More Is Different: Case Studies of How Chemical Complexity Influences Stability in High Entropy Oxides
Bibliographic record
Abstract
Tailoring the chemical composition of a high entropy oxide (HEO) is a powerful approach to enhancing desirable material properties. However, the targeted synthesis of HEOs is often hindered by competing stabilizing and destabilizing factors, which are difficult to predict. This work examines the effects of increased configurational entropy on the phase formation and stability of four notable complex oxide families: perovskite ( AB O 3 ), pyrochlore ( A 2 B 2 O 7 ), Ruddlesden–Popper ( A 2 B O 4 ), and zirconium tungstate ( AB 2 O 8 ). Each of these structures has a tetravalent cation site, which we attempt to substitute with an entropic mixture of four cations, benchmarked by the parallel synthesis of a nondisordered reference compound. While all four target high entropy materials can be expected to form based on ionic radii criteria, only the high entropy perovskite Ba(Ti,Zr,Hf,Sn)O 3 is successfully synthesized. In the case of the pyrochlore, an entropy-stabilized defect fluorite is formed instead, while the Ruddlesden–Popper phase coexists with multiple competing phases. For the tungstate, an unexpected deep eutectic point between the precursors results in melting that precedes the formation of a high entropy phase. Our case studies therefore illustrate that the stability of HEOs cannot be straightforwardly predicted based on ionic radii, lattice geometry, and charge-balancing considerations alone due to the underlying complexity of the interactions between the many chemical constituents.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".