Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy
We present the development of a machine-learning (ML) model for predicting the congruency of compound melts by utilizing a combination of density functional theory-calculated formation energies and a database of experimental melting reactions. Among the various ML models tested, the XGBoost model is...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2025-06-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0247514 |
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