Category Archives: Computational Chemistry

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.

A New Graduate Student


With pleasure I announce that last week our very own Gustavo “Gus” Mondragón became the fifth undergraduate student from my lab to defend his BSc thesis and it has to be said that he did it admirably so.

Gus has been working with us for about a year now and during this time he not only worked on his thesis calculating excited states for bacteriochlorophyl pigments but also helped us finishing some series of calculations on calix[n]arene complexes of Arsenic (V) acids, which granted him the possibility to apear as a co-author of the manuscript recently published in JIPH. Back in that study he calculated the interaction energies between a family of calix macrocycles and arsenic acid derivatives in order to develop a suitable extracting agent.

For his BSc thesis, Gus reproduced the UV-Vis absorption spectra of bacteriochlorophyll-a pigments found in the Fenna-Matthews-Olson complex of photosynthetic purple bacteria using Time Dependent Density Functional Theory (TD-DFT) with various levels of theory, with PBEPBE yielding the best results among the tried set. These calculations were performed at the crystallographic conformation and at the optimized structure, also, in vacuo results were compared to those in implicit solvent (SMD, MeOH). He will now move towards his masters where he will further continue our research on photosynthesis.

Thank you, Gustavo, for your hard work and your sense of humor. Congratulations on this step and may many more successes come your way.

 

New paper in Tetrahedron #CompChem “Why U don’t React?”


Literature in synthetic chemistry is full of reactions that do occur but very little or no attention is payed to those that do not proceed. The question here is what can we learn from reactions that are not taking place even when our chemical intuition tells us they’re feasible? Is there valuable knowledge that can be acquired by studying the ‘anti-driving force’ that inhibits a reaction? This is the focus of a new manuscript recently published by our research group in Tetrahedron (DOI: 10.1016/j.tet.2016.05.058) which was the basis of Guillermo Caballero’s BSc thesis.

fig1

 

It is well known in organic chemistry that if a molecular structure has the possibility to be aromatic it can somehow undergo an aromatization process to achieve this more stable state. During some experimental efforts Guillermo Caballero found two compounds that could be easily regarded as non-aromatic tautomers of a substituted pyridine but which were not transformed into the aromatic compound by any means explored; whether by treatment with strong bases, or through thermal or photochemical reaction conditions.

fig2

These results led us to investigate the causes that inhibits these aromatization reactions to occur and here is where computational chemistry took over. As a first approach we proposed two plausible reaction mechanisms for the aromatization process and evaluated them with DFT transition state calculations at the M05-2x/6-31+G(d,p)//B3LYP/6-31+G(d,p) levels of theory. The results showed that despite the aromatic tautomers are indeed more stable than their corresponding non-aromatic ones, a high activation free energy is needed to reach the transition states. Thus, the barrier heights are the first reason why aromatization is being inhibited; there just isn’t enough thermal energy in the environment for the transformation to occur.

fig3

But this is only the proximal cause, we went then to search for the distal causes (i.e. the reasons behind the high energy of the barriers). The second part of the work was then the calculation of the delocalization energies and frontier molecular orbitals for the non-aromatic tautomers at the HF/cc-pVQZ level of theory to get insights for the large barrier heights. The energies showed a strong electron delocalization of the nitrogen’s lone pair to the oxygen atom in the carbonyl group. Such delocalization promoted the formation of an electron corridor formed with frontier and close-to-frontier molecular orbitals, resembling an extended push-pull effect. The hydrogen atoms that could promote the aromatization process are shown to be chemically inaccessible.

fig4

Further calculations for a series of analogous compounds showed that the dimethyl amino moiety plays a crucial role avoiding the aromatization process to occur. When this group was changed for a nitro group, theoretical calculations yielded a decrease in the barrier high, enough for the reaction to proceed. Electronically, the bonding electron corridor is interrupted due to a pull-pull effect that was assessed through the delocalization energies.

The identity of the compounds under study was assessed through 1H, 13C-NMR and 2D NMR experiments HMBC, HMQC so we had to dive head long into experimental techniques to back our calculations.

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!

 

Maru Sandoval M.Sc. – Our First Graduate Thesis


It is with great pride that I’d like to announce that for the first time we have a Masters Student graduated from this Comp.Chem. lab: María Eugenia “Maru” Sandoval-Salinas has finished her graduate studies and just last Friday defended her thesis admirably earning not only the degree of Masters of Science in Chemistry but doing so with the highest honors given by the National Autonomous University of Mexico.

Maru’s thesis is for many reasons a landmark in this lab not only because it is the first graduate thesis published from our lab but also the first document on our work about the study of Photosynthesis, a long sought after endeavor now closer to publication. It must also be said that Maru came to this lab when she was an undergraduate student five years ago when I just recently joined UNAM as a researcher fresh out of a postdoc stay. After getting her B.Sc. degree and publishing an article in JCTC (DOI: 10.1021/ct4004178) she now is about to publish more papers that I’m sure will be as highly ranked as the previous one. Thus, Maru was a pioneer in our lab giving it a vote of confidence when we had little to nothing to show for; thanks to her hard work and confidence, along with that of the students who have followed her, we managed to succeed as a consolidated research group in the field of computational chemistry.

More specifically, her thesis centered around finding a mechanism for the excitonic transference between pigments (bacteriochlorophyl-a, BChl-a) in the Fenna-Matthews-Olson (FMO) complex, a protein trimer with seven BChl-a molecules in each monomer, located between the antenna complex and the reaction center in green sulfur bacteria. Among the possible mechanisms explored were Förster’s theory, a modification to Marcus’ theory and finally we explored the possibility of Singlet Fission occurring between adjacent molecules with the help of Dr. David Casanova from the Basque Country University where Maru took a short research stay last autumn. Since nature doesn’t conform to any specific mechanism -specially in a complex arrangement such as the FMO- then it could be possible that a combination of the above might also occur but lets just wait for the papers to be published to discuss it. Calculations were performed through the TD-DFT and the C-DFT formalisms using G09 and Q-Chem; comparing experimental data in CH3OH (SMD implicit calculations with the SVWN5 functional) were undertaken previously for selection of the level of theory.

Now, after two original theses written and successfully defended, an article published in JCTC and more in process, at least five posters, a couple of oral presentations and countless hours at her desk, Maru will go pursuit a PhD abroad where I’m sure she will exceed anyone’s expectations with her work, drive, dedication and scientific curiosity. Thank you, Maru, for all your hard work and trust when this lab needed it the most, we wish you the best for you earn it. You will surely be missed.

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!

XIV Mexican Reunion on Theoretical Physical Chemistry


Each year the Mexican community who works in the realm of computational and theoretical chemistry gathers to share the most recent work done around our country. This year, I tried to live Tweet the event and although I failed miserably in doing so -as well as in convincing others to join me- I’m trying to put together the things that caught my attention. I also tried to Storify it but I cannot embed the result here in WordPress.

María Eugenia “Maru” Sandoval just came back from a short stay in Spain where she worked with Prof. David Casanova on Singlet Fission theory applied to her work on photosynthesis. Here work was presented as a poster although we would have preferred she gave a talk.

  

Also, Guillermo “Memo” Caballero presented his recent developments in reaction mechanisms.   

Below there is a list of Tweets from the conference. If you are interested in any of these items please contact me for further information, or just, you know, google the people mentioned in each Tweet, it shouldn’t be too hard.

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Of course when you have a large meeting with so many people working with and on Density Functional Theory (DFT) you know that Perdew’s suggested ‘Jacob’s ladder’ of functional quality for chemical accuracy is bound to show up a few times.
I actually wrote a post that gravitates around this issue but using figurative painting as an analogue. You may find it here

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That is Pt “double bond” Sn. By no means I’m equating platinum to tin. No sir. Mulliken’s population analysis should not even been brought up anymore, should it?

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If there is water and ice on Mars then most definitely should be clathrates. (Please excuse the misspelling throughout, though.)

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The rest are the previous announcements which were aimed to generate the momentum for the live tweeting thing.

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“Not!”

I hope I can make this a thing next year during the 15th edition of RMFQT. I had the honor to be the first speaker and I will upload my presentation soon.

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?

Partial Optimizations with Gaussian09


Sometimes you just need to optimize some fragment or moiety of your molecule for a number of reasons -whether because of its size, your current interest, or to skew the progress of a previous optimization- or maybe you want just some kind of atoms to have their positions optimized. I usually optimize hydrogen atoms when working with crystallographic files but that for some reason I want to preserve the rest of the molecule as refined, in order to keep it under a crystalline field of sorts.
Asking Gaussian to optimize some of the atoms in your molecule requires you to make a list albeit the logic behind it is not quite straightforward to me. This list is invoked by the ReadOptimize keyword in the route section and it includes all atoms by default, you can then further tell G09 which atoms are to be included or excluded from the optimization.
So, for example you want to optimize all atoms EXCEPT hydrogens, then your input should bear the ReadOptimize keyword in the route section and then, at the end of the molecule specification, the following line:

atoms notatoms=H

If you wish to selectively add some atoms to the list while excluding others, here’s an example:

atoms=C H S notatoms=5-8

This list adds, and therefore optimizes, all carbon, hydrogen and sulfur atoms, except atoms 5, 6, 7 and 8, should they be any of the previous elements in the C H S list.
The way I selectively optimize hydrogen atoms is by erasing all atoms from the list -using the noatoms instruction- and then selecting which are to be included in the list -with atoms=H-, but I haven’t tried it with only selecting hydrogen atoms from the start, as in atoms=H

noatoms atoms=H

I probably get very confused because I learned to do this with the now obsolete ReadFreeze keyword; now it sometimes may seem to me like I’m using double negatives or something – please do not optimize all atoms except if they are hydrogen atoms. You can include numbers, ranks or symbols in this list as a final line of your input file.

Common errors (by common I mean I’ve got them):

Lets look at the end of an input I just was working with:

>  AtmSel:  Line=”P  0″
>  Maximum list size exceeded in AddBin.
>  Error termination via Lnk1e in…

AtmSel is the routine which reads the atoms list and I was using a pseudopotential on phosphorous atoms, I placed the atoms list at the end of the file but it should be placed right after the coordinates and the connectivity matrix, should there be one, and thus before any external basis set or pseudopotential or any other specification to be read by Gaussian.

As a sort of test you can use the instruction:

%kjob l103
%chk=myfile.chk
...

at the Link0 section (where your checkpoint is defined). This will kill the job after the link 103 is finished, thus you will only get a list of what parameters were frozen and which were active. Then, if things look ok, you can run the job without the %kjob l103 instruction and get it done.

As usual I hope this helps. Thanks for reading except to those who didn’t read it except for the parts they did read.

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