Performance prediction of high-entropy perovskites La0.8Sr0.2MnxCoyFezO3 with automated high-throughput characterization of combinatorial libraries and machine learning
Bibliographic record
Abstract
Perovskite oxides (ABO3) represent a large family of materials with wide application in many fields due to their celebrated structural and chemical flexibility. Such a vast space of compositions requires efficient exploration strategies now possible with automated high-throughput experiments combined with machine learning prediction algorithms. In this study, we investigate the compositionperformance relationships of high-entropy La0.8Sr0.2MnxCoyFezO3±𝞭 perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells. After deposition of a continuous compositional map using thin film combinatorial pulsed laser deposition, we obtain experimental data of structural, composition and functional propertiesfor the whole material family under study through a combination of six advanced characterization methodologies with mapping capabilities. We prove that supervised machine learning methods, particularly random forests, effectively capture the complex relationships between composition, structural features, and electrochemical performance including oxygen transport properties. Using these predictive methods, we create an accurate continuous map of performance for the whole compositional space under study and we open it to the community. Moreover, our model yields an unambiguousstatistical correlation between the distortion of the oxygen sublattice (obtained from spectral analysis of their Raman-active modes) and the highest performances. Finally, the study consistently identifies Fe-rich high-entropy oxides as the optimal compounds with the lowest area-specific resistance values for oxygen electrodes at 700°C. Overall, the work proves the potential of a detailed exploration of relevant chemical spaces by coupling highthroughput experiments and machine learning models to gain new insights and optimize relevant families of materials.
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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 | 0.000 | 0.001 |
| 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 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".