Ahead by a Century: Discovery of Laves Phases Assisted by Machine Learning
Bibliographic record
Abstract
Laves phases AB 2 form the most abundant group of intermetallic compounds, consisting of combinations of larger electropositive metals A with smaller metals B . Many practical applications of Laves phases depend on the ability to tune their physical properties through appropriate substitution of either the A or B component. Although simple geometrical and electronic factors have long been thought to control the formation of Laves phases, no single factor alone can make predictions accurately. Several machine learning models have been developed to discover new Laves phases, including variations caused by solid solubility, using elemental properties solely on the basis of chemical composition. These models were trained on a data set comprising about 3700 entries of experimentally known phases AB 2 with Laves and non-Laves structures. Among these models, a decision tree algorithm gave very good performance (average recall of 95%, precision of 94%, and accuracy of 96% on the test set) by using only a small set of descriptors, the most important of which relates to the electron density at the boundary of the Wigner–Seitz cell for the B component. This model provides guidance for new experiments by making predictions on >400000 candidates very quickly. A chemically unintuitive candidate Cd(Cu 1– x Sb x ) 2 with a limited solid solubility of Sb for Cu was targeted; it was successfully synthesized and confirmed to adopt a cubic MgCu 2 -type Laves structure.
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How this classification was reachedexpand
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) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".