Navigating Materials Space with ML-Generated Electronic Fingerprints
Bibliographic record
Abstract
Finding materials with good performance in a specific application, especially when the origin of good performance is not well understood or not easily computable, is a major challenge in materials science. Trial-and-error random exploration is prohibitively expensive due to the vastness of the materials space. 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, assessing materials’ similarity requires deriving some fingerprint relevant for material’s performance. Typically, material’s structure is used as the fingerprint, which often does not translate into similarity in properties. Electronic structure fingerprints, e.g., density of states (DOS) or electronic band structure, were proposed as a better alternative, however, the computational cost of their calculation on the scale of 100,000 materials remains too high for rapid exploration. In this work, we developed a Graph Convolutional Network (GCN) ProDosNet which is trained on orbital-resolved and atom-resolved projected density of states (PDOS) data and is 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 similarity of their orbital-resolved PDOS. We demonstrate that these electronic fingerprints allow finding materials with similar electronic properties but drastically different structures for applications in photovoltaics, catalysis, and batteries.
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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 | 0.001 | 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 | 0.002 | 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".