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Record W4409325232 · doi:10.1063/4.0000314

NoSpherA2 in the Hands of a Synthetic Chemist: The Future is Now

2025· article· en· W4409325232 on OpenAlexaff
Renè T. Boeré

Bibliographic record

VenueStructural Dynamics · 2025
Typearticle
Languageen
FieldChemistry
TopicHistory and advancements in chemistry
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsChemistComputer scienceData scienceChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

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describe the implementations of Olex2/NoSpherA2 in the author’s lab over the past four years in numerous published and unpublished crystal structure refinements.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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0030.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0350.026

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.

Opus teacher head0.003
GPT teacher head0.235
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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