A CASE (Computer-Assisted Structure Elucidation) for Bench-Top NMR Systems in the Undergraduate Laboratory for De Novo Structure Determination: How Well Can We Do?
Bibliographic record
Abstract
The recent popularity of benchtop (BT) NMR systems has prompted its applications in undergraduate laboratories around the world. Owing to their low maintenance cost, due to the lack of a superconducting magnetic core, and simple operation, these BT NMR systems can fulfill many of the learning objectives outlined in the undergraduate organic chemistry curricula. With a variety of BT NMR systems currently available (e.g., 43, 60, 80, and 100 MHz), it can be overwhelming for instructors to determine which system is appropriate for their needs. When used as a structure elucidation tool, the focus is often placed solely on solving chemical structures, prompting the eventual question of the magnetic field strength requirements for de novo structure elucidation. To answer this question, two artificial intelligence (AI) software packages, namely Structural Elucidator (v.2020.1.2) from ACD/Laboratories and Mnova Structure Elucidation (v 14.2.3) from Mestrelab Research, were used. These software provide an unbiased, yet separate, metric to gauge the effect of magnetic field strength on the accuracy of the determined structures. For comparison purposes, results from these two BT magnetic field strengths will be compared to those obtained from a high field NMR (500 MHz) spectrometer, providing a complete overview of the advances, as well as limitations in current BT systems for undergraduate education. In addition, the spectral data presented in this work can be used as a practical example in class to illustrate the effect of spectral resolution on the accuracy of determined structures, which is fundamental to understanding structure elucidation within organic chemistry.
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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 | 0.038 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.023 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 0.016 |
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".