Capturing Global Features of Crystals from Their Bond Networks
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".