XGBoost model for electrocaloric temperature change prediction in ceramics
Abstract An eXtreme Gradient Boosting (XGBoost) machine learning model is built to predict the electrocaloric (EC) temperature change of a ceramic based on its composition (encoded by Magpie elemental properties), dielectric constant, Curie temperature, and characterization conditions. A dataset of...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-07-01
|
| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-022-00826-3 |