In this study, the magnetocaloric effect and transition temperature of bulk metallic glass, an amorphous material, were predicted through machine learning based on the composition features. From the Python module ‘Matminer’, 174 compositional features were obtained, and prediction performance was compared while reducing the composition features to prevent overfitting. After optimization using RandomForest, an ensemble model, changes in prediction performance were analyzed according to the number of compositional features. The R2 score was used as a performance metric in the regression prediction, and the best prediction performance was found using only 90 features predicting transition temperature, and 20 features predicting magnetocaloric effects. The most important feature when predicting magnetocaloric effects was the ‘Fe’ compositional ratio. The feature importance method provided by ‘scikit-learn’ was applied to sort compositional features. The feature importance method was found to be appropriate by comparing the prediction performance of the Fe-contained dataset with the full dataset.

In this study, based on the saturation magnetic flux density experimental values (Bs) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict Bs using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R2 score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict Bs above the threshold value of Bs (1.4 T). After obtaining two ANN models optimized for the R2 and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.

The bandgap characteristics of semiconductor materials are an important factor when utilizing semiconductor materials for various applications. In this study, based on data provided by AFLOW (Automatic-FLOW for Materials Discovery), the bandgap of a semiconductor material was predicted using only the material’s compositional features. The compositional features were generated using the python module of ‘Pymatgen’ and ‘Matminer’. Pearson’s correlation coefficients (PCC) between the compositional features were calculated and those with a correlation coefficient value larger than 0.95 were removed in order to avoid overfitting. The bandgap prediction performance was compared using the metrics of R2 score and root-mean-squared error. By predicting the bandgap with randomforest and xgboost as representatives of the ensemble algorithm, it was found that xgboost gave better results after cross-validation and hyper-parameter tuning. To investigate the effect of compositional feature selection on the bandgap prediction of the machine learning model, the prediction performance was studied according to the number of features based on feature importance methods. It was found that there were no significant changes in prediction performance beyond the appropriate feature. Furthermore, artificial neural networks were employed to compare the prediction performance by adjusting the number of features guided by the PCC values, resulting in the best R2 score of 0.811. By comparing and analyzing the bandgap distribution and prediction performance according to the material group containing specific elements (F, N, Yb, Eu, Zn, B, Si, Ge, Fe Al), various information for material design was obtained.

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256  256 pixels (high resolution: HR) from TEM measurements and 32  32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO4 nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

Iron oxides currently attract considerable attention due to their potential applications in the fields of lithiumion batteries, bio-medical sensors, and hyperthermia therapy materials. Magnetite (Fe3O4) is a particularly interesting research target due to its low cost, good biocompatibility, outstanding stability in physiological conditions. Hydrothermal synthesis is one of several liquid-phase synthesis methods with water or an aqueous solution under high pressure and high temperature. This paper reports the growth of magnetic Fe3O4 particles from iron powder (spherical, <10 μm) through an alkaline hydrothermal process under the following conditions: (1) Different KOH molar concentrations and (2) different synthesis time for each KOH molar concentrations. The optimal condition for the synthesis of Fe3O4 using Fe powders is hydrothermal oxidation with 6.25 M KOH for 48 h, resulting in 89.2 emu/g of saturation magnetization at room temperature. The structure and morphologies of the synthesized particles are characterized by X-ray diffraction (XRD, 2θ = 20°–80°) with Cu-kα radiation and field emission scanning electron microscopy (FE-SEM), respectively. The magnetic properties of magnetite samples are investigated using a vibrating sample magnetometer (VSM). The role of KOH in the formation of magnetite octahedron is observed.

Composites of P25 TiO2 and hexagonal WO3 nanorods are synthesized through ball-milling in order to study photocatalytic properties. Various composites of TiO2/WO3 are prepared by controlling the weight percentages (wt%) of WO3, in the range of 1–30 wt%, and milling time to investigate the effects of the composition ratio on the photocatalytic properties. Scanning electron microscopy, x-ray diffraction, and transmission electron microscopy are performed to characterize the structure, shape and size of the synthesized composites of TiO2/WO3. Methylene blue is used as a test dye to analyze the photocatalytic properties of the synthesized composite material. The photocatalytic activity shows that the decomposition efficiency of the dye due to the photocatalytic effect is the highest in the TiO2/ WO3 (3 wt%) composite, and the catalytic efficiency decreases sharply when the amount of WO3 is further increased. As the amount of WO3 added increases, dye-removal by adsorption occurs during centrifugation, instead of the decomposition of dyes by photocatalysts. Finally, TiO2/WO3 (3 wt%) composites are synthesized with various milling times. Experimental results show that the milling time has the best catalytic efficiency at 30 min, after which it gradually decreases. There is no significant change after 1 hour.

Transition-metal oxide semiconductors have various band gaps. Therefore, many studies have been conducted in various application fields. Among these, methods for the adsorption of organic dyes and utilization of photocatalytic properties have been developed using various metal oxides. In this study, the adsorption and photocatalytic effects of WO3 nanomaterials prepared by hydrothermal synthesis are investigated, with citric acid added in the hydrothermal process as a structure-directing agent. The nanostructures of WO3 are studied using transmission electron microscopy and scanning electron microscopy images. The crystal structure is investigated using X-ray diffraction patterns, and the changes in the dye concentrations adsorbed on WO3 nanorods are measured with a UV-visible absorption spectrophotometer based on Beer-Lambert’s law. The methylene blue (MB) dye solution is subjected to acid or base conditions to monitor the change in the maximum adsorption amount in relation to the pH. The maximum adsorption capacity is observed at pH 3. In addition to the dye adsorption, UV irradiation is carried out to investigate the decomposition of the MB dye as a result of photocatalytic effects. Significant photocatalytic properties are observed and compared with the adsorption effects for dye removal.