Probing potential energy surface exploration strategies for complex\n systems
Bibliographic record
Abstract
The efficiency of minimum-energy\nconfiguration searching algorithms\nis closely linked to the energy landscape structure of complex systems,\nyet these algorithms often include a number of steps of which the\neffect is not always clear. Decoupling these steps and their impacts\ncan allow us to better understand both their role and the nature of\ncomplex energy landscape. Here, we consider a family of minimum-energy\nalgorithms based, directly or indirectly, on the well-known Bell–Evans–Polanyi\n(BEP) principle. Comparing trajectories generated with BEP-based algorithms\nto kinetically correct off-lattice kinetic Monte Carlo schemes allow\nus to confirm that the BEP principle does not hold for complex systems\nsince forward and reverse energy barriers are completely uncorrelated.\nAs would be expected, following the lowest available energy barrier\nleads to rapid trapping. This is why BEP-based methods require also\na direct handling of visited basins or barriers. Comparing the efficiency\nof these methods with a thermodynamical handling of low-energy barriers,\nwe show that most of the efficiency of the BEP-like methods lie first\nand foremost in the basin management rather than in the BEP-like step.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".