Geometric Guidance Integrated with Directed Electrostatics Strategy within a Graph Neural Network Approach for Nanocluster Structure Prediction
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Bibliographic record
Abstract
We introduce the Geometric-DESIGNN method, which integrates Geometric Guidance with Directed Electrostatics Strategy within a Graph Neural Network framework to predict the stable configuration of nanoclusters on their potential energy surfaces. This approach merges the geometric and electronic strategies using graph neural network-based models to predict structures of large atomic clusters with specific size and point-group symmetries. This approach aids in constructing atomic metal cluster structures by predicting building frames through a geometric approach and locating the minima in the molecular electrostatic potential (MESP) landscape. By following alternate geometric and DESIGNN building strategies for each shell of parent clusters, we efficiently achieve close-packed daughter structures along their evolutionary paths. The geometric-DESIGNN approach is validated on the prototype Mg n clusters, by building structures for sizes up to n < 561. Furthermore, constraining the point-group symmetry of the parent clusters, we identify new symmetric isomers of medium to large Mg n clusters with n < 150. This methodology is also employed to construct stable Mg n nanoclusters with n = 332, 338, and 561. Benchmarking results show that the geometric-DESIGNN approach is an efficient tool for accelerated prediction of the nanocluster structure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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