Molecular graphs and molecular conduction: the <i>d</i>-omni-conductors
Bibliographic record
Abstract
Ernzerhof's source-and-sink-potential (SSP) model for ballistic conduction in conjugated π systems predicts transmission of electrons through a two-wire device in terms of characteristic polynomials of the molecular graph and subgraphs based on the pattern of connections. We present here a complete classification of conduction properties of all molecular graphs within the SSP model. An omni-conductor/omni-insulator is a molecular graph that conducts/insulates at the Fermi level (zero of energy) for all connection patterns. In the new scheme, we define d-omni-conduction/insulation in terms of Fermi-level conduction/insulation for all devices with graph distance d between connections. This gives a natural generalisation to all graphs of the concept of near-omni-conduction/insulation previously defined for bipartite graphs only. Every molecular graph can be assigned to a nullity class and a compact code defining conduction behaviour; each graph has 0, 1, >1 zero eigenvalues (non-bonding molecular orbitals), and three letters drawn from {C, I, X} indicate conducting, insulating or mixed behaviour within the sets of devices with connection vertices at odd, even and zero distances d. Examples of graphs (in 28 cases chemical) are given for 35 of the 81 possible combinations of nullity and letter codes, and proofs of non-existence are given for 42 others, leaving only four cases open.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".