Navigating Materials Space with ML-Generated Electronic Fingerprints
Bibliographic record
Abstract
In the field of materials science, finding materials with specific properties is a major challenge due to the vastness of the search space, which makes random exploration prohibitively expensive. A more practical approach is to search for new materials within the proximity of known compounds that possess the desired property. In such an approach, fingerprinting methods are often used to measure the similarity of materials and group them into clusters. Such methods rely exclusively on the material’s structure to generate the fingerprint, which often does not correspond to clustering by desired property. To address this issue, electronic structure fingerprints that use properties such as the density of states (DOS) and band structure were proposed as an alternative. However, the computational cost of electronic structure calculations for tens of thousands of materials remains too high for rapid exploration. In this work, we developed a Graph Neural Network (GNN) ProDosNet which is trained on orbital Projected Density of States (PDOS) data and capable of predicting the electronic structure of materials at extremely low computational cost. With this model, we were able to generate PDOS fingerprints for all compounds present in the Materials Projects Database and cluster them by the similarity of their orbital PDOS, and therefore electronic properties. We demonstrate that using PDOS fingerprints allows finding materials that have similar electronic properties but drastically different structures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".