Is There a Simple Descriptor to Predict Laves Phases?
Bibliographic record
Abstract
Laves phases AB 2, which represent the largest group of intermetallic compounds, have many applications as structural and functional materials, whose properties can be optimized through the tuning of solid solutions such as (A1,A2)B 2 or A(B1,B2) 2 . Although they are known to be governed by size and electronic factors, there is no universal set of rules that is able to predict which arbitrary combination of elements will lead to Laves structures. Models have been recently developed that can predict Laves structures accurately based on conventional machine learning algorithms, but more interpretable models would be desirable. Through application of the sure independence screening and sparsifying operator (SISSO) method, modified using decision trees as the scoring function, simple descriptors based on elemental properties were sought to classify Laves vs non-Laves structures within a data set consisting of 534 binary and 3833 ternary experimentally known intermetallic phases reported in Pearson’s Crystal Data. A model based on a one-dimensional descriptor was proposed that depends on elemental properties of the A and B components, with the electron density at the boundary of the Wigner–Seitz cell for the B component playing an important role. This model gave an accuracy of 90% in predicting Laves vs non-Laves structures among binary and ternary phases. As a test of the model, the solid solubility limits for Dy(Ag x Al 1– x ) 2 and Er(Ag x Al 1– x ) 2 Laves phases were predicted and then experimentally validated through arc-melting reactions and structural characterization.
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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) | 0.000 | 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".