An Assessment of Computational Methods for Calculating Accurate Structures and Energies of Bio-Relevant Polysulfur/Selenium-Containing Compounds
Bibliographic record
Abstract
The heavier chalcogens sulfur and selenium are important in organic and inorganic chemistry, and the role of such chalcogens in biological systems has recently gained more attention. Sulfur and, to a lesser extent selenium, are involved in diverse reactions from redox signaling to antioxidant activity and are considered essential nutrients. We investigated the ability of the DFT functionals (B3LYP, B3PW91, ωB97XD, M06-2X, and M08-HX) relative to electron correlation methods MP2 and QCISD to produce reliable and accurate structures as well as thermochemical data for sulfur/selenium-containing systems. Bond lengths, proton affinities (PA), gas phase basicities (GPB), chalcogen–chalcogen bond dissociation enthalpies (BDE), and the hydrogen affinities (HA) of thiyl/selenyl radicals were evaluated for a range of small polysulfur/selenium compounds and cysteine per/polysulfide. The S–S bond length was found to be the most sensitive to basis set choice, while the geometry of selenium-containing compounds was less sensitive to basis set. In mixed chalcogens species of sulfur and selenium, the location of the sulfur atom affects the S–Se bond length as it can hold more negative charge. PA, GPB, BDE, and HA of selenium systems were all lower, indicating more acidity and more stability of radicals. Extending the sulfur chain in cysteine results in a decrease of BDE and HA, but these plateau at a certain point (199 kJ mol−1 and 295 kJ mol−1), and PA and GPB are also decreased relative to the thiol, indicating that the polysulfur species exist as thiolates in a biological system. In general, it was found that ωB97XD/6-311G(2d,p) gave the most reasonable structures and thermochemistry relative to benchmark calculations. However, nuances in performance are observed and discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".