Designing high-entropy ceramics via incorporation of the bond-mechanical behavior correlation with the machine-learning methodology
Bibliographic record
Abstract
Although high-entropy ceramics (HECs) are greatly attractive because of their superior properties over conventional ceramics, there is a lack of reliable and effective design guidelines for producing HECs with the wished-for mechanical properties. The often-used trial-and-error testing approach or case-by-case calculations without clear design guidelines are ineffective and expensive. Here, we propose a machine-learning accelerated strategy to design HECs with the desired mechanical properties. Using rock-salt ceramics as representative examples, we demonstrate that their mechanical properties are determined synergistically by different types of bonds, and bond properties of multi-element ceramics can be weighted from those of the involved constituents. Machine-learning models are developed to describe the correlations between bond characteristics and macro-mechanical properties, which show good prediction accuracy, as verified by computational and experimental data. The strategy for the HEC design, developed based on bond-mechanical property correlations and machine-learning methodology, provides a low-cost, highly efficient, and reliable method for developing advanced ceramics with superior mechanical properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".