Identify Survived Key Features and Relevant Mechanisms for Designing High-Entropy Carbides via AI or Machine Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multielement high-entropy carbides (HECs) provide many opportunities for HECs to obtain optimal combinations of various properties, e.g., high strength and high flexibility, leading to high toughness. However, the multielements significantly increase the compositional arrangements, challenging the development of advanced HECs. Machine learning (ML) provides a powerful approach to HEC design/discovery. Identifying key parameters or selecting the key features is crucial for carbide design with desirable properties. In the meantime, developing a reliable ML model with minimized mutual interference from multiple features, toward more accurate property predictions, also benefits the carbide discovery. In this study, we use a small carbide database to study the correlations between elastic moduli and 13 features with the assistance of recursive feature elimination (RFE) and investigate how the feature selection affects the prediction of HECs' properties. It is demonstrated that the mutual interference among highly correlated features may have a negative influence on the accuracy of ML prediction due to their mutual interference or redundant noise. For HECs, a few basic features are identified, which largely determine their elastic moduli. Among them, electron work function and valence electron concentration (VEC) appear to be more responsible for bulk and shear/Young's moduli, respectively. Other parameters are crucial for all elastic moduli, such as mixing entropy, formation energy, bond order, and bond length. This study demonstrates the significance of identifying the prominent features with lowered mutual interference or noise in further HECs with optimal properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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