NoSpherA2 in the Hands of a Synthetic Chemist: The Future is Now
Why this work is in the frame
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Bibliographic record
Abstract
The importance of single-crystal X-ray crystallography (SC-XRD) to advances in molecular chemistry of all kinds is unquestionable. After more than a century of SC-XRD, 2023 saw the Cambridge Structure Database reach 1.25 million deposited organic and metal-organic structures. After a century of improvements in diffraction hardware in both the home lab and from using beam lines, the focus has now shifted to ways of improving the crystallographic model used in X-ray diffraction to generate the calculated structure factors Fc, which are used in comparison with the squares of the intensity data Fo. It is of course these two terms that are compared in the infamous ‘R-factor’ that is commonly used as a short-hand metric for the quality of a crystal structure: R1 = Σ||Fo| − |Fc||/Σ|Fo|. The release in 2021 of a new software suite called NoSpherA2, (NOn- SPHERical Atom-form-factors in Olex2), promises to place the fruits of advanced quantum crystallographic methods into the hands of ordinary chemist-crystallographers through its incorporation into the highly popular Olex2 GUI for SC-XRD. NoSpherA2 is an implementation of Hirshfeld Atom Refinement (HAR) that makes use of tailor-made aspherical atomic scattering (form) factors calculated on-the-fly from a Hirshfeld-partitioned electron density (ED). The ED is calculated from a Gaussian basis set single determinant SCF wavefunction using standard DFT methods. This presentation will describe the implementations of Olex2/NoSpherA2 in the author’s lab over the past four years in numerous published and unpublished crystal structure refinements. Applications to pure organic, metal-organic coordination compound, supramolecular interactions via hydrogen-bonding, halogen bonding and chalcogen bonding, and hydrated transition metal salt structures have been undertaken. To assess the impact on, and possible improvements in, refinement models, we routinely monitor comparisons of NoSpherA2 models with those refined in the Independent Atom Model using olex2.refine. A surprising and very important finding has been the improvement in the precision determined for light atom bond distances (most commonly C-C, but also C-Br or S-O interatomic distances). Furthermore, correlations have been established between various markers of “data set quality” with improvements in bond precision. Our most important discovery has been the smooth transition observed from mediocre datasets (where NoSpherA2 often has negligible impact on precision) to good datasets (where NoSpherA2 can make dramatic improvements in precision), with thus far a lack of evident deleterious consequences. That is, good data sets yield improved structure models, whilst poor datasets at the least see no improvements, which supports widescale adoption of the method. This will lead into a discussion of when (at the current stage of development) and when not to use NoSpherA2. This presentation will outline the workflows we have adopted to achieve best practices with NoSpherA2, with advice for potential adopters. As with any major change in methodology, the community of chemical crystallographers will need to develop protocols to ensure best practices amongst the wider group of users. Advice for Service Crystallographers in dealing with conservative-minded synthetic chemist clients will be discussed, and motivations for adopting the new methods, especially for routine structure modelling, will be considered. Future prospects will be considered, relevant to the changing world of synthetic chemistry and our trainees.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it