How to separate two Ts in a pod: Classifying T- and T′-type Ruddlesden-Popper cuprates by machine learning
Bibliographic record
Abstract
The first-order Ruddlesden-Popper (RP) phases A 2 BX 4 adopt three structure types that differ in coordination geometry around the B site: T-type (octahedral) and T′-type (square planar), which are most common, and T∗-type (square pyramidal), which is rare. Especially for RP cuprates A 2 CuO 4–δ , it is not intuitively obvious which structure is preferred depending on the combination of cations occupying the A site. Machine learning models were developed that can separate the T- and T′-type structures among these cuprates with an accuracy of >90 %, provided that the T∗-type does not form and the phases can be synthesized. Based on these models, structures were predicted for solid solutions ( A ′, A ″, A ‴) 2 CuO 4–δ containing a complex mixture of A cations ( A ′, A ″, A ‴ = Sr, La, Gd, Ho, In, Bi). The predictions were tested by targeting various members of these solid solutions through high-temperature reactions followed by slow cooling. Three samples contained pure RP phases which were confirmed to adopt the predicted structures: T-type for Sr 0.4 La 1.5 Ho 0.1 CuO 3.8 , and T′-type for Gd 1.7 Ho 0.2 Bi 0.1 CuO 4 and La 0.4 Gd 1.2 Ho 0.4 CuO 4 . Five other samples were mixtures that contained RP phases whose structures (when not T∗-type) were correctly identified by a slightly better performing model based on extra randomized trees classifier. Machine learning models to distinguish T- and T′-type structures of cuprates A 2 CuO 4–δ were experimentally validated by synthesis of new phases. • Machine learning models were built to classify structures of cuprates A 2 CuO 4–δ . • T- and T′-type structures can be distinguished with high accuracy. • ( A ′, A ″, A ‴) 2 CuO 4–δ phases with complex mixtures of A cations were predicted. • Predictions were validated by synthesis and structure determination.
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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".