Category Archives: Models

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 Multiplicity
Coordinates of reagents
--blank line—
Charge Multiplicity
Coordinates of products
--blank line--
Charge Multiplicity
Coordinates 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 test geom=connectivity
--blank line--
Charge Multiplicity
Z-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=calcfc geom=connectivity
--blank line--
Charge Multiplicity
Coordinates 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 Multiplicity
Coordinates 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 CaballeroGuillermo 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=qst2 geom=connectivity freq=noraman
--blank line--
Charge Multiplicity
Coordinates of reagents
--blank line--
Charge Multiplicity
Coordinates 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 Connection. 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.

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=ts geom=connectivity freq=noraman
--blank line--
Charge Multiplicity
Coordinates 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 Multiplicity
Coordinates 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!

 

Quantifying σ-Holes – G09 and MultiWFN


Some atomic properties such as an atomic charge are isotropic, but every now and then some derivations of them become anisotropic, for example the plotting of the Molecular Electrostatic Potential (MEP) on the electron density surface can exhibit some anisotropic behavior; quantifying it can be a bit challenging.

It is well known that halogen atoms such as Chlorine can form so-called halogen-bonds of the type R-Cl-R in crystals with a near perfect 180° angle. This finding has lead to the discovery of σ-holes in halogens. σ-Holes are electrophilic portions of the anisotropic electrostatic potential in an otherwise nucleophilic atom. Recently, Guillermo “Memo” Caballero and I calculated the MEP for a series of trichloromethyl-containing compounds at the MP2/cc-pVQZ level of theory and the mapping shows evidence of such σ-holes as seen in Figure1. Those small blue portions on an otherwise red atom indicate that some electron density is missing in that position, which by the way is located at 180° away from the carbon atom.

But having the picture is not enough. We want to quantify just how strong are those σ-holes to effectively attract a nucleophile and perhaps perform some chemistry on the C-Cl bond. That’s when we resorted to MultiWFN, a Multifunctional Wavefunction Analyzer developed by Tian Lu (卢天) at the Beijing Kein Research Center for Natural Sciences. You can check the project leader list of publications here. Among many other capabilities, MultiWFN is able to print details about properties along a surface.

In order to work with MultiWFN you need to generate a *.wfn file, if you have a previous Gaussian calculation for which you want to analyze their surface you can run a guess=only calculation in order to extract the wavefunction from the checkpoint file. Here is a dummy of the input for such calculation

%chk=oldfile.chk
# output=wfn geom=check guess=(only,read) density=current
--blank--
Title Card
--blank--
0 1
--blank--
filename.wfn
--blank--

In our case, having a post-Hartree-Fock calculation, the use of density=current is mandatory to get the MP2 density matrix and not just the HF one. Running this calculation will generate the file filename.wfn which is now used with MultiWFN. When starting MultiWFN you get to see a terminal window like the one below in which you are asked to input the path of your wfn file:

1

After loading it you will get the following window with the various options available. Type 12// (these two slashes are mandatory) to get the quantitative analysis of molecular surface option.

2

Then you will be asked to define some elements of that surface (we used the default options 0)

3

The following screenshot shows the results section in which several maxima  and minima of electrostatic potential were found (7 and 11 in our case); a star is placed on the side of the global maximum. The value of the MEP at those points is given in Hartrees, eV and kcal/mol which I personally hate because there isn’t such a thing as a mole of ‘potentials’ (same argument as giving an orbital’s energy in kcal/mol, moles of what? orbitals? Personally, I don’t like it even if its valid).4

Their visualizer is activated through the option 0 and although it is far from pretty it is quite good enough to find the numbers corresponding to maxima and minima of the MEP on the isodensity surface. If we look for the maxima then we find for our example (CHCl3) that a maximum is located in front of each Cl atom in a straight line from the C atom. Now we get to put a number on the mapped isosurface provided by Gaussian or even import the file into Chimera.

5ccl3cn

We are still working our way around MultiWFN, I hope we can find the batch option, it would be most useful. In the mean time, Guillermo and I will continue to search for σ-holes in chlorinated reagents. Thanks to Guillermo for his ongoing work in this and other topics within the realm of organic reactivity.

Have you any suggestions or ideas to work with MultiWFN? Please share them!

A personal artistic impression of the CompChem Landscape 


In a nutshell, computational chemistry models are about depicting, reproducing and predicting the electronic-based molecular reality. I had this conversation with my students last week and at some point I drew a parallel between them and art in terms of how such reality is approached.

Semi empirical methods
Prehistoric wall paintings depict a coarse aspect of reality without any detail but nevertheless we can draw some conclusions from the images. In the most sophisticated of these images, the cave paintings in Altamira, we can discern a bison, or could it be a bull? but definitely not a giraffe nor a whale, most in the same way Hückel´s method provides an ad hoc picture of π electron density without any regard of the σ portion of the electron density or the conformational possibilities (s-cis and s-trans 1,3-butadiene have the same Hückel description).

More sophisticated semi-empirical Hamiltonians like PM3 or PM6 have better parametrizations and hence yield better results. We are still replacing a lot of information for experimental or adjusted parameters but we still cannot truly adopt it as truthful. Take this pre-medieval painting of one of the first Kings of England, Aelred the Unready. It is, by today standards, a good children´s drawing and not a royal portrait, we now see more detail and can discern many more features yielding a better description of a human figure than those found in Altamira or Egypt.

Ethelred_the_Unready

Æthelred the Unready King of England ca. 1000 BC

 

Hartree-Fock
HF is the simplest of ab initio methods, meaning that no experimental results or adjustable parameters are introduced. Even more so, from the HF equations for a multi-electron system that complies with Pauli’s exclusion principle the exchange operator arises as a new quantum feature of matter with no classical analogue. Still, there are some shortcomings. Correlation energy is disregarded and most results vary according to the basis set employed. Take the impressionist movement, specially in France: In Monet´s Lady with Umbrella we have a more complicated composition, we observe many more features and although we have a better description of color composition some details, like her face, remain obscure. The impressionists are characterized by their broad strokes, the thicker the strokes the harder it is to observe details similar to what happens in HF when we change from a small to a large basis set, respectively.

Claude_Monet_023

Woman with a parasol – Her face or the identity of the flowers at her feet are indistinguishable yet we might be safe to say its springtime.

CI (Configurations Interaction)
Extension of HF to a multi-reference method yields better results. In CI we take the original guess wavefunction -as expressed through a Slater Determinant- and extend it with one or many more wavefunctions; thus a linear combination of Slater Determinants gives rise to a broader description of the ground state because other electronic configurations are involved to include more details like the ionic and covalent pictures (configurations). The more terms we include the more real the results feel. If we take classical figurative paintings we have a similar result; most of these paintings are constituted of many elements and the more realistically each element is captured the more real the whole composition looks even if some are just merely indicated.

Flaming_June,_by_Frederic_Lord_Leighton_(1830-1896)

Flaming June by Lord Leighton – Extreme details on the fabrics and the sea in the background makes us oblivious to the less detailed foot

CCSD(T) full-CI, CASPT2

In Edwards Much’s the scream, we might think we have lost some information again and went back to impressionism but we know this is actually an expressionist painting; we can now not only observe details of the figurative portion of the image but Munch has captured his subject´s fear in the form of distorsions on the subjective reality. In this way, CCSD(T), full-CI and CASPT2 methods provide a description of the ground as well as the excited states which -in experimental reality- are only accessed through a perturbation of the elecron density by electromagnetic radiation. Something resembling radiation has perturbated the subject in The Scream rendering him frightened and wondering how to return to his ground state or if such thing will be even possible.

The_Scream

The Scream by Edward Munch – what sort of perturbation got this guy’s fears out?

 

Density Functional Methods

At least due to its widespread use, DFT has risen as the preferred method. One of the reasons behind its success is the reduced computing time when compared to previous ab initio methods. So DFT is pretty much like photography, in which reality is captured in full but only apparently after selecting a given lens, an exposition, a filter, shutter speed and the occasional Photoshop for correcting issues such as aliasing. In photography, as in DFT, all details concerning the procedure or method for capturing an uncanny reproduction of reality must be stated in every case for reproduction purposes.

Now, in the end it all comes down to Magritte’s Pipe. Ceci n’est pas une pipe -or, ‘this is not a pipe’- reminds us that painting as with modeling we don’t get reality but rather a depiction of it. In this famous painting we look at an image that in our heads resembles that of a pipe but we cannot grab it, fill it with tobacco and smoke it.

MagrittePipe

This isn’t even Magritte’s painting! Let alone a pipe

The image above is a digital file, which translated becomes a scaled reproduction of an image painted by Magritte in which we see the 2D projection of the image of an object that reminds us of a pipe. In fact, the real name of this work is The Treachery of Images, definitely quite an epistemology problem on perception and knowledge but before I get too metaphysical I should finish this post.

Can you find where cubism or surrealism should be placed? with MPn methods, perhaps?

Comparing the Relative Stability of Non-Equivalent Molecules


How do you compare the stability of two or more compounds which differ in some central atom(s)?

If you simply calculate the energy of both compounds you get a misleading answer since the number of electrons is different from one to the next -in fact, the answer is not so much misleading as it is erroneous. Take compounds 1 and 2 shown in figure 1, for example. Compound 1 was recently synthesized characterized through X-Ray crystallography by my friend Dr. Monica Moya’s group; compound 2 doesn’t exist and we want to know why – or at least know if it is relatively unstable respect to 1.

Figure 1. Compound 1 exists but compound 2 is apparently less stable. Is it?

Figure 1. Compound 1 exists but compound 2 is apparently less stable. Is it?

Although stoichiometry is the same, varying only by the substitution of Ga by Al the number of electrons is quite different. We then made the following assumption: Since the atomic radii of Ga and Al are quite similar (according to the CCDC their respective covalent radii are 122[4] and 121[3] pm), relative stability must rely on the bonding properties rendering 2 harder to obtain, at least through the method used for 1. The total energy for compound 1 was calculated at the M06-2X/6-31G(d,p) level of theory; then both Al atoms were changed by Ga and the total energy was calculated again at the same level. Separately, the energy of isolated Ga and Al atoms were calculated. Compensating the number of electrons was now a simple algebraic problem:

ΔE = E(MnBxOy) – nEM + nEM’ – E(M’nBxOy)

 The absolute energy difference E1 – E2 is staggering due to the excess of 36 electrons in 2. But after this compensation procedure we now have a more reliable result of ΔE value of ca. 81 kcal/mol in favor of compound 1. In strict sense, we performed geometry optimizations at various stages: first on compound 1 to remove the distorsions due to the crystal field and then on the substituted compound 2 to make sure Ga atoms would find a right fit in the molecule but since their covalent radii are similar, no significant changes in the overall geometry were observed confirming the previous assumption.

We now have the value of the energy difference between 1 and 2 and other similar cases, the next step is to find the distal causes of the relative stability which may rely on the bonding properties of the Ga-O bond respect to the Al-O bonds.

What do you think? Is there another method you can share for tackling this problem? Please share your thoughts on the comments section.

Thanks for reading.

Atoms in Molecules (QTAIM) – Flash lesson


As far as population analysis methods goes, the Quantum Theory of Atoms in Molecules (QTAIM) a.k.a Atoms in Molecules (AIM) has become a popular option for defining atomic properties in molecular systems, however, its calculation is a bit tricky and maybe not as straightforward as Mulliken’s or NBO.

Personally I find AIM a philosophical question since, after the introduction of the molecule concept by Stanislao Cannizzaro in 1860 (although previously developed by Amadeo Avogadro who was dead at the time of the Karlsruhe congress), the questions of whether or not an atom retains its identity when bound to others? where does an atom end and the next begins? What are the connections between atoms in a molecule? are truly interesting and far deeper than we usually consider because it takes a big mental leap to think about how matter is organized to give rise to substances. Particularly I’m very interested with the concept of a Molecular Graph which in turn is concerned with the way we “draw lines” to form conceptual molecules. Perhaps in a different post we can go into the detail of the method, which is based in the Laplacian operator of the electron density, but today, I just want to collect the basic steps in getting the most basic AIM answers for any given molecule. Recently, my good friend Pezhman Zarabadi-Poor and I have used rather extensively the following procedure. We hope to have a couple of manuscripts published later on. Therefore, I’ve asked Pezhman to write a sort of guest post on how to run AIMALL, which is our selected program for the integration algorithm.

The first thing we need is a WFN or WFX file, which contains the wavefunction in a Fortran unformatted file on which the Laplacian integration is to be performed. This is achieved in Gaussian09 by incluiding the keyword output=wfn or output=wfx in the route section and adding a name for this file at the bottom line of the input file, e.g.

filename.wfn

(NOTE: WFX is an eXtended version of  WFN; particularly necessary when using pseudopotentials or ECP’s)

Analyzing this file requires the use of a third party software such as AIMALL suite of programs, of which the standard version is free of charge upon registration to their website.

OpenAIMStudio (the accompanying graphical interface) and select the AIMQB program from the run menu as shown in figure 1.

 

Figure 1

Figure 1

Select your WFN/WFX file on which the calculation is to be run. (Figure 2)

 

Figure 2

Figure 2

You can control several options for the integration of the Laplacian of the electron density as well as other features. If your molecules are simple enough, you may go through with a successful and meaningful calculation using the default settings. After the calculation is finished, several result files are obtained. We’ll work in this tutorial only with *.mpgviz (which contains information about the molecular graph, MG) and *.sum (which contains all of  needed numerical data).

Visualization of the MG yields different kinds of critical points, such as: 1) Nuclear Attractor Critical Points (NACP); 2) Bond Critical Points (BCP); 3) Ring CP’s (RCP); and 4) Cage CP’s (CCP).

Of the above, BCP are the ones that indicate the presence of a chemical bond between two atoms, although this conclusion is not without controversy as pointed out by Foroutan-Njead in his paper: C. Foroutan-Nejad, S. Shahbazian and R. Marek, Chemistry – A European Journal, 2014, 20, 10140-10152. However, at a first approximation, BCP’s can help us to explore chemical interactions.

Now, let’s go back to visualizing those MGs (in our examples we’ve used methane and ethylene and acetylene). We open the corresponding *.mpgviz file in AIMStudio and export the image from the file menu and using the save as picture option (figure 3).

Figure 3

Figure 3

The labeled atoms are NACP’s while the green dots correspond to BCP’s. Multiplicity of a bond cannot be discerned within the MG; in order to find out whether a bond is a single, double or triple bond we have to look into the *.sum file, in which we’ll take a look at the bond orders between pairs of atoms in the section labeled “Diatomic Electron Pair Contributions and Delocalization Data” (Figure 4).

Figure 4

Figure 4

Delocalization indexes, DI’s, show the approximate number of electrons shared between two atoms. From the above examples we get the following DI(C,C) values: 1.93 for C2H4 and 2.87 for C2H2; on the other hand, DI(C,H) values are  0.98 for CH4, 0.97 in C2H4 and 0.96 in C2H2. These are our usual bond orders.

This is the first part of a crash tutorial on AIM, in my opinion this is the very basics anyone needs to get started with this interesting and widespread method. Thanks to all who asked about QTAIM, now you have your long answer.

Thanks a lot to my good friend Dr Pezhman Zarabadi-Poor for providing this contribution to the blog, we hope you all find it helpful. Please share and comment.

Elements4D – Exploring Chemistry with Augmented Reality


A bit outside the scope of this blog (maybe), but just too cool to overlook. Augmented reality in chemistry education.

Songs | Snaps | Science

This is a guest post from Samantha Morra of EdTechTeacher.org, an advertiser on FreeTech4Teachers.com. 

Augmented Reality (AR) blurs the line between the physical and digital world. Using cues or triggers, apps and websites can “augment” the physical experience with digital content such as audio, video and simulations. There are many benefits to using AR in education such as giving students opportunities to interact with items in ways that spark inquiry, experimentation, and creativity. There are a quite a few apps and sites working on AR and its application in education.

Elements4D, an AR app from Daqri, allows students explore chemical elements in a fun way while learning about real-life chemistry. To get started, download Elements4D and print the cubes.

There are 6 physical paper cubes printed with different symbols from the periodic table. It takes a while to cut out and put together the cubes, but it…

View original post 475 more words

Transition State Search (QST2 & QST3) and IRC with Gaussian09


Theoretical evaluation of a reaction mechanism is all about finding the right transition states (TS) but there are no guarantees within the available methods to actually find the one we need. Chemical intuition in the proposal of a mechanism is paramount. Let’s remember that a TS is a critical point on a Potential Energy Surface (PES) that is a minimum in every dimension but one. For a PES with more than two degrees of freedom, a hyper-surface, envisioning the location of a TS is a bit tricky, in the case of a three dimensional PES (two degrees of freedom) the saddle point constitutes the location of the TS as depicted in figure 1 by a section of a revolution hyperboloid.

400px-Saddle_point

Fig1. Saddle point on a surface (min in one direction; max in the other)

Fig 1a Pringles chips -Yuck-. They exhibit a maximum on the direction parallel to the screen and a minimum on the direction perpendicular to the screen at the same point.

Fig 1a Pringles chips -Yuck-. They exhibit a maximum on the direction parallel to the screen and a minimum on the direction perpendicular to the screen at the same point.

The following procedure considers gas phase calculations. Nevertheless, the use of the SCRF keyword activates the implicit solvent calculation of choice in order to evaluate to some degree the solvent influence on the reaction energetics at different temperatures with the use of the temperature keyword.

The first step consists of a high level optimization of all minimums involved, such as reagents, products and intermediates, with a subsequent frequency analysis that includes no imaginary eigenvalues.

In order to find the structures of the transition states we use in Gaussian the Synchronous Transit-guided Quasi-Newton method [1] through the keywords QST2 or QST3. In the former case, coordinates for the reagents and products are needed as input; for the latter keyword, coordinates for the TS structure guess is needed also.

QST2)

%chk=file.chk
%nprocshared=n
%mem=nGB

#p opt=(qst2,redundant) m062x/6-31++G(d,p) freq=noraman Temperature=373.15 SCRF=(Solvent=Water)

Title card for reagents

Q M
Cartesian Coordinates for reagents
blank line
Title card for products

Q M
Cartesian Coordinates for products
blank line

QST3)

%chk=file.chk
%nprocshared=n
%mem=nGB

#p opt=(qst3,redundant) m062x/6-31++G(d,p) freq=noraman Temperature=373.15 SCRF=(Solvent=Water)

Title Card for reagents

Q M
Cartesian Coordinates for reagents
blank line
Title card for products

Q M
Cartesian Coordinates for products
blank line–
Title card for TS
Q M
Cartesian Coordinates for TS
blank line

NOTE: It is fundamental that the numbering order is kept constant throughout the molecular specifications of all two, or three, input structures. Hence, I recommend to build a set of molecules, save their structure, and then modified the coordinates on the same file to produce the following structure, that way the number for every atom will remain the same for every step.

As I wrote above, there are no guarantees of finding the right TS so many attempts are probably needed. Once we have the optimized structures for all the species involved in our mechanistic proposal we can plot their energies very simply with MS Excel the way we’ve previously described in this blog (reblogged from eutactic.wordpress.com)

Once we’ve succeeded in finding the structure of our TS we may run an Internal Reaction Coordinate (IRC) calculation. This calculation will connect the TS structure to those of the products and the reagents. Initial constant forces are required and these are commonly retrieved from the TS calculation checkpoint file through the RCFC keyword.

%chk=QST3_2p.chk
%nprocshared=8

#p m062x/6-31++G(d,p) IRC=(Maxpoints=50,RCFC,phase=(2,1))Temperature=373.15 SCRF=(Solvent=Water) geom=allcheck

Title Card

Q M
blank line

Finally, the IRC path can be visualized with GaussView from the Results menu. A successful IRC will link both structures along a single reaction coordinate proving that both reagents and products are linked by the obtained TS.

Hat tip to Howard Diaz who has become quite skillful in calculating these mechanisms as proven by his recent poster at the XII RMFQT a couple of weeks back. And as usual thanks to everyone who reads, comments, likes, recommends, rates and shares my silly little posts.

Natural Bond Orbitals (NBO) Visualization with Chemcraft


It’s been a long time since I last posted something and so many things have happened in our research group! I should catch up with them in short but times have just been quite hectic.

I’m glad to publicly thank Prof. Frank Weinhold’s gesture to include this blog in the bibliography section of the new NBO6.0 website under the NBO-Related Websites tab.

Here is a contribution from Igor Marques at the University of Aveiro in Portugal (Group Website); the original text can be found as a comment in the original NBO Visualization post but it is pretty much the same thing you can find in this post. Here is a link to Chemcraft’s website. Thanks for sharing this, Igor!

=> Examples provided by Igor Marques used Chemcraft Version 1.7, build 365 <=

In the Gaussian input, with the NBORead option included under the population keyword, we should include the PLOT option as illustrated below. The gfoldprint keyword will print the basis set to the output file in the old G03 format. Some visualization programs require a certain format of the basis set to be printed to the output file in order to plot orbitals and other surfaces like the electron density; therefore, if you want to play safe, use gfoldprint, gfprint and gfinput in the same line. gfprint will print the basis set as a list but in the new G09 format, whereas gfinput will print the basis set using Gaussian’s own input format. (The used level of theory and number of shared processors are shown as illustrations only; also the Opt keyword is not fundamental to the visualization of the NBO’s)

%chk=filename.chk
%nprocshared=8

#P b3lyp/6-311++g** Opt pop=(full,nboread) gfoldprint 
filename

0 1
molecular coordinates
$NBO BNDIDX PLOT $END

this will generate files from *.31 to *.41
For the visualization of NBOs, you’ll need FILE.31 and FILE.37. Open FILE.31 from chemcraft. It will automatically detect FILE.37 (if in the same directory).

Tools > Orbitals > Render molecular orbitals

select the NBOs of interest (whcih are in the same order of the output),

Adjust settings > OK

On the left side of the window, select the NBO of interest and then click on ‘show isosurface’. Adjust the remaining settings. To represent another orbital, click on ‘keep this surface’ and then select another orbital from the rendered set and follow the previous steps.

Some Considerations:

> It’s possible to open a formated checkpoint file, containing the NBOs, in chemcraft.
Gaussian input:

%Chk=filename.chk
%nprocshared=4
#P b3lyp/6-311++g** Opt pop=(full,nboread,savenbo) gfoldprint 
filename

0 1
molecular coordinates
$NBO BNDIDX $END

the procedure is identical, but it is only necessary to read the *fchk file and then render the desired orbitals.
However, two problems might arise:
a) Orbitals in the checkpoint are reordered, thus requiring some careful inspection of the output.
b) Sometimes, for a larger molecule, the checkpoint might not be properly saved and the Gaussian job (as previously reported – http://goo.gl/DrSgA ) will end with:

Failed in SchOr1 in NBStor.
Error termination via Lnk1e in /data/programs/g09/l607.exe at Wed Mar 6 15:27:33 2013.

****

As usual, thanks to all for reading/commenting/rating this and other posts in this blog!

Delta G of solvation in Gaussian09


How to calculate the Delta G of solvation? This is a question that I get a lot in this blog, so it is about time I wrote a (mini)post on it, and at the same time put an end to this posting drought which has lasted for quite a few months due to a lot of pending work with which I’ve had to catch up. Therefore, this is another post in the series of SCRF calculations that are so popular in this blog. For the other posts on this subjects remember to click here and here.

SMD

SMD is the keyword you want to use when performing a Self Consistent Reaction Field (SCRF) calculation with G09. This keyword was only made available in this last version of the program and it corresponds to Truhlar’s and coworkers solvation model which is recommended by Gaussian itself as the preferred model to calculate Delta G of solvation. The syntax used is the standard way used in any other Gaussian input files as follows:

# 'route section keywords' SCRF=SMD

Separately, we must either perform a gas phase calculation or use the DoVacuum keyword within the same SCRF input, and then take the energy difference between gas phase and solvated models.

# 'route section keywords' SCRF=(SMD,DoVacuum)

No solvation or cavity model should be defined since, by definition, SMD will use the IEFPCM model which is a synonym for PCM.

As opposed to the previous versions of Gaussian, the output energy already contains all corrections, this is why we must take the difference between both values (remember to calculate them both at the same level of theory if calculated separately!). Nevertheless, when using the SMD keyword we get a separate line, just below the energy, stating the SMD-CDS non electrostatic value in kCal/mol.

The radii were also defined in the original paper by Truhlar; I’m not sure if using the keyword RADII with any of its options yields a different result or if it even ends in an error. Its worth the try!

Some calculation variations are not available when using SMD, such as Dis (calculation of the solute-solvent dispersion interaction energy), Rep (solute-solvent repulsion interaction energy) and Cav (inclusion of the solute cavitation energy in the total energy). I guess the reason for this might be that the SMD model is highly parametrized.

Have you found any issue with any item listed above? Pleases share your thoughts in the comments section below. As usual I hope this post was useful and that you all rate it, like it and comment.

References

A. V. Marenich, C. J. Cramer, and D. G. Truhlar, “Universal solvation model based on solute electron density and a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions,” J. Phys. Chem. B, 113 (2009) 6378-96.
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