Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies

A polarization double-enhancement strategy to achieve super low energy consumption with ultra-high energy storage capacity in BCZT-based relaxor ferroelectrics

Due to dielectric capacitors' already-obtained fast charge-discharge speed, research has been focused on improving their Wrec. Increasing the polarization and enhancing the voltage endurance are efficient ways to reach higher Wrec, however simultaneous modification still seems a paradox. For example, in the ferroelectric-to-relaxor ferroelectric (FE-to-RFE) phase transition strategy, which has been widely used in the latest decade, electric breakdown strength (Eb) and energy storage efficiency (η) always increase, while at the same time, the maximum polarization (Pmax) inevitably decreases. The solution to this problem can be obtained from another degree of freedom, like defect engineering. By incorporating Bi(Zn2/3Ta1/3)O3 (BZT) into the Ba0.15Ca0.85Zr0.1Ti0.9O3 (BCZT) lattice to form (1 - x)Ba0.15Ca0.85Zr0.1Ti0.9O3-xBi(Zn2/3Ta1/3)O3 (BCZT-xBZT) solid-solution ceramics, in this work, ultrahigh ferroelectric polarization was achieved in BCZT-0.15BZT, which is caused by the polarization double-enhancement, comprising the contribution of interfacial and dipole polarization. In addition, due to the electron compensation, a Schottky contact formed at the interface between the electrode and the ceramic, which in the meantime, enhanced its Eb. A Wrec of 8.03 J cm-3, which is the highest among the BCZT-based ceramics reported so far, with an extremely low energy consumption, was finally achieved. BCZT-0.15BZT also has relatively good polarization fatigue after long-term use, good energy storage frequency stability and thermal stability, as well as excellent discharge properties.

Negative Capacitance Field Effect Transistors based on Van der Waals 2D Materials

Steep subthreshold swing (SS) is a decisive index for low energy consumption devices. However, the SS of conventional field effect transistors (FETs) has suffered from Boltzmann Tyranny, which limits the scaling of SS to sub-60 mV dec-1 at room temperature. Ferroelectric gate stack with negative capacitance (NC) is proved to reduce the SS effectively by the amplification of the gate voltage. With the application of 2D ferroelectric materials, the NC FETs can be further improved in performance and downscaled to a smaller dimension as well. This review introduces some related concepts for in-depth understanding of NC FETs, including the NC, internal gate voltage, SS, negative drain-induced barrier lowering, negative differential resistance, single-domain state, and multi-domain state. Meanwhile, this work summarizes the recent advances of the 2D NC FETs. Moreover, the electrical characteristics of some high-performance NC FETs are expressed as well. The factors which affect the performance of the 2D NC FETs are also presented in this paper. Finally, this work gives a brief summary and outlook for the 2D NC FETs.

Feature Selection in Machine Learning for Perovskite Materials Design and Discovery

Perovskite materials have been one of the most important research objects in materials science due to their excellent photoelectric properties as well as correspondingly complex structures. Machine learning (ML) methods have been playing an important role in the design and discovery of perovskite materials, while feature selection as a dimensionality reduction method has occupied a crucial position in the ML workflow. In this review, we introduced the recent advances in the applications of feature selection in perovskite materials. First, the development tendency of publications about ML in perovskite materials was analyzed, and the ML workflow for materials was summarized. Then the commonly used feature selection methods were briefly introduced, and the applications of feature selection in inorganic perovskites, hybrid organic-inorganic perovskites (HOIPs), and double perovskites (DPs) were reviewed. Finally, we put forward some directions for the future development of feature selection in machine learning for perovskite material design.

Ferroelasticity in Two-Dimensional Tetragonal Materials

Ferroelasticity is a prominent material property analogous to ferroelectricity and ferromagnetism, but its characteristic spontaneous structural polarization has remained less studied and poorly understood. Here, we use a high-throughput computation approach in conjunction with first-principles calculations to identify 65 (M=transition metal, X=nonmetal) monolayers exhibiting in-plane ferroelasticity out of 166 stable tetragonal monolayers. Molecular orbital theory analysis reveals that ferroelastic distortion arises when M-d/X-p and M-d/M-d couplings are both sufficiently weak. We have developed a physically interpretable one-dimensional descriptor that correctly predicts 89% of ferroelastics or nonferroelastics among the examined MX monolayers. Moreover, we find eleven MX compounds that exhibit strongly coupled ferroelasticity and magnetism driven by strain-controlled magnetocrystalline anisotropy, raising the prospects of developing 2D ferroelasticity-based multiferroics.

Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength

As a high-quality thermal barrier coating material, yttria-stabilized zirconia (YSZ) can effectively reduce the temperature of the collective materials to be used on the surface of gas turbine hot-end components. The bonding strength between YSZ and the substrate is also one of the most important factors for the applications. Herein, the Gaussian mixture model (GMM) and support vector regression (SVR) were used to construct a machine learning model between YSZ coating bonding strength and atmospheric plasma spraying (APS) process parameters. First, GMM was used to expand the original 8 data points to 400 with the R value of leave-one-out cross-validation improved from 0.690 to 0.990. Then, the specific effects of APS process parameters were explored through Shapley additive explanations and sensitivity analysis. Principal component analysis was used to explain the constructed model and obtain the optimized area with a high bonding strength. After experimental validation, the results showed that under the APS process parameters of a current of 617 A, a voltage of 65 V, a H₂ flow of 3 L min^-1, and a thickness of 200 μm, the bonding strength increased by more than 19% to 55.5 MPa compared with the original maximum value of 46.6 MPa, indicating that the constructed GMM-SVR model can accurately predict the bonding strength of YSZ coating.

Prediction of dielectric constants of ABO3-type perovskites using machine learning and first-principles calculations

In this study, the machine-learning method, combined with density functional perturbation theory (DFPT) calculations, was implemented to predict and validate the dielectric constants of ABO₃-type perovskites. For the construction of the training database, the dielectric constants of 7113 inorganic materials were extracted from the Materials Project. The chemical, structural, and physical descriptors were generated and trained using the gradient-boosting-based regressor after feature engineering. The prediction accuracies were 0.83 and 0.67 (R²) and 0.12 and 0.26 (root mean square error) for the electronic and ionic contributions to the dielectric constant, respectively. The constructed surrogate model was then employed to predict the dielectric constants of the ABO₃-type perovskites (216 structures), whose thermodynamic stabilities were satisfactory. The predicted values were validated using DFPT calculations. The constructed database was further used to develop a surrogate model for the prediction of dielectric constants. The final R² prediction accuracies reached 0.79 (electronic) and 0.67 (ionic).