Multi-step modeling of liquid crystals using <i>ab initio</i> molecular packing and hybrid quantum mechanics/molecular mechanics simulations
Bibliographic record
Abstract
A density functional theory (DFT) based multi-step simulation method is used to characterize the detailed molecular structure and inter/intra- molecular interactions of two benchmark liquid crystals (LC) 5CB, 8CB and a novel tri-biphenyl ring bent core LC material. The method uses hybrid DFT at the B3LYP/6-31G* level to obtain molecular structure and Raman data. These results are fed to a crystal packing simulation to find possible crystal structures. A pico-second quantum mechanics/molecular mechanics (QM/MM) simulation model is built for the selected structures with lower overall energy as well as optimal density. The stabilized crystal structures are then extended into a super cell, heated and simulated using a mixed force field and nano-second molecular dynamics (MD). The described simulation process sequence provides predictions of molecular Raman spectrum, LC density, isotropic depolarization ratio, ratio of differential polarizability, order parameters, molecular structures, and rotating Raman spectrum of the different mesophases. The Raman spectra, order parameters and depolarization ratios all agree well with existing experimental and previous simulation results. The study of the novel tri-biphenyl ring bent core LC system shows that the ratio of differential polarizability depends on intra-molecular interactions. The findings presented in this manuscript contribute to the on-going efforts to establish links between LC molecular structures and their properties, including optical behavior.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".