Accurate analytic potentials for Li2(X Σ1g+) and Li2(A Σ1u+) from 2 to 90 Å, and the radiative lifetime of Li(2p)
Bibliographic record
Abstract
Extensions of the recently introduced "Morse/long-range" (MLR) potential function form allow a straightforward treatment of a molecular state for which the inverse-power long-range potential changes character with internuclear separation. Use of this function in a direct-potential-fit analysis of a combination of new fluorescence data for (7,7)Li(2), (6,6)Li(2), and (6,7)Li(2) with previously reported data for the A((1)Sigma(u) (+)) and X((1)Sigma(g) (+)) states yields accurate, fully analytic potentials for both states, together with the analytic "adiabatic" Born-Oppenheimer breakdown radial correction functions which are responsible for the difference between the interaction potentials and well depths for the different isotopologues. This analysis yields accurate well depths of D(e)=8516.709(+/-0.004) and 8516.774(+/-0.004) cm(-1) and scattering lengths of 18.11(+/-0.05) and 23.84(+/-0.05) A for the ground-states of (7,7)Li(2) and (6,6)Li(2), respectively, as well as improved atomic radiative lifetimes of tau(2p)=27.1018(+/-0.0014) ns for (7)Li(2p) and 27.1024(+/-0.0014) ns for (6)Li(2p).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".