Multilevel Fragment-Based Approach (MFBA): A Novel Hybrid Computational Method for the Study of Large Molecules
Why this work is in the frame
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Bibliographic record
Abstract
We present a novel method for the calculation of large molecules and systems, the multilevel fragment-based approach. It is based on dividing the system into small fragments followed by separate calculations of these fragments and the interactions between them. Unlike previous fragmentation-based methods, we use multiple computational methods for the individual calculations. Using an accurate method only to calculate local interactions and more approximate methods for interactions over larger distances, it is possible to achieve results very close to a more demanding fragmented calculation using the higher level method only. The number of calculations performed at the higher level scales linearly with the size of the system, which significantly improves the efficiency and allows this scheme to be used for very large systems. In this work, we have combined density functional theory with the more approximate density functional tight binding method and applied this method to the calculation of model peptides. Formulation of first derivatives of the total energy within this fragmentation scheme is also presented and tested.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it