Structures and properties of large supramolecular coordination complexes predicted with the generalized energy-based fragmentation method
Bibliographic record
Abstract
The generalized energy-based fragmentation (GEBF) method has been extended to facilitate ab initio calculations of large supramolecular coordination complexes. For metal-containing coordination complexes, a special fragmentation scheme is proposed for GEBF calculations, in which coordinate bonds between metal ions and ligands are kept intact, and only single covalent bonds in organic ligands are cut into fragments. A simple strategy is exploited for the determination of the ground-state spin multiplicity of each metal ion so that the total spin of all metal-containing subsystems is assigned automatically. With this fragmentation scheme, the GEBF method is demonstrated to provide reliable energies, optimized geometry, nuclear magnetic resonance (NMR) properties and the infrared spectrum for a medium-sized supramolecular coordination complex, which are very consistent with those from the full-system quantum chemistry calculations. The GEBF method is then applied to two large supramolecular coordination complexes to illustrate its capability. For the trimetallic coordination complex Fe2Zn2(RuL2)2 (with 618 atoms), the calculated 1H chemical shifts from GEBF calculations with the B97-2 functional can account well for the experimental NMR spectrum. For the cage-guest complex Pd4L8(BF4-)3, the computed infrared spectrum obtained with the GEBF-M06-2X method can help assign the experimental peaks to the corresponding vibrational motions. The GEBF method combining with advanced electronic structure methods is expected to be a useful tool to understand and interpret structural and spectroscopic information of various supramolecular coordination complexes.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".