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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".