Abstract:Fueled by the widespread adoption of Machine Learning and the high-throughput screening of materials, the data-centric approach to materials design has asserted itself as a robust and powerful tool for the in-silico prediction of materials properties. When training models to predict material properties, researchers often face a difficult choice between a model's interpretability or its performance. We study this trade-off by leveraging four different state-of-the-art Machine Learning techniques: XGBoost, SISSO… Show more
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