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- W3088297452 abstract "• The GPR model is developed to predict lattice constants for spinel compounds. • The model uses ionic radii and electronegativities as descriptors. • Predictions based on the GPR agree well with experimental results. • The modeling approach demonstrates a high degree of accuracy and stability. Spinels can house a large variety of elements into the crystal structure. As a crystallographic parameter, the lattice constant, a, is highly sought in further investigations into materials properties. Experimental approaches to obtain the lattice constant are resource-intensive, and limit the exploration into non-synthesized spinels. Here, we develop the Gaussian process regression model to shed light on the relationship among ionic radii, electronegativities, and lattice constants. 167 samples with lattice constants between 8.044 Å and 11.660 Å are investigated. The model provides more accurate predictions than previous studies based on linear regressions, and statistical relationships between descriptors and the target." @default.
- W3088297452 created "2020-10-01" @default.
- W3088297452 creator A5019106763 @default.
- W3088297452 creator A5063944760 @default.
- W3088297452 date "2020-12-01" @default.
- W3088297452 modified "2023-09-24" @default.
- W3088297452 title "Machine learning lattice constants for spinel compounds" @default.
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- W3088297452 doi "https://doi.org/10.1016/j.cplett.2020.137993" @default.
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