Structure maps and crystal growth of ternary ThCr2Si2-type rare-earth transition-metal silicides and germanides
Bibliographic record
Abstract
To classify ternary rare-earth silicides REM 2 Si 2 and germanides REM 2 Ge 2 adopting the ThCr 2 Si 2 -type structure, a two-dimensional map based on radius ratios and valence electron counts was developed. This map suggested that the transition metal M plays a dominant role, which was confirmed independently by applying a machine learning algorithm called the sure independence screening and sparsifying operator (SISSO) method. In this way, a simple one-dimensional descriptor based solely on properties of the metal component M was identified in which ThCr 2 Si 2 -type phases are more likely to be formed if this descriptor meets a minimum threshold of 1.68 for silicides and 2.27 for germanides. Although arc-melting is typically used to prepare these compounds, it does not usually afford suitably sized crystals for further characterization. Flux growth of ternary germanides was investigated, with the use of indium yielding crystals of RE Co 2 Ge 2 ( RE = Ce, Eu, Yb) and other compounds. Two-dimensional maps were developed to classify ternary rare-earth silicides and germanides with ThCr2Si2-type structure, but simpler one-dimensional maps were also sought by machine learning. • Models were sought to classify ThCr 2 Si 2 -type silicides and germanides. • Radius ratios and valence electron counts are effective descriptors. • A simpler one-dimensional descriptor was developed by machine learning. • Single crystals of ternary germanides were grown in indium flux.
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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".