Accurate prediction of the structure and vibrational spectra of ionic liquid clusters with the generalized energy-based fragmentation approach: critical role of ion-pair-based fragmentation
Bibliographic record
Abstract
A generalized energy-based fragmentation (GEBF) approach has been developed to facilitate ab initio calculations of the ground-state energies, structures and vibrational spectra of general ionic liquid (IL) clusters. For the selected IL clusters, the accuracy of the GEBF approach with two different fragmentation schemes (ion-pair-based fragmentation and ion-based fragmentation) is evaluated with the conventional quantum chemistry calculations. Our results demonstrate that for the selected IL clusters, the GEBF approach with the ion-pair-based fragmentation scheme can provide much more accurate descriptions than that with the ion-based fragmentation scheme. The main reason for these results is that the non-integer charge behavior of each ion (cation or anion) in IL systems may induce significant errors for the GEBF approach with the ion-based fragmentation scheme, in which every ion is assumed to have an integer charge. However, this problem can be avoided by the ion-pair-based fragmentation scheme, in which each ion pair is assumed to be electrically neutral. Our illustrative results show that the GEBF approach with a dynamic ion-pair-based fragmentation scheme, in which ion pair fragments are updated for every structure, can provide satisfactory descriptions on the ground-state energies, optimized structures, and vibrational spectra of general IL clusters. The performance of the GEBF approach is found to be almost independent of the basis sets or theoretical methods, and the computational cost of the GEBF approach scales linearly with the system size at density functional theory (DFT) and second-order Møller-Plesset perturbation theory (MP2) levels. Due to its excellent parallel efficiency, the GEBF approach is expected to be a cost-effective tool for investigating the structure, vibrational spectra, as well as other properties of large IL clusters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".