In this work,a machine learning(ML)model was created to predict intrinsic hardness of various compounds using their crystal chemistry.For this purpose,an initial dataset,containing the hardness values of 270 compounds...In this work,a machine learning(ML)model was created to predict intrinsic hardness of various compounds using their crystal chemistry.For this purpose,an initial dataset,containing the hardness values of 270 compounds and counterpart applied loads,was employed in the learning process.Based on various features generated using crystal information,an ML model,with a high accuracy(R^(2)=0.942),was built using extreme gradient boosting(XGB)algorithm.Experimental validations conducted by hardness measurements of various compounds,including MSi_(2)(M=Nb,Ce,V,and Ta),Al_(2)O_(3),and FeB_(4),showed that the XGB model was able to reproduce load-dependent hardness behaviors of these compounds.In addition,this model was also used to predict the behavior based on prototype crystal structures that are randomly substituted with elements.展开更多
基金This research was supported by National Research Foundation(NRF)of South Korea(2020R1A2C1004720).
文摘In this work,a machine learning(ML)model was created to predict intrinsic hardness of various compounds using their crystal chemistry.For this purpose,an initial dataset,containing the hardness values of 270 compounds and counterpart applied loads,was employed in the learning process.Based on various features generated using crystal information,an ML model,with a high accuracy(R^(2)=0.942),was built using extreme gradient boosting(XGB)algorithm.Experimental validations conducted by hardness measurements of various compounds,including MSi_(2)(M=Nb,Ce,V,and Ta),Al_(2)O_(3),and FeB_(4),showed that the XGB model was able to reproduce load-dependent hardness behaviors of these compounds.In addition,this model was also used to predict the behavior based on prototype crystal structures that are randomly substituted with elements.
