Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data
Northwestern Polytechnical University · Technische Universität Darmstadt
Abstract
High-entropy carbide ceramics (HECCs) possess promising properties for extreme high-temperature applications. Machine learning offers an effective pathway to accelerate the discovery of novel HECCs, but data imbalance poses challenges for predictive performance. Here, we integrate the Borderline-SMOTE with machine learning algorithms to address this issue. A dataset containing 251 samples was established from literature, experimental synthesis, and synthetic oversampling. Key features influencing phase formation were selected via a four-step feature selection strategy. Ten common machine learning models were trained and optimized, with the random forest (RF) model identified as the most suitable for predicting…
Citation impact
- FWCI
- 149.40
- Percentile
- 100%
- References
- 48
Authors
11Topics & keywords
- Random forest
- Feature selection
- Predictive modelling
- Training set
- Entropy (arrow of time)
- Model selection
- Extreme learning machine
- High dimensional