"Big Bang" in the Undergraduate Chemistry Curriculum via Symbolic Computation
Bibliographic record
Abstract
The modern delivery of concepts in physical chemistry can now take advantage of the integration of symbolic computation engines. The advancement of the friendly user- interfaces of the existent packages open to dedicated chemists the programming capacity for the creation of precise, digital definitions for most of the core notions in physical chemistry. Basic concepts such as orbitals, molecular dynamics, vibrational reaction coordinate, Stirling-compliant distribution models, thermodynamic probability and statistical entropy, etc. can now be readily calculated for medium-populated chemical systems by using the computation power of the computation engines, rather than only suggested via pictures or highly approximate calculations on the blackboard. As a result, the undergraduate curriculum can be expanded to include concepts previously introduced only in the graduate curriculum, and even subjects at the frontier of science - research objects. The impact on students is instantaneous, as they can now be equipped with tools matching the modelling/computation power utilized by high calibre researchers only a few decades ago. This paper presents the pedagogical and research results obtained by the implementation of the CHEMLOG educational system in the (under)graduate curriculum. The CHEMLOG system is based on the utilization of a symbolic computation engine interfaced with a database of chemical concepts regularly updated with the newest research results reported in the literature in the field of physical chemistry. The analysis covers a 5-year period of classroom- delivery, as well as the analysis of the online- setup covering more than one million requests from the CHEMLOG server since 1999.
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 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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".