Navigating Materials Space with ML-Generated Electronic Fingerprints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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