A Template‑Based Automatic Fragmentation Algorithm for Complex and Large Systems in the Generalized Energy‑Based Fragmentation Framework
Bibliographic record
Abstract
A major bottleneck in low‑scaling energy‑based fragmentation methods is the need for manual intervention in the fragmentation step, which is time‑consuming, inconsistent, and hard to generalize across diverse molecular system. To address this challenge, we develop a template‑based automatic fragmentation algorithm that extends the generalized energy‑based fragmentation (GEBF) approach to a wide range of large and complex molecules. A hierarchical SMILES‑encoded GEBF template library for both cyclic and acyclic functional groups enables chemically meaningful and efficient partitioning via structure conversion, macrocycle detection, substructure matching, and small‑fragment merging. Controlling fragment sizes ensures a balance between accuracy and computational cost, while user‑defined templates offer enhanced flexibility. Benchmarks on biomacromolecules, macrocycles, porous organic cages, polyamide oligomers, and ionic liquids reproduce conventional quantum‑chemistry results within a few kcal$\cdot$mol$^{-1}$ (or sub‑meV/atom), while reducing the largest subsystem basis size to less than one‑third of the full system. Experimental‑level agreement is achieved in structural and spectroscopic predictions, and systems with $\approx$1,500 atoms are computed within practical timeframes. This work paves the way for fully automated, scalable, low‑cost, high‑accuracy quantum chemistry, bridging theory and large‑scale real‑world applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".