Machine learning of material properties: Predictive and interpretable multilinear models
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DOI: 10.1126/sciadv.abm7185
Abstract: Machine learning models can provide fast and accurate predictions of material properties but often lack transparency. Interpretability techniques can be used with black box solutions, or alternatively, models can be created that are directly interpretable.… read more here.
Keywords: properties predictive; machine learning; material; material properties ... See more keywords