are not calculating the Raman frequencies, which for the purpose of a TS is unnecessary and saves computing time. Frequency analysis MUST be performed AT THE VERY SAME LEVEL OF THEORY at which the optimization is performed.

The main advantage for using the qst2 option is that if your calculation is going to crash, it generally crashes at the beginning, in the moment of guessing your transition state structure. Once the program have a guess, it starts the optimization. I suggest you to ask the algorithm to calculate the force constants once, this generally improves on the convergence, it will take slightly more time depending on the size of your structure but it pays off. The keyword in the route section is **opt=(qst2,calcfc)**. Indeed, I hardly encourage you to use the **calcfc** keyword in any optimization you want to run.

**METHOD 2. **If method 1 does not work, my next advice is to use the **opt=ts** keyword. For this method, the coordinates in your input file are those for the TS structure. Here is an example of the input file.

link 0 --blank line-- #p b3lyp/6-31G(d,p)opt=tsgeom=connectivity freq=noraman --blank line--Charge MultiplicityCoordinates of TS --blank line--

The question that arises here is how should I get the coordinates for my TS? Well, honestly this is not a trivial task, here is where you use all the chemistry you know. For example, you can start with the coordinates of your reagents and manually get them closer. If you are forming a bond whose length is to be 1.5Å, then I suggest you to have that length in 1.6Å in your TS. Sometimes this becomes trial and error but the most accurate your TS structure is, based on your chemical knowledge, the easiest to find your TS will be. As another example, if you want to find a TS for a [1,5]-sigmatropic reaction a good TS structure will be putting the hydrogen atom that migrates in the middle point through the way. I have to insist, this method hardly depends on your imagination to elucidate a TS and on your chemistry background.

Most of the time when you use the opt=ts keyword the calculations crashes because of an error in the number of eigenvalues, you can avoid it adding **noeigen** to the route section; here is an example of the input file, I encourage you to use this method.

link 0 --blank line-- #p b3lyp/6-31G(d,p)opt=(ts,noeigen,calcfc)geom=connectivity freq=noraman --blank line--Charge MultiplicityCoordinates of TS --blank line--

If you have problems in the optimization steps I suggest you to ask the algorithm to calculate the force constants in every step of the optimization **opt=(ts,noeigen,calcall)** this is quite a harsh method because will consume long computing time but works well for small molecules and for complicated TSs to find.

Another ‘tricky’ way to get your coordinates for your TS is to run the qst2 calculation, then if it fails, take the second- or the third-step coordinates and used them as a ‘pre-optimized’ set of coordinates for this method.

By the way, here is another useful trick. If you are evaluating a group of TSs, let’s say, if you are varying a functional group among the group, focus on finding the TS for the simplest case, then use this optimized TS as a template where you add the moieties and use this this method. This works pretty well.

For this post we’ll leave it up to here and post the rest of Guillermo’s tricks and advice on finding TS structures next week when we’ll also discuss the use of IRC calculations and some considerations on energy corrections when plotting the full energy profile. In the mean time please take the time to rate, like and share this and other posts.

Thanks for reading!