Structures and properties of ionic crystals and condensed phase ionic liquids predicted with the generalized energy‐based fragmentation method
Bibliographic record
Abstract
Abstract The generalized energy‐based fragmentation (GEBF) approach is extended to facilitate ab initio investigations of structures, lattice energies, vibrational spectra and 1H NMR chemical shifts of ionic crystals and condensed‐phase ionic liquids (ILs) with the periodic boundary conditions (PBC). For selected periodic systems, our results demonstrate that the so‐called PBC‐GEBF approach can provide satisfactory descriptions on ground‐state energies, structures, and vibrational spectra of ionic crystals and IL crystals. The PBC‐GEBF approach is then applied to three realistic condensed phase systems. For three ionic crystals (LiCl, NaCl, and KCl), we apply the PBC‐GEBF approach with MP2 theory as well as some popular DFT methods to investigate their crystal structures and lattice energies. Our calculations indicate that the crystal structures obtained with PBC‐GEBF‐MP2/6–311 + G** are very close to the corresponding X‐ray structures, while PBC‐GEBF‐ωB97X‐D/6–311 + G** provides satisfactory prediction for crystal structures and lattice energies. For two polymorphs of [n‐C4mim][Cl] crystals, we find that the PBC‐GEBF approach at the M06‐2X/6–311 + G** level can give a satisfactory descriptions on structures and Raman spectra of these two crystals. Furthermore, for [C2mim][BF4] ILs, we demonstrate that their 1H NMR chemical shifts can be estimated from averaging over 5 typical snapshots (extracted from MD simulations) with the PBC‐GEBF approach at the B97‐2/pcSseg‐2 level. The calculated results account for the observed experimental data quite well. Therefore, we expect that the PBC‐GEBF approach, combined with various quantum chemistry methods, will become an effective tool in predicting structures and properties of ionic crystals and condensed‐phase ILs.
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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".