Capturing Global Features of Crystals from Their Bond Networks
Why this work is in the frame
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Bibliographic record
Abstract
Representing crystal structures for machine learning property prediction traditionally relies on either composition-based methods or structure-based graph neural networks (GNNs). While these methods have been successful in predicting certain properties, they fall short in accurately capturing the periodicity of crystal structures, particularly long-range information. In this work, we show that topological features derived from labeled quotient graphs (LQGs)--finite graph representations that encode bond topology without relying on real-space geometric information--can effectively predict non-local properties, i.e., properties that are not solely determined by individual local atomic environments. Using a dataset of 25,000 silica zeolite structures, we demonstrate that XGBoost models trained on LQG-derived topological features (XGB-LQG) outperform conventional GNNs (CGCNN, MEGNet) in predicting non-local properties. Furthermore, hybrid architectures that combine GNN embeddings with LQG features achieve intermediate performance, highlighting the complementary nature of geometric and topological representations. Our results establish LQGs as a powerful representation for incorporating bond topology into crystal property prediction.
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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