I run into this weird error all the time in Gaussian when doing optimization or frequency restarts. This is a big problem for me because a lot of times I run these on a large number of processors, but in a queue with a relatively short max wall time. Searching the web for this error yields one hit, and it’s in Chinese and not helpful even under Google translate.

Error originates: RdWrOT: IFlag = 2 Data mismatch
Search? I only get here. And the results aren’t exactly useful …

你给出的信息太少了,能不能多贴一点出来?

Anyway this almost always seems to be a problem with the collision of the previous and current route. Often I have to increase the number of optimization or SCF cycles because my systems are large and I do optimizations with diffuse functions, which tend to be pretty ill-behaved. Here’s the total head of the log:

******************************************
Gaussian 03: AM64L-G03RevE.01 11-Sep-2007
29-Mar-2009
******************************************
%chk=freq_min60.chk
%nprocshared=8
Will use up to 8 processors via shared memory.
%nproclinda=16
Will use up to 16 processors via Linda.
%mem=100MW
Default route: MaxDisk=200GB
----------------------------------------------------------------------
# freq=restart b3lyp/6-31+g(d) geom=allcheckpoint guess=read int=fmmna
toms=300 scf=tight
----------------------------------------------------------------------
1/10=4,30=1,35=1/3;
99//99;

GradGradGradGradGradGradGradGradGradGradGradGradGradGradGradGradGradGrad
Berny optimization.
Restoring state from the checkpoint file "freq_min60.chk".
Title: min60
Route: # opt b3lyp/6-31+g(d) geom=allcheckpoint guess=read int=fmmnat
oms=300 optcyc=1000 scfcyc=1000
RdWrOT: IFlag = 2 Data mismatch
MaxStp (old) = 504 MaxStp (new) = 2
MaxJob (old) = 1 MaxJob (new) = 1
RdWrOT: Data mismatch on MaxStp/MaxJob
Error termination via Lnk1e in /apps/steele/g03-E.01/l103.exe at Sun Mar 29 01:19:04 2009.
Job cpu time: 0 days 0 hours 0 minutes 13.6 seconds.
File lengths (MBytes): RWF= 49 Int= 0 D2E= 0 Chk= 56 Scr= 1
Command exited with non-zero status 1


I’m posting this more so this error comes up in Google to Vdov so maybe, maybe someone can tell me about it (no one, including people who really know the software well, has been able to provide an acceptable explanation thus far). If the two routes are both optimizations, for instance, you can usually get around this error by eliminating the opt cycle specification in the new restarted route. But if you’re moving the guess and geometry to some new calculation, it’s nearly impossible to get around this. The solution is almost always to create a formatted checkpoint file (formchk) and convert back (unfchk), so the route disappears. You could also obviously do this by specifying the new geometry as a Z-matrix in the initial calculation, but I much prefer to read my initial guess from the checkpoint, so this is not a good option in many cases. Starting the calculation and then restarting from the new binary checkpoint file usually does the trick, as there are appear to be no collisions in the route cycles.

Anyway, cheers. Hopefully someone who knows something about this will let me know.

This is a method for generating data in gromacs programs which require the system to be composed of only those molecules or atoms on which statistics will be run.

Let’s say you have a system composed of N different species and you’ve got your xtc trajectory file from the run. Then let’s say you want to know about average cluster sizes of one of the species in the simulation. For some programs in gromacs (not g_clustsize, the one in question here) this is fairly easy because the software lets you specify, either through the program options itself or through an index file, what you’d like to consider. With others though, especially those with options for dealing with molecule statistics explicitly, that don’t allow you to do this for whatever reason. So, a workaround is necessary.

First, edit your input mdp file and topology with extreme prejudice, eliminating or commenting out references to anything you’re not interested in. For instance, in my files, I need to remove all water and ions. I call all these new files “fake” versions of the real files.

$ diff fake_fullmd.mdp fullmd.mdp
15,16c15,16
< xtc_grps = protein ; sol na+ cl-
< energygrps = protein ; sol na+ cl-
---
> xtc_grps = protein sol na+ cl-
> energygrps = protein sol na+ cl-


$ diff fake_topol.top topol.top
163a164,166
> SOL 9529
> NA+ 10
> CL- 10


Make sure you have an index file if you don’t already:

$ make_ndx -f conf.gro


Then dump the first frame of the real simulation. The program (trjconv) will ask you which parts of the frame you’d like to dump.

$ trjconv -f traj.xtc -o fake_protein.gro -s b4md.tpr -n index.ndx -dump 0
...
Select group for output
Group 0 ( System) has 37968 elements
Group 1 ( Protein) has 1568 elements
Group 2 ( Protein-H) has 784 elements
...
Select a group:


Now generate the new input binary for your “fake” system.

$ grompp -f fake_fullmd.mdp -c fake_protein.gro -p fake_topol.top -o fake_b4md.tpr


Convert your trajectory, again selecting whichever part of the trajectory you’re interested in.

$ trjconv -f traj.xtc -o fake_protein.xtc -s b4md.tpr -n index.ndx
...
Select group for output
Group 0 ( System) has 37968 elements
Group 1 ( Protein) has 1568 elements
Group 2 ( Protein-H) has 784 elements
...
Select a group:


Now you’re ready to run your analysis! It won’t actually use the index file you specify here (since you’re only looking at molecules with the -mol option), though it requires it for some reason that eludes me.

$ g_clustsize -f fake_protein.xtc -s fake_b4md.tpr -mol -n index.ndx


And there you have it. Cluster statistics for an arbitrary subset of your system. Cheers.

NOTE: There actually are slightly more elegant ways of doing this, but this is perfectly sufficient for simple situations, like clustering of some molecule in some other explicit medium.

Who doesn’t like a good White Russian? Well Khalua is a damn expensive way to make your cream taste like sweet coffee. Thus I have concocted a simulacrum that would put a smile on The Dude’s face.

First you have to be in Rhode Island or it’s immediate vicinity because you need coffee syrup (it might be available elsewhere but I can’t vouch for it but it just happens to be half the ingredients for the state drink… coffeemilk) which is pictured to the left. Then it is cream and cheap vodka.

2 oz vodka
1 oz coffee syrup
light cream

Done!

UPDATE: If you search for “coffee syrup” you don’t get this but if you search for “eclipse syrup” you do… bravo

There is an interesting roundup at HubSpot of characteristics of Twitter users that correspond to more/less followers than the average number of followers. The one that caught my eye was followers by political affiliation. It corresponds to my earlier posts about politicians who use twitter and the observation that Republican politicians seem to twitter more often and more “authentically” than Democrats. Perhaps it is a reflection of the political makeup of the twitter community? I wouldn’t get too wild with pulling correlations from this data but it is interesting. (apparently #tcot is a tag for “top conservatives on twitter”

One of my favorite subjects and probably most briefly studied in college was Economics. It is one of the courses that I think everyone should take at least a little of or should read up on in their own time. That said I like to keep a couple Economics blogs in my RSS reader just to see the views Economists have on whatever is going on (not surprisingly they have a lot to say right now). I also like to get a few different perspectives so I usually read Paul Krugman in the Times, Marginal Revolution which is a couple different Economists, and Greg Mankiw’s blog. Krugman is a Nobel laureate in Economics, Tyler Cowen (Marginal Revolution) is a prof at George Mason and writes for the NYT, Alex Tabarrok (Marginal Revolution) is also a George Mason prof, and Greg Mankiw is a Harvard prof and author of the best selling intro Econ textbook.

So now the point of this is that Mankiw’s blog now has a great tie in done by the publisher of his textbook. The publisher went through his blog and made a “blog map” tying blog posts to specific citations in the textbook. Its pretty cool considering that the idea behind Mankiw’s blog is to keep students of Economics up to date with contemporary examples. Now I have no idea what the back end is like on the “blog map” but you can imagine how this could be seamlessly integrated in a very semantic way. For example tags on blog posts could auto update to the relevant citations on the map, certain phrases could trigger citation/tagging automatically, and on the textbook end you could imagine that sections be tagged in an electronic form as they are added in new additions thus updating citations throughout the blog map even with older posts. The awesome part is that you can really see the value added to the textbook. By linking it to the blog you basically get continuous additions by Mankiw making the value of his authorship continual rather than finite.