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Prediction of CI, MLR, and SPuDS models for novel cubic compounds. Table 3 transition of cubic structure with a = 3.971 A at Curie temperature of 763 K [38]. A single crystal of PbZrO3, at room temperature, has orthorhombic structure with a=8.231A, b=11.77A, and c=5.881 A [39]. However, at high temperature, this compound undergoes a phase transition to cubic structure with a= 4.176 A [38]. More orthorhombic compounds of SrRuO3 [37], CdGeO3 [40], CaGeO3 [41,42], and LaNiO3 [43] are also included in Table 3. Previously, CaGeO3 and CdGeO3 compounds are reported as cubic with a= 3.723 A and a=3.74A respectively [40]. However, these compounds are claimed to be orthorhombic, with a=5.261 A; b=5.268 A; c=7.44A for CaGeO3 and a=5.261A; b=5.268A; c=7.44 A for CdGeO3 [42]. Therefore, their pseudocubic LC is com- puted to be a= 3.792 A (CaGeO3) and a= 3.785 A (CdGeOs) using y/1/4abc [44]. Simple ruthenium based oxides (ARuO;: A = Ba, Sr, Ca) have received attention due to appealing magnetic properties [37]. Both SrRuO3 (a= 5.571 A; b =5.535 A; c=7.85 A) and CaRuO3 (a = 5.357 A; b = 5.533 A; c = 7.663 A) compounds have orthorhom- bic structure [37]. LC values of their pseudocubic perovskite struc- tures are computed, using the same Eq. in [44], to be a = 3.84 A and a= 3.92 A, respectively.

Table 3 Prediction of CI, MLR, and SPuDS models for novel cubic compounds. Table 3 transition of cubic structure with a = 3.971 A at Curie temperature of 763 K [38]. A single crystal of PbZrO3, at room temperature, has orthorhombic structure with a=8.231A, b=11.77A, and c=5.881 A [39]. However, at high temperature, this compound undergoes a phase transition to cubic structure with a= 4.176 A [38]. More orthorhombic compounds of SrRuO3 [37], CdGeO3 [40], CaGeO3 [41,42], and LaNiO3 [43] are also included in Table 3. Previously, CaGeO3 and CdGeO3 compounds are reported as cubic with a= 3.723 A and a=3.74A respectively [40]. However, these compounds are claimed to be orthorhombic, with a=5.261 A; b=5.268 A; c=7.44A for CaGeO3 and a=5.261A; b=5.268A; c=7.44 A for CdGeO3 [42]. Therefore, their pseudocubic LC is com- puted to be a= 3.792 A (CaGeO3) and a= 3.785 A (CdGeOs) using y/1/4abc [44]. Simple ruthenium based oxides (ARuO;: A = Ba, Sr, Ca) have received attention due to appealing magnetic properties [37]. Both SrRuO3 (a= 5.571 A; b =5.535 A; c=7.85 A) and CaRuO3 (a = 5.357 A; b = 5.533 A; c = 7.663 A) compounds have orthorhom- bic structure [37]. LC values of their pseudocubic perovskite struc- tures are computed, using the same Eq. in [44], to be a = 3.84 A and a= 3.92 A, respectively.

Related Figures (12)

Fig. 1. Block diagram for developing CI based computational models.
Fig. 1. Block diagram for developing CI based computational models.
Prediction of CI models using cubic compounds for training data.
Prediction of CI models using cubic compounds for training data.
Table 1 (continued)
Table 1 (continued)
Prediction of CI models using cubic compounds for validation data.
Prediction of CI models using cubic compounds for validation data.
* For monoclinic perovskites, Mean PAD is determined by taking the mean of PAD values of lattice parameters a, b and c. However, overall Mean PAD (1.064) of MLR models is computed from the Mean PADa = 1.0343, Mean PADb = 1.285, and Mean PADc = 0.8749 (see supplementary Table 7). > To compare the prediction performance, for ABX3-type cubic compounds, the following developed MLR models are used; aypr = 0.06742 + 0.49533(rq + rx) + 1.2856(rg +Trx), Jiang et al. [6]. dyrp = 0.7785 + 1.8218(rg + rx) + 1.1359t, Moreira et al. [7]. dyiz = 0.06741 + 0.49052(ra + ry) + 1.29212(rg + ry), R. Ubic [8]. Overall prediction of CI and MLR models for cubic and monoclinic compounds. Table 5
* For monoclinic perovskites, Mean PAD is determined by taking the mean of PAD values of lattice parameters a, b and c. However, overall Mean PAD (1.064) of MLR models is computed from the Mean PADa = 1.0343, Mean PADb = 1.285, and Mean PADc = 0.8749 (see supplementary Table 7). > To compare the prediction performance, for ABX3-type cubic compounds, the following developed MLR models are used; aypr = 0.06742 + 0.49533(rq + rx) + 1.2856(rg +Trx), Jiang et al. [6]. dyrp = 0.7785 + 1.8218(rg + rx) + 1.1359t, Moreira et al. [7]. dyiz = 0.06741 + 0.49052(ra + ry) + 1.29212(rg + ry), R. Ubic [8]. Overall prediction of CI and MLR models for cubic and monoclinic compounds. Table 5
Optimal parameters selection for Cl computational Models. Table 4
Optimal parameters selection for Cl computational Models. Table 4
Fig. 2. Correlation between experimental and predicted LC of SVR model using cubic compounds for training data.
Fig. 2. Correlation between experimental and predicted LC of SVR model using cubic compounds for training data.
* As reported in [22], LC values of compounds in the first seven rows are computed using DFT tools of GPAW and DACAPO. Table 6
* As reported in [22], LC values of compounds in the first seven rows are computed using DFT tools of GPAW and DACAPO. Table 6
Fig. 3. Correlation between experimental and predicted LC of SVR model using cubic compounds for validation data.
Fig. 3. Correlation between experimental and predicted LC of SVR model using cubic compounds for validation data.
Fig. 4. Correlation between experimental and predicted LC of SVR model for monoclinic compounds.
Fig. 4. Correlation between experimental and predicted LC of SVR model for monoclinic compounds.
Prediction of CI models using monoclinic compounds for training data. Table 7
Prediction of CI models using monoclinic compounds for training data. Table 7
Prediction of CI models using monoclinic compounds for validation data.
Prediction of CI models using monoclinic compounds for validation data.
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Materials EngineeringCondensed Matter PhysicsMaterials ScienceComputational Materials ScienceSupport Vector RegressionGeneral Regression Neural Network
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