Henry Rzepa's blog

I have variously talked about persistent identifiers on this blog. These largely take the form of DOIs (Digital object identifiers), and here they relate to either journal articles or datasets associated with either the article or the blog post or both. Other disciplines, particularly the earth sciences, have long used persistent identifiers (PIDs) to identify physical objects rather than digital ones. One of my ambitions is to assign such identifiers to a small but highly historical collection of physical objects in my possession, as described at this post. As a prelude to this project, here I describe some ways of searching for physical objects that have been assigned a PID. Thanks Rorie for providing these! 

1. Here is a general search for physical objects with associated metadata describing them as registered with DataCite. https://commons.datacite.org/doi.org?query=types.resourceTypeGeneral:PhysicalObject (11,269,090 items)
2. The search can be slightly constrained to find only identifiers that originate from the earlier IGSN ID (International generic sample number) see here for details and https://www.igsn.org/about/ for the organisation set up) using the syntax query=client.client_type:igsnCatalog types.resourceTypeGeneral:PhysicalObject (9,642,030 items)

The exciting prospect is that in due time, such searches could be constrained by adding specifically chemical properties, most obviously eg an InChI identifier. At the moment, it is unlikely any existing samples have even been registered with such a term.

3. Thus combining two queries would give the following:
   query=client.client_type:igsnCatalog types.resourceTypeGeneral:PhysicalObject+AND+subjects.subjectScheme:inchikey+AND+subjects.subject:*
4. Removing the PhysicalObject constrain gives a different response:
   query=(subjects.subjectScheme:inchikey+AND+subjects.subject:*+OR+subjects.subjectScheme:inchi+AND+subjects.subject:*)

 When this becomes possible, (see project above!), it would enable for example journal articles (or the FAIR data associated with them) to reference information about a physical sample associated with eg the preparation of a molecule new to science.

Sometimes, the properties of a molecule are predicted long before it is synthesised. One such is diberyllocene. I first encountered a related molecule, beryllocene itself, many moons ago.[1] This was unusual because unlike the original metallocenes, the metal atom was not symmetrically disposed between the two cyclopentadienyl faces. Now diberyllocene is finally reported in which replacing one Be by Be-Be induces (according to calculation, D₂) symmetry[2]. I will not repeat the excellent analysis of the wavefunction reported in this article, but confine myself to showing two molecular orbitals which examplify its bonding.

Highest occupied molecular orbital
Lowest occupied π-molecular orbital

The HOMO (FAIR data 10.14469/hpc/12702) essentially shows a Be-Be single bond, originating formally from the central Be₂²⁺ dication, balanced by the two cyclopentadienyl anion ligands. Click on the images to see this orbital in  3D.

Oddly, an excited state of Be₂ on its own actually carries a Be=Be double bond, a property again predicted a long time ago by theory. The most stable π-MO in diberyllocene originates in the six electron aromatic cyclopentadienyl rings. By acquiring a share of six electrons from one Cp ring, and a share of the two electrons from the Be-Be bond, each Be atom achieves the octet of electrons known by generations of students. This is the sort of molecule that could be taught in schools at an early stage to illustrate the octet rule. And its good to know that simple new molecules illustrating this are still being discovered by chemists.

Since this started with experimental realisation of a predicted molecule, can I suggest as a new prediction, lithioborocene, in which a B and an Li replace two Be atoms? Individually, lithiocenes, boracenes and Li-B bonds are known from crystal structures. So its not a way out prediction to combine these observations. Any friendly synthetic chemist up for the challenge?

This post has DOI: 10.14469/hpc/12704

References

1. M.J.S. Dewar, and H.S. Rzepa, "Ground states of molecules. 45. MNDO results for molecules containing beryllium", Journal of the American Chemical Society, vol. 100, pp. 777-784, 1978. https://doi.org/10.1021/ja00471a020
2. J.T. Boronski, A.E. Crumpton, L.L. Wales, and S. Aldridge, "Diberyllocene, a stable compound of Be(I) with a Be–Be bond", Science, vol. 380, pp. 1147-1149, 2023. https://doi.org/10.1126/science.adh4419

This is a venerable organic reaction, which curiously I have not previously covered here. First described in 1859, its nature was only properly elucidated in 1873. It is a member of a class of reaction I have previously named “solvolytically assisted pericyclic”, or “perisolvolytic“. Here I explore some of the subtle stereoelectronic effects observed for this apparently simple reaction.

It applies to a class of molecule known as 1,2-diols. Protonation is quickly followed by migration of a (in this example) methyl group, followed by deprotonation of the carbonyl group formed by this process. There are two mechanistic stages, the first being the departure of the now protonated “ol” unit, and the second the migration of the methyl. In most text books and of course Wikipedia, these are shown as very distinct steps. But they could also occur in one concerted step, albeit probably asynchronously.

A B3LYP+GD3+BJ/Def2-TZVPP/SCRF=ethanol calculation provides mechanistic detail (FAIR Data 10.14469/hpc/1769)

1. To start with, we note the H-bond formed between O22-H21. Between IRC = -10 and -6, this lengthens from 1.625Å to 1.843Å, destabilising the protonated alcohol group.
2. Between IRC -6 to -1, the C1-O19 bond breaks, from a starting length of 1.556Å to ~2.787Å.
3. When IRC 0.0 is reached (the transition state), the C11 methyl starts to migrate across, a process mostly complete by IRC +2. 
4. The final stage is formation of a weak interaction between C2 and O19 to reach IRC 7.
5. Several more minor effects can also be discerned. Firstly methyl C3 rotates, to set up a better hyperconjugative interaction with the temporary carbocation forming at C1. This rotamer forms the first of several “hidden intermediates” in the reaction, intermediates which almost form before being consumed, at IRC -6.5 (see the plot above labelled RMS gradient form, for the minimum in the function at this IRC value).
6. Another hidden intermediate appears at IRC -2, being the transient carbocation, as shown in stepwise versions of this mechanism, such as the Wikipedia page. But its not real, merely hidden! As it approaches, methyl C7 rotates to maximise the hyperconjugative interactions.
7. At IRC ~+3, methyl C15 rotates to again maximise hyperconjugation with the newly formed C=O bond.

Ca we quantify some of these effects? This can be done by computing localised orbitals (NBOs) and pairwise interactions between a donor NBO (a bond or a lone pair) and an acceptor NBO (an antibonding orbital). 

1. The E(2) interaction between donor bond C2-C11 and acceptor C1-O19 is 3.3 kcal/mol (above the noise, but not especially strong). It corresponds to an antiperiplanar alignment of the C2-C11 σ orbital and the C1-O19 σ^* orbitals and results in the breaking of bond C2-C11 (and reformation as C1-C11). 
2. The E(2) value between donor lone pair O22 and acceptor C2-C11 σ^* is 6.9 kcal/mol and corresponds to antiperiplanar alignment of these two orbitals, resulting in formation of the C=O carbonyl π-bond, whilst simultaneously increasing the antibonding character of the C-C bond to encourage it to break.

Models of these two interactions can be seen below. Click on the image to load them. The colour blue overlaps positively with the colour purple, and red with orange.

By the time the transition state is reached, these two interactions have evolved to the following:

So this venerable reaction has some nice subtle stereoelectronic behaviour. Those methyl rotations have been skipped over here, but a deeper look into them might also be worthwhile. There is much more to this reaction, but I will leave this analysis here.

This post has DOI https://doi.org/10.14469/hpc/12684

This editorial from Nature[1] is a timely reminder of the importance of data. But also, not just any data, but “accurate and accessible training data“. Accessible of course is one of the attributes of FAIR (Findable, Accessible, Interoperable and Re-usable). The editorial also states “data need to be recorded in agreed and consistent formats, which they are not at present“. That is covered by the I and R of FAIR, often applied in conjunction with metadata recording the Media type that the data is held in (See DOI https://doi.org/jvk9 for examples of the use of Media types in chemical computation and chemical NMR). Again, “The best possible training sets would also include data on negative outcomes“. This relates to the separation of the two publication processes, namely the article itself (or the story behind the data) and the data itself as a first class scientific object. Thus when we publish FAIR data in association with articles, the data archive will often contain data that is not used in the article itself (perhaps because it led to a negative outcome), but is nevertheless part of the FAIR data collection for that topic. Even if the data does not lead to journal publication, publishing it in a data repository means it will not be lost. Somebody (or AI software) may still find it useful.

Whilst the acronym AI is increasingly used and hyped up, I would argue that FAIR should accompany the use of the term AI in most cases (as indeed it is at eg.[2]). Amongst other benefits, FAIR implies a metadata descriptor record is present, which if richly populated, would help address the “accurate” of “accurate and accessible” by adding context. As we show here[2], FAIR is also “AI-Ready“. Indeed an often used alternative expansion of the acronym is “FAIR is AI-Ready”. It is indeed designed to be so if the metadata is sufficiently rich. I also remind that an IUPAC working party is working to produce recommendations to help with this aspect.[3]

My final comment adds to the requirement of “accurate and accessible training data“. I would reformulate this as “accurate, accessible and complete training data“. Much data in chemical science is recorded on an instrument, or computed using modelling software. As it emerges from the instrument or the software package, it can be said to be “complete”. Nothing has been thrown away at this stage. But think of eg NMR data. This is acquired as a FID, and then subjected to analysis (A Fourier Transform, after weighting, which does introduce potential artefacts into the data!). It is the latter data type that is invariably published, often in a visual (PDF) form which may lack numerical accuracy and which is machine processable only with difficulty.  Or think of crystallography, where data emerges as diffraction images and is then transformed into structure factors and coordinates. Only the last form is often published (as a CIF file), but the original data is almost never so (see[4] for an example where complete crystallographic data is published). Then again, chemical computations. The full record of the computation is often produced as a “checkpoint” or “interoperability format” (see eg DOI: 10.14469/hpc/10043) which contains the computed wavefunction and which can be re-used to compute a wide variety of new properties. But most articles currently record computational data simply as a set of atom coordinates. If you are really lucky, you might get some keywords used to run the calculation. But nothing which would eg allow an AI-algorithm to easily compute a property it might need. We cannot be sure that a machine learning/AI procedure might not benefit from such complete data.

So, FAIR and AI are conjoined, they each need the other and should not be separated. And to repeat, where data is transformed before being published, please also add the complete dataset, not just any reduced form.

Post DOI: 10.14469/hpc/12586

References

1. "For chemists, the AI revolution has yet to happen", Nature, vol. 617, pp. 438-438, 2023. https://doi.org/10.1038/d41586-023-01612-x
2. H.S. Rzepa, and S. Kuhn, "A data‐oriented approach to making new molecules as a student experiment: artificial intelligence‐enabling FAIR publication of NMR data for organic esters", Magnetic Resonance in Chemistry, vol. 60, pp. 93-103, 2021. https://doi.org/10.1002/mrc.5186
3. R.M. Hanson, D. Jeannerat, M. Archibald, I.J. Bruno, S.J. Chalk, A.N. Davies, R.J. Lancashire, J. Lang, and H.S. Rzepa, "IUPAC specification for the FAIR management of spectroscopic data in chemistry (IUPAC FAIRSpec) – guiding principles", Pure and Applied Chemistry, vol. 94, pp. 623-636, 2022. https://doi.org/10.1515/pac-2021-2009
4. J. Almond-Thynne, A.J.P. White, A. Polyzos, H.S. Rzepa, P.J. Parsons, and A.G.M. Barrett, "Synthesis and Reactions of Benzannulated Spiroaminals: Tetrahydrospirobiquinolines", ACS Omega, vol. 2, pp. 3241-3249, 2017. https://doi.org/10.1021/acsomega.7b00482

I have previously looked at the pigments used to colour the Book of Kells, which dates from around 800 AD and which contained arsenic sulfide as the yellow colourant. The Bayeaux tapestry is a later embroidery dating probably from around 1077 and here the colours are based entirely on mordanted natural dyes. These are generally acknowledged to be blue woad (principle component indigo), red madder (principle component alizarin) and the less well-known yellow weld, which comes from the plant Reseda Luteola and the principle component of which is luteolin.

Luteolin has an interesting chemical history. It was first purified in 1829, in the dawn of organic chemistry, and its formula C₁₅H₁₀O₆ established by 1864. A. G. Perkin, the son of the William Perkin who discovered the dye mauveine, then provided the chemical structure[1] in 1896. This latter article is well worth a modern read, since it beautifully illustrates how the art of structure determination was conducted in the days before crystallography and NMR.

Perkin obtains his structure by comparing luteolin to then known quercetin, concluding that the former must also contain an aromatic hydroxy group “ortho” to the carbonyl group, as in querecetin. The key experimental evidence was that alkylation of luteolin with iodoethane only produces a triethoxy derivative of luteolin, with “one hydroxy group resisting ethylation“. It was by then established, by four different sets of researchers, that hydroxy groups adjacent to the carbonyl in e.g. quercetin or alizarin resisted alkylation. The structure of luteolin was established (see eg 10.5517/cc798yq) by combining various such observations, a method (and skill) that has largely lapsed nowadays. 

A modern take on this selective alkylation might be to compute e.g. the wavefunction (ωB97XD/Def2-TZVPP/SCRF=water) of luteolin to inspect the energies of the orbitals associated with alkylation of the hydroxyl group, using the energy of the nucleophilic lone pair oxygen orbital (FAIR DOI: 10.14469/hpc/12185) as an indicator. The least stable such orbital (highest energy) is normally an indicator of the most nucleophilic electron pair. In this case, the highest (most reactive) such orbital is the one adjacent to the carbonyl group, which thereby reveals a mystery, since it is this very hydroxyl that resists alkylation! A transition state approach to this might be needed to resolve the mystery, factoring in perhaps steric effects etc.

-0.6951 au	-0.7132 au
-0.7169 au	-0.7205 au
	<

The calculated UV-Vis spectrum is shown below, showing the peak at ~300 NM responsible for the intense yellow colour (300-400 nm).


The strongest oscillator contribution to the transition is shown below.

LUMO au	HOMO

So here I have cast a little more light on this relatively unknown natural yellow dye, that was used for many centuries to colour woollen materials.

References

1. A.G. Perkin, "XLIX.—Luteolin. Part II", J. Chem. Soc., Trans., vol. 69, pp. 799-803, 1896. https://doi.org/10.1039/ct8966900799

In the previous post, I discussed how data associated with two of the candidates for molecules of the year – 2022 could be retrieved and then used to inspect their three dimensional structures. Here I focus on the ultra large cavitands recently reported[1]. As I noted, these have an associated data coordinate archive published on Zenodo (DOI: 10.5281/zenodo.6953961) although this is not cited in the article itself.

Shown below are the coordinates of the A4-T molecule containing C₇₀, the first being optimized at the PM6 level and the second at the PM7 level. The most obvious difference is that all the close C-H…H-C contacts of the host molecule shrink from between ~4Å to 2.6Å at PM6 geometries, down to about 2.1Å for PM7, a contraction of at least 0.5Å. Also, the gap between the host and the guest reduces from around 4.2Å to 3.45 Å (a distance typical of π-π stacking by the way), a significant reduction of ~0.75Å. Click on the two images below to view this model.

The difference in the dispersion terms for these two geometries emerges as 36.6 kcal/mol lower for the PM7 optimised geometry compared to the original PM6 geometry, a significant stabilisation. FAIR data is at DOI: 10.14469/hpc/12022 if you want to analyse the cavity sizes further.

Shown below is the NCI (non-covalent-interaction) surface, computed at the PM7 geometry and using the MNDO wavefunction. This illustrates the stabilisations occuring from the non-covalent density (takes a little while to load).

This post has DOI: 10.14469/hpc/12027

References

1. J. Pfeuffer‐Rooschüz, S. Heim, A. Prescimone, and K. Tiefenbacher, "Megalo‐Cavitands: Synthesis of Acridane[4]arenes and Formation of Large, Deep Cavitands for Selective C70 Uptake", Angewandte Chemie International Edition, vol. 61, 2022. https://doi.org/10.1002/anie.202209885