Designing high-entropy ceramics via incorporation of the bond-mechanical behavior correlation with the machine-learning methodology
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it