Posts Tagged ‘speaker’

(another) WATOC 2017 report.

Tuesday, August 29th, 2017

Another selection (based on my interests, I have to repeat) from WATOC 2017 in Munich.

  1. Odile Eisenstein gave a talk about predicted 13C chemical shifts in transition metal (and often transient) complexes, with the focus on metallacyclobutanes. These calculations include full spin-orbit/relativistic corrections, essential when the carbon is attached to an even slightly relativistic element. She noted that the 13C shifts of the carbons attached to the metal fall into two camps, those with δ ~+80 ppm and those with values around -8 ppm. These clusters are associated with quite different reactivities, and also seem to cluster according to the planarity or non-planarity of the 4-membered ring. There followed some very nice orbital explanations which I cannot reproduce here because my note taking was incomplete, including discussion of the anisotropy of the solid state spectra. A fascinating story, which I add to here in a minor aspect. Here is a plot of the geometries of the 52 metallacyclobutanes found in the Cambridge structure database. The 4-ring can be twisted by up to 60° around either of the C-C bonds in the ring, and rather less about the M-C bonds. There is a clear cluster (red spot) for entirely flat rings, and perhaps another at around 20° for bent ones, but of interest is that it does form something of a continuum. What is needed is to correlate these geometries with the observed 13C chemical shifts to see if the two sets of clusters match. I include this here because in part such a search can be done in “real-time” whilst the speaker is presenting, and can then be offered as part of the discussion afterwards. It did not happen here because I was chairing the meeting, and hence concentrating entirely on proceedings!

  2. Stefan Grimme introduced his tight binding DFT method, an ultra fast procedure for computing large molecules and in passing noted the arrival of his D4 procedure (almost everyone currently uses D3 methods for this, including many of the results reported on this blog) for correcting for dispersion energies in molecules based on computed charge dependencies using the TBDFT methods. Thus we see dispersion as a property which is based on the wavefunction of the molecule, but still fast enough to accurately correct dispersion energies. He followed this with his automated procedures based on the TBDFT methods for computing full spin-spin coupled 1H NMR spectra of organic molecules. The core of this method is to recognise conformational and rotational freedoms and to compute the NMR properties for all identified isomers. These parameters are then Boltzmann averaged prior to computation of the final spin-coupled simulated frequency domain spectrum (rather than inverting this procedure by computing spin-coupled spectra of all rotamers and conformations and then averaging the spectral envelopes). This should widely revolutionise the interpretation of 1H NMR spectra by synthetic chemists.
  3. Another automated tool for synthetic chemists was presented by Jan Jenson, and can be seen here. It used MOPAC PM3 semi-empirical theory to compute relative proton affinities for a series of regioisomers as a prelude to predicting the position of aromatic electrophilic substitutions in heteroaromatic molecules. Try it out by putting a SMILES string into the box provided (e.g. COC1=CC=CC=C1) waiting a bit and seeing what the prediction is (it should be p- for the preceding example). During Q&A, a question was asked about the canonical “purity” of the SMILES (the one used in this tool comes from the Chemdraw program, which might not be identical to a SMILES for the same molecule produced by a different program), and whether an InChI descriptor might be better (also produced by Chemdraw, but perhaps a bit more canonical). Also asked was whether the prediction for an electrophile rather larger than a proton might not give good predictions? This one perhaps could be tested by readers, who could report back here?
  4. Walter Thiel completes the semi-empirical theme when he reported the new ODM2 method, the D now including dispersion. This is a powerful program, which includes e.g. full CI (configuration interaction + gradients) capability and is especially good for excited states, for dynamic simulations, and for combining these into dynamic photochemical simulations. This was applied to the chromophore in the famous “nanocar” in studying the dynamics of the photochemical rotation of the motor of the car (the thermally induced rotation was not studied). At the time that the nanocar caught my attention, I wondered about how the four independent molecular motors synchronised their rotations to allow the car to drive in a straight line. No doubt the answer is known, and if anyone reading this knows, please tell! It is probably a dynamics problem on four rotors (Walter reported just on one!).

OpenCon (2016)

Friday, November 25th, 2016

Another conference, a Cambridge satellite meeting of OpenCon, and I quote here its mission: “OpenCon is a platform for the next generation to learn about Open Access, Open Education, and Open Data, develop critical skills, and catalyze action toward a more open system of research and education” targeted at students and early career academic professionals. But they do allow a few “late career” professionals to attend as well!

I could only attend the morning session, for which the keynote speaker was Erin McKiernanorcid The presentation was entitled How open science helps researchers succeedpresented as an exploration of an article written by Erin and colleagues with the same name and published in eLife[1] Erin has created a support page at http://whyopenresearch.org to augment the presentation and it’s well worth a visit.

One striking point made was the assertion that Open publications get more citations! 
Open publications get more citations

As with many metrics of the impacts of the science publication processes, a citation itself lacks the context of why it was made (see this post for further discussion), but the expectation is that a citation is “good”. From my perspective as a chemist, I did wonder why molecular science was missing from the graphic above. Do open chemistry publications also get more citations?

Which brings me to another point made during the talk, the increasingly controversial aspect of (journal) impact factors and the pressure placed on early career researchers to publish only in those with “high” impact factors, and for their careers to be assessed at least in part based on these and the anticipated “h-index”. The audience was indeed encouraged to go visit http://www.ascb.org/Dora/ (Declaration on Research Assessment, or Putting science into the assessment of research). Have you signed it yet?

Another manifestation of the modern trend to analyse impact metrics is the site Impactstory.org. This is a scripted resource that starts from your ORCID identifier and (optionally) your Twitter account (yes, apparently Tweets matter!) to derive a more complex alternative metric of a individual’s impacts. I had not tried this one before and so I submitted my ORCID and my Twitter account, and watched as the system went off to http://orcid.scopusfeedback.com (Scopus is an Elsevier product) to attempt to create my profile. It ground for quite a while, reporting initially that I had no publications! This was followed by an unexpected error; I did not get my impact back! But this experiment served to highlight one aspect that was discussed at the meeting; data and other research objects. The graphic above refers only to the citation of journal articles, it does not yet include the citation of data. However ORCID DOES include data and research objects as works.  And because the granularity of my data and research objects is very fine (one molecule = one work), I have quite a few. In fact ~200,000! ORCID gets to about 8000 before it gives up. I suspect http://orcid.scopusfeedback.com queries ORCID, gets back ~8000 entries and crashes. No doubt the programmer tasked with implementing this resource did not anticipate that any individual could accumulate 8000+ entries! Or probably factor in that the vast majority of these would of course not be journal articles but data. If the site gets back to me about the crash I experienced, I will update here.

Simon Deakin was the next speaker with (open) data as the focus and the worries many researchers have in being scooped by others who have re-used your open data without proper attributions. The discussion teased out that if data is properly deposited, it will indeed have full associated metadata and in particular a date stamp that could help protect an author’s interests.

It was really good to meet so many early career researchers who espouse the open ethos. Perhaps, in 20 years time,  another graphic akin to the one above might demonstrate that open researchers get more promotions!

References

  1. E.C. McKiernan, P.E. Bourne, C.T. Brown, S. Buck, A. Kenall, J. Lin, D. McDougall, B.A. Nosek, K. Ram, C.K. Soderberg, J.R. Spies, K. Thaney, A. Updegrove, K.H. Woo, and T. Yarkoni, "How open science helps researchers succeed", eLife, vol. 5, 2016. https://doi.org/10.7554/elife.16800

QR codes and InChI strings.

Sunday, July 22nd, 2012

A month or so ago at a workshop I was attending, a speaker included in his introductory slide a QR (Quick Response) Code. It is a feature of most digital eco-systems that there is probably already “an app for it”. So I thought I would jump on the band wagon by coding an InChI string. Here it is below:

QRCode for an InChI string. Point your smart device at it, and see the InChI appear!

You then invoke an appropriate app (I used QR Reader for iPhone, but there are many), point it at the screen (a fair bit of wobble seems tolerated) and you get the InChI. Are there any hackers out there that could process the resulting InChI and display not so much it, but the molecule it corresponds to? A Quick mash-up I should imagine (its probably already been done!).

Here is another QR Code, this time for another post on this blog (more serious than this one!). 

QR URL code for using on a mobile device.

If you click on either QR image above, this will take you to one (of several) QR code generators. I found that selecting error correction code H seems to make recognition virtually instant. Suddenly an image popped into my mind, of a class of students in a lecture, pointing their device at my InChI codes on the projected screen, and twiddling with the molecules during my lecture (they probably never listen to me anyway 🙂 This may not be as unlikely as it seems. I am in fact compositing an iTunesU course at the moment. For a sneak β-style preview, open this page on an iPad and click on this link to load the course up (or use the QR code below). You probably need to also load up the iTunesU app first. 

QR Code for iTunesU course.

Comments welcome, QR code below!.