Calculation of the Refractive Index of Metal-Organic Frameworks Using Empirical Electronic Polarizabilities
Bibliographic record
Abstract
Abstract The prediction of optical properties from the chemical composition is of great value in developing new materials for optical applications. In this study, an approach for the calculation of the refractive index (RI) of hybrid materials from empirical polarizabilities is presented. Metal–organic frameworks (MOFs) are a prominent class of hybrid inorganic–organic materials with a modular design. This allows the fine-tuning of their optical properties. For the calculation of the RI of hybrid materials like MOFs using empirical electronic polarizabilities, a fragmentation is required. In this process, the MOF is split into its modular components, the linkers and the inorganic building units (IBUs). The electronic polarizabilities of the linkers are calculated using refractivities obtained from organic compounds. To calculate the electronic polarizabilities of the IBUs, the electronic polarizabilities of ions derived from minerals are used. With a combination of these values the MOF’s RI is calculated. Therefore, the Anderson-Eggleton equation is used with an optimized c parameter. This approach was applied to a set of 19 MOFs including Zr-based MOFs and zeolitic imidazolate frameworks (ZIFs). In a first step, the calculated polarizability values of the modular components were compared to theoretical polarizability values obtained from density functional theory (DFT) calculations. In addition, the predicted RI values are validated using HSE06 hybrid DFT calculations and available high quality experimental data. The examination of the MOF set highlighted the use of empirical electronic polarizabilities as a facile approach allowing quantitative predictions of the RI with a mean deviation from the periodic DFT calculations of 1.60%. Graphical Abstract
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".