Design method for regulating metal-insulator phase transition of rare earth nickel oxide
By employing first-principles calculations and machine learning methods, the challenge of regulating the metal-insulator phase transition in rare-earth nickel oxide superlattices was solved, enabling efficient and low-cost material design and optimization, and providing theoretical support for new materials.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANGHAI UNIV
- Filing Date
- 2024-08-06
- Publication Date
- 2026-06-19
AI Technical Summary
Existing technologies struggle to efficiently and cost-effectively control the metal-insulator phase transition of rare-earth nickel oxides, especially in rare-earth nickel oxide superlattices. The lack of in-depth understanding of the material's microstructure and electronic behavior leads to time-consuming and costly experimental control.
By employing first-principles calculations combined with machine learning methods, a rare-earth nickel oxide superlattice model was constructed for structural optimization and optical absorption spectroscopy analysis. Machine learning algorithms were then used to predict the metal-insulator phase transition behavior of the material, providing guidance for the design and optimization of superlattice materials.
This study achieved precise control over the superlattice of rare-earth nickel oxides, reduced experimental costs, improved computational efficiency, and provided theoretical guidance and design basis for the preparation of new materials.
Smart Images
Figure CN119007888B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of inorganic functional materials analysis and characterization, and in particular to a design method for regulating the metal-insulator phase transition of rare earth nickel oxides. Background Technology
[0002] Rare earth nickel oxides, as important functional materials, have shown broad application potential in catalysis, sensing, energy storage, and conversion. In particular, the metal-insulator phase transition characteristics of these materials have a decisive influence on their electronic and optical properties, making their regulation a hot topic in materials science research.
[0003] Rare earth nickelates (ReNiO3) are perovskite oxides with highly correlated d orbitals. Studies have shown that an increase in the Ni-O-Ni bond angle is associated with a decrease in the metal-insulator transition temperature of ReNiO3. The rich electronic properties of ReNiO3 make it suitable for a range of potential applications, including electronic devices, energy storage, and high-temperature superconductors.
[0004] Superlattices are complex structures formed by the periodic layering of two or more different materials. The scale and periodicity of these structures are tunable, endowing materials with many novel optical, thermal, electrical, and magnetic properties. When two different perovskite oxide films are stacked to form a superlattice, differences in lattice constants and symmetries lead to significant changes in their physical properties.
[0005] Traditional control methods rely primarily on experimental trial and error, a process that is time-consuming and costly. Furthermore, due to a lack of in-depth understanding of the material's microstructure and electronic behavior, these methods often struggle to achieve precise control. With the development of computational materials science, first-principles calculations have provided a powerful tool for understanding the atomic-scale behavior of materials. However, for complex material systems, such as rare-earth nickel oxide superlattices, simple first-principles calculations still face significant challenges in terms of computational cost and time.
[0006] Furthermore, although existing studies have explored the electronic structure of rare-earth nickel oxides to some extent using density functional theory (DFT), systematic research on their thickness and compositional diversity remains insufficient. At the same time, the calculation and prediction of optical properties, especially the optical response of superlattice structures, still require more efficient computational methods and tools. Summary of the Invention
[0007] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a design method for regulating the metal-insulator phase transition of rare-earth nickel oxides. This method clarifies the correlation between the metal-insulator phase transition of rare-earth nickel oxide superlattices and the thickness of the superlattice and the types of rare-earth elements. The atomic microstructure, electronic behavior, and optical absorption spectrum of the material are calculated using first-principles calculations, and then the band structure and optical properties are predicted using machine learning methods, providing a deeper understanding for the design and optimization of superlattice materials.
[0008] The objective of this invention can be achieved through the following technical solutions:
[0009] This invention provides a design method for regulating the metal-insulator phase transition of rare-earth nickel oxides, comprising the following steps:
[0010] S1: Construct a rare-earth nickel oxide superlattice model using the crystal structure visualization software Materials Studio, and convert it into a three-dimensional atomic coordinate file to achieve model construction;
[0011] S2: The surface structures of rare earth nickel oxide and strontium titanate were optimized using VASP software, and a superlattice model was obtained by merging them. The surface binding energy was calculated, and the interface energy calculation design was completed.
[0012] S3: The optimized system architecture is displayed through crystal structure visualization software, and charge density diagrams, density of states diagrams, band structure diagrams and optical absorption spectra are drawn. Machine learning algorithms are used to analyze and predict changes in material thickness and energy, and the results are processed and analyzed to guide experimental research and material optimization.
[0013] Furthermore, S1 specifically includes the following steps:
[0014] S1-1: Using Materials Studio software, a superlattice model was designed and constructed based on the crystal structure information of rare earth nickel oxides;
[0015] S1-2: After the model is built, use Materials Studio software to convert the superlattice model into a three-dimensional atomic coordinate file.
[0016] Furthermore, S1-1 specifically includes: selecting appropriate rare earth element types and nickel oxide layer thickness, as well as determining the periodicity and symmetry of the superlattice to ensure the physical meaning and chemical accuracy of the model;
[0017] Specifically, S1-2 includes: expressing the atomic position information in the model in numerical form, and the generated coordinate file will be used for structure optimization and electronic property calculation to ensure the accuracy and repeatability of the calculation.
[0018] Furthermore, S2 specifically includes:
[0019] S2-1: Individual structural optimization of the surface structures of rare earth nickel oxide and strontium titanate: Structural optimization was performed using VASP software, and the lowest energy state, i.e. the most stable geometric configuration, of the surface structure of rare earth nickel oxide and strontium titanate materials was determined by calculation.
[0020] S2-2: Superlattice Model Construction and Interface Energy Calculation: After obtaining the optimized stable surface structure, the surface structures of rare earth nickel oxide and strontium titanate are merged to form an initial model of rare earth nickel oxide / strontium titanate superlattice. Then, the merged superlattice model is further optimized to ensure that the atomic arrangement at the superlattice interface also reaches the lowest energy state.
[0021] S2-3: Calculate the surface binding energy of the optimized superlattice model.
[0022] Furthermore, in S2-1, the optimization process specifically includes: adjusting atomic positions and / or lattice parameters until convergence conditions are met, thereby ensuring the mechanical and thermodynamic stability of the structure;
[0023] Specifically, S2-3 includes: quantitatively analyzing the stability and bonding strength of the interface by evaluating the energy state at the interface between rare earth nickel oxide and strontium titanate in the superlattice.
[0024] Furthermore, S3 specifically includes:
[0025] S3-1: Using the crystal structure visualization software Materials Studio, the superlattice model of rare earth nickel oxide after structural optimization is displayed, and the charge density diagram, density of states diagram and band structure diagram of the system are further drawn to obtain the electronic structure and property information of the material.
[0026] S3-2: Calculate the optical absorption spectrum based on the optimized electronic structure and property information data file;
[0027] S3-3: Machine learning algorithms are used to process and analyze the obtained optical absorption spectrum data, quantitatively characterize the correlation between material thickness variation and energy variation, and predict the metal-insulator phase transition behavior of rare earth nickel oxide superlattices, thereby guiding experimental research and material optimization.
[0028] Furthermore, S3-3 specifically includes:
[0029] Machine learning algorithms were used to extract features and assess importance from data obtained through first-principles calculations, including: calculating the superlattice correlation coefficient matrix based on the Pearson correlation coefficient, and identifying key factors affecting the metal-insulator phase transition of rare-earth nickel oxide superlattices.
[0030] Based on the results of feature analysis, a machine learning model is trained, and the thickness and energy changes of rare earth nickel oxide superlattice materials are quantitatively characterized by the machine learning model. The trained model is then used to predict the metal-insulator phase transition behavior of superlattice materials with different thicknesses and rare earth element types, providing guidance for material design and optimization.
[0031] Furthermore, the machine learning model includes at least one of the following algorithm models: Bagging, GBR, RF, and XGBoost.
[0032] Furthermore, in S1-2, the calculation process for the electronic characteristics includes:
[0033] The process involves structural optimization, bandgap calculation, optical property calculation, and finally machine learning.
[0034] The electronic property calculations used the type of rare earth element and the thickness of the nickel oxide layer as variables to examine the influence of these variables on the metal-insulator phase transition of rare earth nickel oxides.
[0035] Furthermore, in S3-1, the band structure diagram is generated by setting the connection mode in the reciprocal space high symmetry point file and performing analysis and calculation based on the self-consistent charge density.
[0036] The optical property calculation is based on the wavefunction file, obtaining the dielectric constant matrix, and then combining the real and imaginary parts of the dielectric constant to calculate the absorption spectrum.
[0037] Compared with the prior art, the present invention has the following technical advantages:
[0038] (1) The present invention is based on first-principles calculations. It requires basic crystal structure information of each element in the rare earth nickel oxide material system, but does not require other additional parameters, and calculates information such as the spatial structure, electronic state, and total energy of the system.
[0039] (2) This invention reveals the important factors that regulate the metal-insulator phase transition of rare earth nickel oxides through calculation. It can make necessary supplements to the experimental study of the metal-insulator phase transition of rare earth nickel oxide superlattices, and can also provide theoretical guidance and design basis for the preparation of new materials.
[0040] (3) This invention employs four different machine learning fitting models to evaluate the feature importance of the factors that regulate the metal-insulator phase transition of rare earth nickel oxide superlattices. This can accurately and quantitatively characterize the correlation of the regulatory factors and provide a broad perspective for the preparation of new materials.
[0041] (4) This invention uses experimental equipment that only requires a computer and does not require experimental raw materials, which greatly reduces experimental costs and has high computational efficiency and easy process control. Attached Figure Description
[0042] Figure 1 This is a flowchart illustrating the entire design method in the embodiment.
[0043] Figure 2 (a) Figure 2 (b) Figure 2 (c) is a schematic diagram of the thickness setting of rare earth nickel oxide superlattice layer.
[0044] Figure 3 This is a schematic diagram showing the band gap width of rare earth nickel oxide superlattices of different thicknesses.
[0045] Figure 4 This is a schematic diagram (n) showing the light absorption data of rare earth nickel oxide superlattices in the infrared, visible, near-ultraviolet, and far-ultraviolet light ranges.
[0046] Figure 5 This is the correlation coefficient matrix of rare earth nickel oxide superlattices obtained based on Pearson calculations. Detailed Implementation
[0047] Overall, this invention discloses a method for controlling the metal-insulator phase transition of rare-earth nickel oxides, including the construction of rare-earth nickel oxide / strontium titanate superlattices with different atomic layer numbers and the screening of rare-earth element types for superlattices from light rare-earth to heavy rare-earth. This invention clarifies the influence of rare-earth element types and atomic layer thickness on the superlattice band gap width, and characterizes the correlation between the intrinsic physical parameters and electrical / optical properties of the rare-earth nickel oxide / strontium titanate superlattice through machine learning, providing a deeper understanding for the design and optimization of superlattice materials and the control of their electrical and optical properties.
[0048] In practice, the method for regulating the phase transition of rare earth nickel oxide metal-insulator includes the following steps:
[0049] S1. Construction of the rare earth nickel oxide / strontium titanate superlattice model: The rare earth nickel oxide superlattice model is constructed using crystal structure visualization software, and then the superlattice model is converted into a three-dimensional atomic coordinate file using the same software.
[0050] S2. Calculation and design of the interface energy of rare earth nickel oxide / strontium titanate superlattice: The surface structures of rare earth nickel oxide and strontium titanate are set as input files respectively. The structure of the material is optimized to obtain coordinate files of two stable material surface structures. The two stable material surface structures are merged and used as the input file of rare earth nickel oxide / strontium titanate superlattice. The optimized structure of the material is calculated, and the surface binding energy of rare earth nickel oxide superlattice is analyzed and calculated.
[0051] S3. Results Processing and Analysis: The optimized system architecture can be displayed using crystal structure visualization software to analyze changes in bond lengths, bond angles, and lattice constants. Charge density diagrams, density of states diagrams, band structure diagrams, and optical absorption spectra are plotted. The types of rare earth elements in superlattices, ranging from light to heavy rare earth elements, are screened. The surface binding energy and band gap width of nickel oxide superlattices with different thicknesses and different rare earth elements are calculated. Machine learning is used to process the data, analyze the thickness and energy changes of the material, and analyze and further predict its metal-insulator phase transition, providing theoretical guidance for regulating the metal-insulator phase transition of nickel oxides.
[0052] In practice, the crystal structure visualization software used is Materials Studio, the electronic property calculation software is VASP, and the calculation software used for band prediction is PYTHON.
[0053] In practice, the calculation process for electronic properties is as follows: first, structural optimization is performed, then static self-consistent calculation is performed, then property calculation is performed based on the generated charge density data file, and finally optical property calculation is performed.
[0054] In practice, the above-mentioned design method for regulating the metal-insulator phase transition of rare earth nickel oxide adopts a screening method. The screening method is based on the first principle calculation of density functional theory, and the screening object is a superlattice model with different rare earth element types and nickel oxide layer thickness.
[0055] In practice, the above electronic property calculations use the type of rare earth element and the thickness of the nickel oxide layer as variables to examine the influence of these variables on the metal-insulator phase transition of rare earth nickel oxide.
[0056] In practice, the above-mentioned design method for regulating the metal-insulator phase transition of rare earth nickel oxides is based on the interaction between ions and valence electrons, and is described by the method of adding plane waves. The exchange correlation functional adopts the generalized gradient approximation method.
[0057] In practical implementation, the above-mentioned design method for regulating the metal-insulator phase transition of rare earth nickel oxides is structurally optimized. During calculation, the high symmetry point is generated using the reciprocal space origin method. During structural optimization, the high symmetry point of rare earth nickel oxide is set to 5×5×3, and the high symmetry point of strontium titanate is set to 8×8×8.
[0058] In practice, the above band structure diagram is generated by setting the connection mode in a reciprocal space high symmetry point file; analysis and calculation are performed based on the self-consistent charge density.
[0059] In practice, the above optical property calculations are based on the wavefunction file to obtain the dielectric constant matrix, and then the absorption spectrum is calculated by combining the real and imaginary parts of the dielectric constant.
[0060] In practice, the above machine learning method for band prediction uses a superlattice correlation coefficient matrix obtained based on Pearson calculation, and evaluates the feature importance using Bagging, GBR, RF and XGBoost algorithm models.
[0061] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Component models, material names, connection structures, control methods, algorithms, and other features not explicitly described in this technical solution are considered common technical features disclosed in the prior art.
[0062] Example 1
[0063] The design method for regulating the metal-insulator phase transition of rare-earth nickel oxides in this embodiment specifically includes:
[0064] S1, Construction of the rare earth nickel oxide / strontium titanate superlattice model: The rare earth nickel oxide superlattice model was constructed using crystal structure visualization software, and then the superlattice model was converted into a three-dimensional atomic coordinate file using the same software.
[0065] S2, Calculation and design of interface energy of rare earth nickel oxide / strontium titanate superlattice: The surface structures of rare earth nickel oxide and strontium titanate are set as input files respectively, and the structure of the material is optimized to obtain coordinate files of two stable material surface structures; the above two stable material surface structures are merged and used as input files of rare earth nickel oxide / strontium titanate superlattice, and the optimized structure of the material is calculated. The surface binding energy of rare earth nickel oxide superlattice is analyzed and calculated.
[0066] S3. Results Processing and Analysis: The optimized system architecture can be displayed using crystal structure visualization software to analyze changes in bond lengths, bond angles, and lattice constants. Charge density maps, density of states maps, band structure maps, and optical absorption spectra are plotted. Superlattice rare earth element types from light rare earth to heavy rare earth are screened. The surface binding energy and band gap width of nickel oxide superlattices with different thicknesses and different rare earth elements are calculated. Machine learning is used to process the data, analyze the thickness and energy changes of the material, analyze and further predict its metal-insulator phase transition, and provide theoretical guidance for regulating the metal-insulator phase transition of nickel oxides.
[0067] In this embodiment, the crystal structure visualization software is Materials Studio, and the electronic property calculation software is VASP (Vienna Ab-initio Simulation Package). This software is simple to operate, accurate, and relatively universal.
[0068] In this embodiment, the computational software for the aforementioned machine learning calculations is Python software. This software is easy to operate, has a rich database, and good accuracy, making it a relatively common software.
[0069] In this embodiment, the calculation process for the above-mentioned electronic properties is as follows: First, the material structure is optimized, then static self-consistent calculation is performed, and then property calculation is performed based on the generated charge density data file to minimize the energy of the model structure, ensure the correctness and accuracy of each property calculation, and prevent sudden changes in the physical properties of the system.
[0070] In this embodiment, the above-mentioned design method for regulating the metal-insulator phase transition of rare earth nickel oxide adopts a screening method. The screening method is based on the first principle of density functional theory. The screening objects are the number of layers of the rare earth nickel oxide superlattice and the types of rare earth elements. Considering that the types of elements and the number of layers have a profound impact on its metal-insulator phase transition, the scientific nature of the screening is improved.
[0071] In this embodiment, the above electronic property calculations use the number of superlattice layers and the types of rare earth elements as variables to examine the influence of these two variables on the metal-insulator phase transition of rare earth nickel oxides, thus improving computational efficiency. The design method for controlling the metal-insulator phase transition of the rare earth nickel oxide superlattice model describes the interaction between ions and valence electrons using a plane wave approach, and employs the generalized gradient approximation method for the exchange-correlation functional, ensuring that the computational results of the system achieve sufficient accuracy within a controllable computational range.
[0072] In this embodiment, the above-mentioned design method for regulating the metal-insulator phase transition of the rare-earth nickel oxide superlattice model is used to optimize the structure by generating high symmetry points using the reciprocal space origin method during calculation. This method can quickly generate grid points and avoid special points, thereby improving computational efficiency.
[0073] In this embodiment, the above-mentioned band structure diagram is obtained by setting the connection mode in the reciprocal space high symmetry point input file and performing analysis and calculation based on the self-consistent charge density. This method can automatically insert a specified number of high symmetry points between the start and end points to improve the calculation accuracy.
[0074] In this embodiment, the above optical property calculation is based on the wave function, and calculation parameters are added according to the material itself to obtain a dielectric constant matrix. Then, the absorption spectrum is calculated by combining the real and imaginary parts of the dielectric constant.
[0075] In this embodiment, the above machine learning analysis and processing calculation is based on the Pearson correlation coefficient, and the characteristic correlation coefficient is added according to the material itself to obtain the characteristic value correlation mapping of the band.
[0076] As can be seen, the features of this invention are as follows: (1) Rare earth nickel oxide superlattice setting: Based on the first-principles calculation method of density functional theory, the rare earth nickel oxide superlattice structure is geometrically optimized, and relevant data read from the datasheet is used for writing. (2) Convergence test of the model: Several superlattice models composed of different rare earth elements are constructed, and rare earth nickel oxide layers of different thicknesses are set in each model. The convergence test of the superlattice model is carried out. During the test, the valence electrons of the material are clearly calculated, the corresponding plane wave cutoff energy is selected according to the material, the grid size of the reciprocal space high symmetry point is selected, and the convergence criteria of the interatomic interaction force and the energy convergence criteria are modified. (3) Calculation of superlattice structures of different thicknesses: The surface binding energy of each structure is calculated to ensure that a stable state is achieved in accordance with the basic physical principles. (4) Based on the dielectric constant matrix obtained by calculation, and combined with the real and imaginary parts of the dielectric constant, the absorption spectrum is calculated. (5) Based on the band data obtained by previous calculation, machine learning simulation is performed to obtain the Pearson correlation coefficient matrix. Its correlation is clarified.
[0077] Based on the data on rare earth nickel oxides obtained from the datasheet, compile the calculation input file, such as... Figure 1 As shown, three superlattice models with different rare-earth nickel-oxygen layer thicknesses were constructed, and their structures are as follows. Figure 2As shown, ten different rare earth elements were then used to replace the elements, constructing thirty superlattice models with different elements and thicknesses. The structural convergence of the models was then tested. Sufficient plane wave cutoff energy was selected based on the dopant element size during the test. When calculating the superlattice, the reciprocal space grid size was set to 4×4×3, 4×4×2, and 3×3×1 for layers 1, 2, and 3, respectively. The convergence criterion for interatomic interaction forces was... The energy convergence criterion is 1.0 × 10⁻⁶. -5 eV / atom. Band gap widths of rare-earth nickel oxide superlattices with different layer thicknesses, such as... Figure 3 As shown, the light absorption data of rare earth nickel oxide superlattices in the infrared, visible, near-ultraviolet, and far-ultraviolet light ranges are as follows: Figure 4 As shown, the correlation coefficient matrix of rare-earth nickel oxide superlattices obtained after machine learning processing based on Pearson calculation is as follows: Figure 5 As shown.
[0078] First, we examined superlattice models with different layer thicknesses for each element and incorporated them into the design calculations of a rare-earth nickel oxide superlattice model. Second, we used density functional theory (DFT) first-principles calculations to verify and determine the accuracy and stability of each superlattice model. Third, we performed electronic and optical calculations to examine the influence of atomic microstructure on its metal-insulator phase transition. Finally, we conducted machine learning analysis, revealing that the electrical properties of the superlattice are highly sensitive to the type of rare-earth element. In the spectrum of rare-earth elements from light to heavy, the rare-earth nickel oxide superlattice exhibits a transition from metal to insulator. Furthermore, the band gap of the superlattice decreases with increasing rare-earth nickel oxide layer thickness. Machine learning analysis has determined that the superlattice thickness and interfacial strain are key determinants of the band gap. In addition, the light absorption coefficient is positively correlated with the band gap and in-plane lattice constant, but negatively correlated with the total superlattice thickness. This invention clarifies the influence of rare earth elements and layer thickness on the band gap width of rare earth nickel oxide superlattices. The correlation between microstructure and electronic behavior is characterized using machine learning, providing a deeper understanding for the design and optimization of superlattice materials and the control of their electrical and optical properties. The screening method is based on first-principles calculations using density functional theory, and the screening targets are superlattice models with different rare earth elements and nickel oxide layer thicknesses. The convergence of the model size is measured, and the influence of microstructure on the metal-insulator phase transition of rare earth nickel oxides is examined and controlled, using rare earth elements and the thickness of the rare earth nickel oxide layer as variables.
[0079] The above description of the embodiments is provided to enable those skilled in the art to understand and use the invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.
Claims
1. A design method for regulating the metal-insulator phase transition of rare earth nickel oxides, characterized in that, Includes the following steps: S1: Construct a rare-earth nickel oxide superlattice model using the crystal structure visualization software Materials Studio, and convert it into a three-dimensional atomic coordinate file to achieve model construction; S2: The surface structures of rare earth nickel oxide and strontium titanate were optimized using VASP software, and a superlattice model was obtained by merging them. The surface binding energy was calculated, and the interface energy calculation design was completed. S3: The optimized system architecture is displayed through crystal structure visualization software, and charge density diagram, density of states diagram, band structure diagram and optical absorption spectrum are drawn. Machine learning algorithms are used to analyze and predict the changes in material thickness and energy, and the results are processed and analyzed to guide experimental research and material optimization. In S1, after being converted into a three-dimensional atomic coordinate file, electronic property calculations are also included. The specific process includes: structural optimization, band gap calculation, optical property calculation, and finally machine learning. The electronic property calculations used the type of rare earth element and the thickness of the nickel oxide layer as variables to examine the influence of these variables on the metal-insulator phase transition of rare earth nickel oxides. S3 specifically includes: S3-1: Using the crystal structure visualization software Materials Studio, the superlattice model of rare earth nickel oxide after structural optimization is displayed, and the charge density diagram, density of states diagram and band structure diagram of the system are further drawn to obtain the electronic structure and property information of the material. S3-2: Calculate the optical absorption spectrum based on the optimized electronic structure and property information data file; S3-3: Machine learning algorithms are used to process and analyze the obtained optical absorption spectrum data, quantitatively characterize the correlation between material thickness variation and energy variation, and predict the metal-insulator phase transition behavior of rare earth nickel oxide superlattices, thereby guiding experimental research and material optimization. S3-3 specifically includes: Machine learning algorithms were used to extract features and assess importance from data obtained through first-principles calculations, including: calculating the superlattice correlation coefficient matrix based on the Pearson correlation coefficient, and identifying key factors affecting the metal-insulator phase transition of rare-earth nickel oxide superlattices. Based on the results of feature analysis, a machine learning model is trained, and the thickness and energy changes of rare earth nickel oxide superlattice materials are quantitatively characterized by the machine learning model. The trained model is then used to predict the metal-insulator phase transition behavior of superlattice materials with different thicknesses and rare earth element types, providing guidance for material design and optimization.
2. The design method for regulating the metal-insulator phase transition of rare earth nickel oxides according to claim 1, characterized in that, S1 specifically includes the following steps: S1-1: Using Materials Studio software, a superlattice model was designed and constructed based on the crystal structure information of rare earth nickel oxides; S1-2: After the model is built, use Materials Studio software to convert the superlattice model into a three-dimensional atomic coordinate file.
3. The design method for regulating the metal-insulator phase transition of rare earth nickel oxides according to claim 2, characterized in that, In S1-1, the specific steps include: screening rare earth elements and nickel oxide layer thickness, as well as determining the periodicity and symmetry of the superlattice to ensure the physical meaning and chemical accuracy of the model. Specifically, S1-2 includes: expressing the atomic position information in the model in numerical form, and the generated coordinate file will be used for structure optimization and electronic property calculation to ensure the accuracy and repeatability of the calculation.
4. The design method for regulating the metal-insulator phase transition of rare earth nickel oxides according to claim 1, characterized in that, S2 specifically includes: S2-1: Individual structural optimization of the surface structures of rare earth nickel oxide and strontium titanate: Structural optimization was performed using VASP software, and the lowest energy state, i.e. the most stable geometric configuration, of the surface structure of rare earth nickel oxide and strontium titanate materials was determined by calculation. S2-2: Superlattice Model Construction and Interface Energy Calculation: After obtaining the optimized stable surface structure, the surface structures of rare earth nickel oxide and strontium titanate are merged to form an initial model of rare earth nickel oxide / strontium titanate superlattice. Then, the merged superlattice model is further optimized to ensure that the atomic arrangement at the superlattice interface also reaches the lowest energy state. S2-3: Calculate the surface binding energy of the optimized superlattice model.
5. The design method for regulating the metal-insulator phase transition of rare earth nickel oxides according to claim 1, characterized in that, In S2-1, the specific process of structural optimization using VASP software includes: adjusting atomic positions and / or lattice parameters until convergence conditions are met, thereby ensuring the mechanical and thermodynamic stability of the structure. Specifically, S2-3 includes: quantitatively analyzing the stability and bonding strength of the interface by evaluating the energy state at the interface between rare earth nickel oxide and strontium titanate in the superlattice.
6. The design method for regulating the metal-insulator phase transition of rare earth nickel oxides according to claim 1, characterized in that, The machine learning model includes at least one of the following algorithms: Bagging, GBR, RF, and XGBoost.
7. The design method for regulating the metal-insulator phase transition of rare earth nickel oxides according to claim 1, characterized in that, In S3-1, the band structure diagram is generated by setting the connection mode in the reciprocal space high symmetry point file and performing analysis and calculation based on the self-consistent charge density. The optical property calculation is based on the wavefunction file, obtaining the dielectric constant matrix, and then combining the real and imaginary parts of the dielectric constant to calculate the absorption spectrum.