Superconducting transition temperature detection system and method

Abstract

The invention provides a superconducting transition temperature detection system and method, relates to the technical field of superconducting transition temperature measurement, and solves the technical problems that in the prior art, superconducting transition temperature detection needs manual intervention, and the automation level is low. The system specifically comprises a first module, a second module and a third module which are sequentially cascaded, the first module is used for generating an input file which is adaptive to the superconducting property calculation step and has consistent parameters; the superconducting property calculation step comprises structural relaxation, self-consistent field calculation, phonon calculation, non-self-consistent calculation and superconducting density functional theory solving; the second module is used for calculating and outputting the electroacoustic coupling matrix and coulomb interaction data based on the input file; and the third module is used for calling the SCDFT solver based on the electroacoustic coupling matrix and coulomb interaction data, outputting the superconducting transition temperature and generating a Fermi surface energy gap visualization file. The method is used for measuring the superconducting transition temperature.

Description

Technical Field

[0001] This application relates to the field of superconducting transition temperature measurement technology, and in particular to a superconducting transition temperature detection system and method. Background Technology

[0002] Accurate and efficient prediction of the superconducting transition temperature of superconducting materials is crucial for high-throughput screening and research. Existing technologies mainly rely on a combination of density functional theory (DFT) and superconducting density functional theory (SCDFT) calculation methods. By using software packages such as Quantum ESPRESSO (QE) and Superconducting-Toolkit (SCTK) in series, researchers manually complete a series of processes, including crystal structure file format conversion, input file writing for each calculation step, structural relaxation, convergence testing of key parameters, phonon and electroacoustic coupling calculation, shielded Coulomb interaction calculation, and SCDFT equation solving. The entire process requires manual intervention in task connection, data transfer, and task submission. The operation is complex and prone to errors due to inconsistent parameters, incorrect file paths, etc. At the same time, it requires a high level of theoretical knowledge and operational skills from the operator, resulting in low computational efficiency and throughput, and poor reproducibility of results. Therefore, existing technologies suffer from the technical problems of requiring manual intervention for superconducting transition temperature detection and low level of automation. Summary of the Invention

[0003] This application provides a superconducting transition temperature detection system and method, which solves the technical problems of existing technologies that require manual intervention and have low automation levels in superconducting transition temperature detection.

[0004] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, a superconducting transition temperature detection system is provided, comprising: a first module, a second module, and a third module cascaded in sequence; the first module is used to generate an input file that is compatible with the calculation steps of superconducting properties and has consistent parameters; the calculation steps of superconducting properties include structural relaxation, self-consistent field calculation, phonon calculation, non-self-consistent calculation, and superconducting density functional theory solution; the second module is used to calculate and output the electro-acoustic coupling matrix and Coulomb interaction data based on the input file; the third module is used to call the SCDFT solver based on the electro-acoustic coupling matrix and Coulomb interaction data, output the superconducting transition temperature, and generate a Fermi level bandgap visualization file.

[0005] In conjunction with the first aspect mentioned above, in one possible implementation, the first module includes: a structure analysis and conversion unit and a parameterization generation unit; the structure analysis and conversion unit is used to identify the structure file of the superconducting material and call the Phonopy tool to find the lattice unit; the parameterization generation unit has built-in expert knowledge and is used to receive the configuration file and generate the input file by combining the structure information of the lattice unit with the expert knowledge; the expert knowledge sets different densities of k-point grids that are compatible with each other for different computational tasks.

[0006] In conjunction with the first aspect mentioned above, in one possible implementation, the second module includes: a core computing unit, a closed-loop convergence testing unit, a job dependency management unit, and a data flow management unit; the core computing unit is used to sequentially execute structural relaxation, calculate the electroacoustic coupling matrix, and calculate Coulomb interaction data; the closed-loop convergence testing unit is used to generate multiple sets of self-consistent field calculation tasks with different plane wave cutoff energies, analyze the total energy, determine the optimal plane wave cutoff energy value according to a preset convergence threshold, and update it to subsequent input files; the job dependency management unit is used to submit tasks according to the computational logic order of the core computing unit, and submit subsequent dependent steps only after the previous steps are successful; the data flow management unit is used to create a standard working directory for each step of the core computing unit.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, the third module includes: a data aggregation unit, a Tc solving unit, and a result extraction and output unit; the data aggregation unit is used to acquire the electro-acoustic coupling matrix and Coulomb interaction data, and to perform correlation and integration; the Tc solving unit is used to call the SCDFT solver, iteratively adjust the temperature, and obtain the superconducting transition temperature; the result extraction and output unit is used to generate a Fermi surface bandgap visualization file based on the superconducting transition temperature.

[0008] Secondly, a method for detecting the superconducting transition temperature is provided, implemented using a scripting language. This method includes: acquiring the structure file and configuration file of the superconducting material; the structure file is a standard crystal structure file in CIF or POSCAR format, used to provide information on the atomic arrangement and periodicity of the superconducting material; the configuration file is a file recording user-defined global parameters; the global parameters include pressure, magnetism, and spin-orbit coupling parameters; generating an input file based on the structure file and configuration file; performing structural relaxation, parameter convergence testing, calculating the electro-acoustic coupling matrix, and calculating Coulomb interaction data sequentially based on the input file; aggregating the electro-acoustic coupling matrix and Coulomb interaction data, calling the SCDFT solver, and solving for the superconducting transition temperature using the bisection method.

[0009] In conjunction with the second aspect mentioned above, in one possible implementation, after aggregating the electro-acoustic coupling matrix and Coulomb interaction data, calling the SCDFT solver, and solving the superconducting transition temperature using the bisection method, the method further includes: generating a Fermi surface bandgap visualization file based on the superconducting transition temperature.

[0010] In conjunction with the second aspect mentioned above, in one possible implementation, an input file is generated based on the structure file and configuration file. This includes: identifying the structure file and calling the Phonopy tool to find the lattice unit cells; extracting the global parameters from the configuration file; based on the global parameters and the structural information of the lattice unit cells, combined with built-in expert knowledge, setting multi-level k-point grid configurations of different densities that are compatible with each other for structural relaxation, self-consistent field calculation, phonon calculation, non-self-consistent calculation, and superconducting density functional theory solutions; generating an input file that adapts to all calculation steps; the prefix, lattice parameters, and atomic coordinate basic information in the input file are consistent, and it embeds multi-level k-point grid configurations of different densities that are compatible with each other.

[0011] In conjunction with the second aspect above, in one possible implementation, a multi-level k-point grid configuration with different densities and mutual compatibility includes: setting a coarse q-point grid for phonon calculations; setting a medium-density k-point grid for self-consistent field calculations; setting a fine k-point grid for non-self-consistent calculations; and satisfying an integer multiple relationship between the coarse q-point grid, the medium-density k-point grid, and the fine k-point grid.

[0012] In conjunction with the second aspect mentioned above, in one possible implementation, based on the input file, structural relaxation, parameter convergence testing, calculation of the electro-acoustic coupling matrix, and calculation of Coulomb interaction data are performed sequentially. This includes: obtaining the material's ground-state lattice constant and atomic coordinates through geometric optimization; generating multiple sets of self-consistent field calculation tasks with different plane wave cutoff energies, analyzing the total energy, determining the optimal plane wave cutoff energy value according to a preset convergence threshold, and updating it to subsequent input files; sequentially performing self-consistent field calculations using a medium-density k-point grid, density functional perturbation theory calculations using a coarse q-point grid, and calculations of the electro-acoustic coupling matrix elements using a coarse k-point grid to obtain the electro-acoustic coupling matrix; and sequentially performing non-self-consistent calculations using a fine k-point grid, twin mesh generation, and calculations of shielded Coulomb and spin fluctuation interactions to obtain Coulomb interaction data.

[0013] In conjunction with the second aspect mentioned above, one possible implementation involves aggregating the electro-acoustic coupling matrix and Coulomb interaction data, calling the SCDFT solver, and solving for the superconducting transition temperature using a bisection method. This includes: associating and integrating the electro-acoustic coupling matrix and Coulomb interaction data; calling the SCDFT solver from the superconducting toolkit, loading the integrated electro-acoustic coupling matrix and Coulomb interaction data, and initializing the search interval and preset convergence threshold for the superconducting transition temperature; based on the bisection logic, continuously bisecting the current temperature search interval, solving the linearized Eliashberg equation at the midpoint temperature, and calculating the superconducting bandgap at the corresponding temperature; if the superconducting bandgap is greater than the preset convergence threshold, adjusting the temperature search interval according to the sign of the superconducting bandgap, and repeating the bisection iteration steps; if the superconducting bandgap is less than or equal to the preset convergence threshold, the iteration terminates, and the midpoint temperature is determined as the superconducting transition temperature.

[0014] Thirdly, this application provides a superconducting transition temperature detection device, comprising: a communication unit and a processing unit; the communication unit is used to acquire the structure file and configuration file of the superconducting material; the processing unit is used to generate an input file based on the structure file and configuration file; based on the input file, perform structural relaxation, parameter convergence test, calculate the electro-acoustic coupling matrix, and calculate Coulomb interaction data in sequence; aggregate the electro-acoustic coupling matrix and Coulomb interaction data, call the SCDFT solver, and solve for the superconducting transition temperature using the bisection method.

[0015] This application provides a superconducting transition temperature detection system and method, which can complete the entire process calculation from crystal structure input to superconducting transition temperature output through a sequentially cascaded module design and scripting language. This significantly improves computational efficiency and throughput, ensures the reliability and reproducibility of calculation results, and solves the technical problems of existing technologies that require manual intervention for superconducting transition temperature detection and have low automation levels, thereby achieving high-throughput screening of superconducting materials.

[0016] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0017] Figure 1 A system architecture diagram of a superconducting transition temperature detection system provided in this application embodiment; Figure 2 A system architecture diagram of another superconducting transition temperature detection system provided in this application embodiment; Figure 3 A system architecture diagram of another superconducting transition temperature detection system provided in this application embodiment; Figure 4 A system architecture diagram of another superconducting transition temperature detection system provided in this application embodiment; Figure 5 A schematic flowchart of a superconducting transition temperature detection method provided in an embodiment of this application; Figure 6 A schematic flowchart of another superconducting transition temperature detection method provided in an embodiment of this application; Figure 7 A schematic flowchart of another superconducting transition temperature detection method provided in an embodiment of this application; Figure 8 A schematic flowchart of another superconducting transition temperature detection method provided in an embodiment of this application; Figure 9 A schematic flowchart of another superconducting transition temperature detection method provided in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of a superconducting transition temperature detection device provided in an embodiment of this application; Detailed Implementation

[0018] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0019] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0020] The superconducting transition temperature detection method provided in this application embodiment can be applied to, for example... Figure 1 The superconducting transition temperature detection system shown includes a first module 101, a second module 102, and a third module 103 cascaded in sequence.

[0021] The first module 101 is used to generate an input file that is compatible with the calculation steps of superconducting properties and has consistent parameters; the second module 102 is used to calculate and output the electro-acoustic coupling matrix and Coulomb interaction data based on the input file; the third module 103 is used to call the SCDFT solver based on the electro-acoustic coupling matrix and Coulomb interaction data, output the superconducting transition temperature, and generate a Fermi surface bandgap visualization file.

[0022] As an example, such as Figure 2 As shown, the first module 101 includes: a structure parsing and conversion unit 201 and a parameterization generation unit 202; The structure analysis and conversion unit 201 is used to identify the CIF or POSCAR format standard structure file of the superconducting material and call the Phonopy tool to find the lattice unit cell. The parameterization generation unit 202 is used to receive the configuration file that records global parameters such as pressure, magnetism, and spin-orbit coupling. Combining the structural information of the lattice unit cell with built-in expert knowledge, it sets multi-level k-point grid configurations with different densities and mutual compatibility for superconducting property calculation steps such as structural relaxation and self-consistent field calculation. It generates an input file set that is compatible with all calculation steps and has consistent basic information such as prefix, lattice parameters, and atomic coordinates.

[0023] As an example, such as Figure 3 As shown, the second module 102 includes: a core computing unit 301, a closed-loop convergence testing unit 302, a job dependency management unit 303, and a data flow management unit 304; The core computing unit 301 is used to sequentially perform structural relaxation, electroacoustic coupling matrix calculation, and Coulomb interaction data calculation; the closed-loop convergence test unit 302 is used to generate multiple sets of self-consistent field calculation tasks with different plane wave cutoff energies, analyze the total energy and determine the optimal plane wave cutoff energy value according to the preset convergence threshold, and update it to the subsequent input file; the job dependency management unit 303 is used to submit tasks according to the calculation logic order of the core computing unit, and submit subsequent dependent steps only after the previous steps are successful; the data flow management unit 304 is used to create a standard working directory for each step of the core computing unit.

[0024] As an example, such as Figure 4 As shown, the third module 103 includes: a data aggregation unit 401, a Tc solving unit 402, and a result extraction and output unit 403; The data aggregation unit 401 is used to acquire the electroacoustic coupling matrix and Coulomb interaction data output by the second module and perform correlation and integration; the Tc solving unit 402 is used to call the SCDFT solver, load the integrated data and initialize the search range and preset convergence threshold of the superconducting transition temperature, adjust the temperature based on the bisection method logic iteration, calculate the superconducting bandgap by solving the linearized Eliashberg equation, and determine the superconducting transition temperature when the bandgap is less than or equal to the convergence threshold; the result extraction and output unit 403 is used to generate a Fermi surface bandgap visualization file based on the superconducting transition temperature.

[0025] To address the technical problems of existing technologies requiring manual intervention and having low automation levels in superconducting transition temperature detection, this application provides a method for superconducting transition temperature detection. This method includes: acquiring the structure file and configuration file of the superconducting material using a scripting language; generating input files adapted to each calculation step based on these files; sequentially performing structural relaxation, parameter convergence testing, and calculations of the electro-acoustic coupling matrix and Coulomb interaction data; aggregating the two types of data; and then calling the SCDFT solver to solve for the superconducting transition temperature using the bisection method. Based on this, the entire process from crystal structure input to superconducting transition temperature output can be automated without manual intervention, significantly improving computational efficiency and throughput, ensuring the reliability and reproducibility of the calculation results, lowering the technical threshold, and ultimately achieving high-throughput screening of superconducting materials.

[0026] like Figure 5 As shown in the embodiments of this application, the superconducting transition temperature detection method includes: S501. Obtain the structure file and configuration file of the superconducting material.

[0027] The structure file is a standard crystal structure file in CIF and POSCAR formats, used to provide information on the atomic arrangement and periodicity of superconducting materials; the configuration file is a file that records global parameters set by the user, including pressure, magnetism, and spin-orbit coupling parameters.

[0028] In this embodiment, automated acquisition is achieved through a scripting language. The script allows users to directly upload files or read files through a specified path, and automatically verifies the compliance of file formats without the need for manual conversion or filtering.

[0029] It should be noted that the script is compatible with mainstream crystal structure file formats, avoiding calculation interruptions due to format incompatibility.

[0030] As an example, a script deployed on a high-performance computing cluster receives user-uploaded structure files and configuration files via the "upload_input" command and automatically stores them in the specified working directory.

[0031] Based on the above steps, compliant calculation inputs can be obtained quickly, reducing the time cost of manual preprocessing and the risk of formatting errors.

[0032] S502. Generate input files based on structure files and configuration files.

[0033] In this embodiment of the application, based on the first module 101, the script automatically parses the atomic arrangement and periodicity information of the structure file, extracts the global parameters in the configuration file, and integrates them to generate input files for all steps such as adaptive structure relaxation and self-consistent field calculation, ensuring that the basic parameters of each file are consistent.

[0034] It should be noted that the logic for generating the input file is fixed by the script, which avoids parameter omissions or conflicts caused by manual coding.

[0035] Based on the above steps, the input file can be generated automatically and in a standardized manner, improving parameter consistency and the reliability of subsequent calculations.

[0036] S503. Based on the input file, perform structural relaxation, parameter convergence test, calculate electroacoustic coupling matrix, and calculate Coulomb interaction data in sequence.

[0037] Among them, structural relaxation refers to obtaining the ground-state lattice constant and atomic coordinates of the material through geometric optimization, while parameter convergence testing is used to determine the optimal values ​​of key computational parameters.

[0038] In this embodiment of the application, based on the second module 102, the script schedules each computing task in a preset logical order, automatically submits jobs to the computing cluster, monitors the task status in real time, and automatically starts subsequent tasks after the previous steps are completed, without the need for manual intervention.

[0039] Based on the above steps, multi-step calculations are efficiently connected, human error is reduced, and the overall calculation time is shortened.

[0040] S504, aggregated electro-acoustic coupling matrix and Coulomb interaction data, called SCDFT solver, and solved the superconducting transition temperature using the bisection method.

[0041] Among them, the bisection method is a numerical method for determining the superconducting transition temperature by iteratively adjusting the temperature search range to approach the critical point where the superconducting bandgap is zero.

[0042] In this embodiment of the application, based on the third module 103, the script automatically aggregates the two types of data generated in the preceding steps, loads them into the SCDFT solver after format adaptation, and solves them through the built-in bisection logic iterative solution without the need for manual configuration of the solution parameters.

[0043] Based on the above steps, the superconducting transition temperature can be obtained quickly and accurately, improving computational efficiency and the stability of the results.

[0044] Based on the above technical solution, the automated process implemented by sequentially cascaded module design and scripting language can complete the entire calculation process from crystal structure input to superconducting transition temperature output without manual intervention, which greatly improves the calculation efficiency and throughput, ensures the reliability and reproducibility of the calculation results, and solves the technical problems of existing technologies that require manual intervention for superconducting transition temperature detection and have low automation levels, thereby realizing high-throughput screening of superconducting materials.

[0045] In one possible implementation of the embodiments of this application, combined with Figure 5 ,like Figure 6 As shown, the above S502 can be specifically implemented through the following S601, S602, S603 and S604, which are explained in detail below: S601. Identify the structure file and call the Phonopy tool to find the lattice unit cell.

[0046] Among them, the crystal unit cell is the smallest repeating unit of the crystal structure, containing the core information of the crystal's periodicity.

[0047] In this embodiment of the application, based on the structure parsing and conversion unit 201, the script automatically identifies CIF and POSCAR format files and calls the Phonopy tool to perform cell extraction.

[0048] It should be noted that the script can automatically skip redundant atoms, retain the core periodic structure, and reduce computational redundancy.

[0049] As an example, a script deployed on a Linux cluster generates lattice primitives using the command "phonopy -c POSCAR primcell".

[0050] Based on the above steps, the computational system is simplified, reducing the number of atoms and complexity of subsequent calculations.

[0051] S602, Extract global parameters from the configuration file.

[0052] The global parameters are the calculation conditions set by the user, including pressure, magnetism, and spin-orbit coupling parameters.

[0053] In this embodiment, the script supports parsing configuration files in formats such as JSON and INI, automatically extracting parameters and converting them into the format required for calculation.

[0054] As an example, the script reads and extracts parameters such as "pressure":10 and "lspinorb":true from the config.json file.

[0055] Based on the above steps, compliant calculation conditions can be quickly obtained, avoiding calculation failures caused by incorrect parameter input.

[0056] S603, based on global parameters and structural information of lattice units, combined with built-in expert knowledge, sets up multi-level k-point grid configurations with different densities that are compatible with each other for structural relaxation, self-consistent field calculation, phonon calculation, non-self-consistent calculation, and superconducting density functional theory solutions.

[0057] Among them, the multi-level k-point grid configuration is a combination of k / q-point grids matched for different computing tasks, balancing computing accuracy and efficiency.

[0058] In this embodiment of the application, based on the parameterized generation unit 202, the script has built-in expert knowledge and automatically assigns grids to each computing task: phonon computing is set with a coarse q-point grid, self-consistent field computing is set with a medium-density k-point grid, and non-self-consistent computing is set with a fine k-point grid, and the grids satisfy an integer multiple relationship.

[0059] As an example, the script assigns 2×2×2 q-point meshes, 4×4×4 k-point meshes, and 8×8×8 k-point meshes to 3D materials, with the three types being compatible in integer multiples.

[0060] Based on the above steps, the allocation of grid resources is optimized to reduce the consumption of ineffective computing power while ensuring computational accuracy.

[0061] S604. Generate input files that are compatible with all calculation steps.

[0062] The input file serves as the parameter carrier for subsequent calculation steps, containing core information such as lattice parameters, atomic coordinates, and mesh configuration.

[0063] In this embodiment of the application, based on the parameterized generation unit 202, the script generates input files according to the format requirements of each calculation step, ensuring that the basic information of the prefix and lattice parameters is consistent, and embedding multi-level mesh configuration.

[0064] As an example, the script generates files such as relax.in, scf.in, and ph.in, all with the prefix "supercon", and the atomic coordinates are consistent with the lattice unit cell information.

[0065] Based on the above steps, standardized generation of input files is achieved, avoiding parameter conflicts and format errors caused by manual writing.

[0066] Based on the above technical solution, the process of unit cell extraction, parameter parsing, mesh configuration and input file generation is completed automatically by script, without the need for manual intervention. This greatly improves the efficiency and compatibility of input file generation, and provides an accurate and unified input foundation for subsequent automated calculations.

[0067] In one possible implementation of the embodiments of this application, combined with Figure 5 ,like Figure 7As shown, the above S503 can be specifically implemented through the following S701, S702, S703 and S704, which are explained in detail below: S701. Obtain the ground-state lattice constant and atomic coordinates of the material through geometric optimization.

[0068] Among them, geometry optimization includes relax (fixed lattice) or vc-relax (both lattice and atom optimization) calculations. The ground state lattice constant and atomic coordinates are the core structural parameters of the material in the lowest energy state.

[0069] In this embodiment, based on the core computing unit 301, a geometry optimization tool is invoked, allowing users to select optimization types through configuration files. Without the need for manual writing of optimization input scripts, tasks are automatically submitted to the computing cluster and the completion status is monitored to perform structural relaxation.

[0070] As an example, a script deployed on the SLURM scheduling cluster submits a cell optimization task via the command "sh relax.sh -type vc -relax" and automatically outputs the ground state structure file.

[0071] Based on the above steps, a stable ground-state structure of the material can be obtained quickly, avoiding structural deviations caused by manually selecting optimization parameters.

[0072] S702. Generate multiple sets of self-consistent field calculation tasks with different plane wave cutoff energies, analyze the total energy and determine the value of the optimal plane wave cutoff energy according to the preset convergence threshold, and update it to the subsequent input files.

[0073] Among them, the plane wave cutoff energy (ecutwfc) is a key parameter controlling the accuracy of the electronic wave function expansion, and the self-consistent field (SCF) calculation is used to obtain the total energy of the material's ground state.

[0074] In this embodiment of the application, based on the closed-loop convergence test unit 302, multiple sets of SCF calculation tasks with different cutoff energies are automatically generated by the ecutwfc.sh script. After batch submission, the total energy output by each task is analyzed in real time, and the energy change is compared to see if it meets the preset convergence threshold. The optimal cutoff energy is automatically updated to all subsequent input files that depend on this parameter, and parameter convergence test is performed.

[0075] It should be noted that the script supports customizing the range of values ​​that can be truncated and the convergence threshold, so as to avoid excessive truncation that would waste computing resources.

[0076] As an example, the script generates 5 sets of cutoff energies (30Ry, 40Ry, 50Ry, 60Ry, 70Ry), with a preset convergence threshold of 1e-6eV. After parsing, the optimal value of 50Ry is updated to files such as scf.in and ph.in.

[0077] Based on the above steps, the optimal calculation parameters are automatically determined, balancing calculation accuracy and resource utilization, without the need for manual analysis of test results.

[0078] S703. Perform the following steps in sequence: self-consistent field calculation for medium-density k-point grid, density functional perturbation theory calculation for coarse q-point grid, and calculation of electro-acoustic coupling matrix elements for coarse k-point grid to obtain the electro-acoustic coupling matrix.

[0079] Density functional perturbation theory (DFPT) is used to efficiently calculate phonon properties, and the electro-acoustic coupling matrix is ​​the core data describing the strength of the interaction between electrons and phonons.

[0080] In this embodiment, based on the core computing unit 301, the phonon.sh script sequentially schedules three types of computing tasks according to preset logic to calculate the electroacoustic coupling matrix. At the same time, the job dependency management unit 303 automatically transfers the output data of the preceding tasks to the subsequent steps without the need for manual copying or modification of file paths.

[0081] It should be noted that the k / q point grid density is consistent with the multi-level configuration in the preceding input file to ensure computational compatibility.

[0082] As an example, the script first performs a 4×4×4 medium k-point SCF calculation, then a 2×2×2 coarse q-point DFPT calculation, and finally a coarse k / q-point mesh electro-acoustic coupling matrix element calculation, and integrates the results to obtain the electro-acoustic coupling matrix.

[0083] Based on the above steps, multi-stage calculations are efficiently connected, human error is reduced, and high-quality electroacoustic coupling data is obtained quickly.

[0084] S704. Perform fine k-point mesh non-self-consistent calculation, twin mesh generation, and shielded Coulomb-spin fluctuation interaction calculation in sequence to obtain Coulomb interaction data.

[0085] Among them, non-self-consistent (NSCF) calculation is used to obtain high-precision electronic band structure, and twin mesh is an auxiliary mesh for accurate calculation of Coulomb interaction nuclei.

[0086] In this embodiment, based on the core computing unit 301, the coulomb.sh script automatically performs fine k-point mesh NSCF calculation, calls the twinrid.x tool to generate twin mesh, and then connects the superconducting toolkit (SCTK) to calculate the shielded Coulomb and spin fluctuation interaction. The entire process does not require manual intervention in tool calls.

[0087] It should be noted that the density of the fine k-point grid conforms to the integer multiple relationship of the previous multi-level configuration, ensuring the accuracy of the band structure.

[0088] As an example, the script performs an 8×8×8 fine k-point NSCF calculation, generates a twin mesh, and then calls the SCTK scdft.x tool to output a Coulomb interaction data file.

[0089] Based on the above steps, the calculation of Coulomb interaction is completed automatically, improving data accuracy and computational efficiency, and lowering the technical threshold.

[0090] Based on the above technical solution, the automated scheduling and data flow management of multi-step calculations are realized through scripting languages. There is no need for manual intervention in task connection, parameter configuration and data transmission, which not only ensures the accuracy of calculation and the reliability of results, but also significantly shortens the calculation cycle.

[0091] In one possible implementation of the embodiments of this application, combined with Figure 5 ,like Figure 8 As shown, the above S504 can be specifically implemented through the following S801, S802, S803 and S804, which are explained in detail below: S801. Correlate and integrate the electroacoustic coupling matrix and the Coulomb interaction data.

[0092] Among them, correlation integration refers to matching the two types of data according to the computational logic, format, and alignment dimensions to ensure that the input requirements of the SCDFT solver are met.

[0093] In this embodiment of the application, based on the data aggregation unit 401, the data file output by the previous steps is automatically read through the scdft.sh script, and the data structure is parsed before format conversion and dimension alignment are performed.

[0094] It should be noted that the script supports multiple data output formats and can be flexibly adapted to different versions of the SCDFT solver.

[0095] As an example, the script calls the "data_integrate" function to integrate the .dat file of the electroacoustic coupling matrix with the .nc file of the Coulomb interaction into a solver-compatible .h5 format file.

[0096] Based on the above steps, automated and compatible data processing can be achieved, reducing the error rate and time cost of manual integration.

[0097] S802. Call the SCDFT solver in the superconducting toolkit, load the integrated electro-acoustic coupling matrix and Coulomb interaction data, and initialize the search range for the superconducting transition temperature and the preset convergence threshold.

[0098] Among them, the superconducting transition temperature search range is the temperature range to be solved, and the preset convergence threshold is the critical value for judging whether the superconducting bandgap has reached stability.

[0099] In this embodiment, based on the Tc solver unit 402, the script's built-in solver call command automatically locates the installation path of the Superconducting Toolkit (SCTK), loads the integrated data, and also supports users to customize the search interval and convergence threshold through configuration files.

[0100] It should be noted that the rationality of the initialization parameters is verified by the script's built-in logic to avoid divergence in the solution due to improper parameter settings.

[0101] As an example, the script calls the SCTK scdft.x tool, loads .h5 format data, initializes the search range to 1K-200K, and presets the convergence threshold to 1e-5eV.

[0102] Based on the above steps, the solver can be started quickly and the parameters can be initialized, improving the startup efficiency and stability of the solution process.

[0103] S803, based on the bisection logic, continuously divides the current temperature search interval into two parts, solves the linearized Eliashberg equation at the midpoint temperature, and calculates the superconducting bandgap at the corresponding temperature.

[0104] Among them, the bisection method is an efficient numerical method that approaches the target value by gradually narrowing the temperature range, and the linearized Eliashberg equation is the core equation describing the relationship between the superconducting bandgap and temperature.

[0105] In this embodiment, based on the Tc solver 402, the script solidifies the bisection method iteration logic, automatically calculates the midpoint of the temperature range, calls the solver to solve the equation and extract the superconducting gap, without the need for manual intervention in the iteration process.

[0106] As an example, with an initial range of 1K-200K, the script first calculates the bandgap at the midpoint temperature of 100.5K, then divides the range in half based on the result, and continues to iterate until it approaches the target temperature.

[0107] It should be noted that this application does not specifically limit the process of using a solver to solve the Eliashberg equation.

[0108] Based on the above steps, the iterative solution is automated, significantly shortening the calculation cycle of the superconducting bandgap.

[0109] S804. If the superconducting gap is greater than the preset convergence threshold, adjust the temperature search interval according to the sign of the superconducting gap and repeat the binary iteration step; if the superconducting gap is less than or equal to the preset convergence threshold, the iteration terminates and the midpoint temperature is determined as the superconducting transition temperature.

[0110] In this embodiment of the application, based on the Tc solving unit 402, the script automatically compares the superconducting band gap with the preset convergence threshold, and determines the temperature adjustment direction according to the sign of the band gap (if the band gap is positive, the lower limit of the temperature range is increased, and if it is negative, the upper limit is decreased). The entire iteration process does not require manual judgment or adjustment.

[0111] It should be noted that the script has a maximum limit on the number of iterations to prevent infinite iterations due to special circumstances.

[0112] As an example, if the bandgap at the midpoint temperature of 100.5K is 0.02eV (greater than 1e-5eV) and positive, the script will adjust the range to 100.5K-200K and continue to calculate the bandgap at 150.25K using binary search.

[0113] Based on the above steps, the superconducting transition temperature can be accurately located, ensuring the accuracy and reliability of the results without the need for manual threshold judgment.

[0114] Based on the above technical solution, the data integration, solver invocation, iterative calculation and result judgment are fully automated through scripting language, without the need for manual intervention in data processing or the solution process. This not only improves the solution efficiency of superconducting transition temperature, but also ensures the accuracy of the results, providing efficient core computational support for high-throughput screening of superconducting materials.

[0115] In one possible implementation, combining Figure 5 ,like Figure 9 As shown, following S504, the superconducting transition temperature detection method provided in this application embodiment further includes the following S901: S901. Based on the superconducting transition temperature, generate a visualization file of the Fermi surface bandgap.

[0116] Among them, the Fermi surface bandgap visualization file is a file that intuitively presents the bandgap distribution at the Fermi surface of a superconducting material. The commonly used format is .frmsf, ​​which can be directly used for subsequent physical property analysis.

[0117] In this embodiment of the application, based on the result extraction output unit 403, the script automatically calls the visualization function in the superconducting toolkit, and combines the superconducting transition temperature obtained in S504 with the electronic structure data calculated in the previous step, without the need for manual configuration of drawing parameters, and automatically generates a standardized visualization file.

[0118] It should be noted that the file contains key information such as bandgap size and spatial distribution, and can be used directly without additional data processing.

[0119] As an example, after obtaining the superconducting transition temperature of 100K, the script calls the "plot_frmsf" function to generate a .frmsf file containing a thermal map of the bandgap distribution, and saves it to the "result_visual" directory.

[0120] Based on the above steps, the core bandgap characteristics of superconducting materials are presented intuitively, simplifying the data interpretation process and improving the efficiency of scientific research analysis.

[0121] The foregoing mainly describes the solutions of the embodiments of this application from the perspective of device implementation. It is understood that each device, such as a superconducting transition temperature detection device, includes at least one of the hardware structures and software modules corresponding to the execution of each function in order to achieve the above-mentioned functions. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0122] This application embodiment can divide the superconducting transition temperature detection device into functional units according to the above method example. For example, each function can be divided into separate functional units, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0123] When using integrated units, Figure 10 A possible structural schematic diagram of the superconducting transition temperature detection device (referred to as superconducting transition temperature detection device 100) involved in the above embodiments is shown. The superconducting transition temperature detection device 100 includes a processing unit 1001 and a communication unit 1002, and may also include a storage unit 1003. Figure 10 The schematic diagram shown can be used to illustrate the structure of the superconducting transition temperature detection device involved in the above embodiments.

[0124] when Figure 10 The schematic diagram shown illustrates the structure of the superconducting transition temperature detection device involved in the above embodiments. The processing unit 1001 is used to control and manage the operation of the superconducting transition temperature detection device, the communication unit 1002 is used for the superconducting transition temperature detection device to communicate with other devices, and the storage unit 1003 is used to store the program code and data of the superconducting transition temperature detection device.

[0125] For example, communication unit 1002 is used to acquire the structure file and configuration file of the superconducting material; Processing unit 1001 is used to generate an input file based on the structure file and configuration file; based on the input file, it sequentially performs structural relaxation, parameter convergence test, calculates the electro-acoustic coupling matrix, and calculates the Coulomb interaction data; it aggregates the electro-acoustic coupling matrix and the Coulomb interaction data, calls the SCDFT solver, and solves the superconducting transition temperature using the bisection method.

[0126] In one possible implementation, after aggregating the electro-acoustic coupling matrix and Coulomb interaction data, calling the SCDFT solver, and solving for the superconducting transition temperature using the bisection method, the method further includes: generating a Fermi surface bandgap visualization file based on the superconducting transition temperature.

[0127] In one possible implementation, the processing unit 1001 is further configured to generate an input file based on the structure file and configuration file, including: identifying the structure file and calling the Phonopy tool to find the lattice unit cell; extracting the global parameters of the configuration file; based on the global parameters and the structural information of the lattice unit cell, combined with built-in expert knowledge, setting multi-level k-point grid configurations of different densities that are compatible with each other for structural relaxation, self-consistent field calculation, phonon calculation, non-self-consistent calculation, and superconducting density functional theory solutions; generating an input file that adapts to all calculation steps; the prefix, lattice parameters, and atomic coordinate basic information in the input file are consistent, and the multi-level k-point grid configurations of different densities that are compatible with each other are embedded.

[0128] In one possible implementation, a multi-level k-point grid configuration with different densities and mutual compatibility includes: setting a coarse q-point grid for phonon calculations; setting a medium-density k-point grid for self-consistent field calculations; setting a fine k-point grid for non-self-consistent calculations; and satisfying an integer multiple relationship between the coarse q-point grid, the medium-density k-point grid, and the fine k-point grid.

[0129] In one possible implementation, the processing unit 1001 is further configured to sequentially perform structural relaxation, parameter convergence testing, calculation of the electro-acoustic coupling matrix, and calculation of Coulomb interaction data based on the input file, including: obtaining the material's ground-state lattice constant and atomic coordinates through geometric optimization; generating multiple sets of self-consistent field calculation tasks with different plane wave cutoff energies, analyzing the total energy and determining the optimal plane wave cutoff energy value according to a preset convergence threshold, and updating it to subsequent input files; sequentially performing self-consistent field calculations of medium-density k-point grids, density functional perturbation theory calculations of coarse q-point grids, and calculations of the element-wise electro-acoustic coupling matrix of coarse k-point grids to obtain the electro-acoustic coupling matrix; and sequentially performing non-self-consistent calculations of fine k-point grids, twin grid generation, and calculations of shielded Coulomb and spin fluctuation interactions to obtain Coulomb interaction data.

[0130] In one possible implementation, the processing unit 1001 is further configured to aggregate the electro-acoustic coupling matrix and Coulomb interaction data, call the SCDFT solver, and solve for the superconducting transition temperature using a bisection method. This includes: associating and integrating the electro-acoustic coupling matrix and Coulomb interaction data; calling the SCDFT solver in the superconducting toolkit, loading the integrated electro-acoustic coupling matrix and Coulomb interaction data, and initializing the search interval and preset convergence threshold for the superconducting transition temperature; based on the bisection logic, continuously bisecting the current temperature search interval, solving the linearized Eliashberg equation at the midpoint temperature, and calculating the superconducting bandgap at the corresponding temperature; if the superconducting bandgap is greater than the preset convergence threshold, adjusting the temperature search interval according to the sign of the superconducting bandgap, and repeating the bisection iteration steps; if the superconducting bandgap is less than or equal to the preset convergence threshold, the iteration terminates, and the midpoint temperature is determined as the superconducting transition temperature.

[0131] The processing unit 1001 can be a processor or a controller, and the communication unit 1002 can be a communication interface, transceiver, transceiver circuit, transceiver device, etc. The term "communication interface" is a general term and may include one or more interfaces. The storage unit 1003 can be a memory. When the superconducting transition temperature detection device 100 is a chip, the processing unit 1001 can be a processor or a controller, and the communication unit 1002 can be an input interface and / or an output interface, pins, or circuits, etc. The storage unit 1003 can be a storage unit within the chip (e.g., a register, cache, etc.) or a storage unit located outside the chip (e.g., read-only memory (ROM), random access memory (RAM, etc.)).

[0132] The communication unit can also be called a transceiver unit. The antenna and control circuit with transceiver functions in the superconducting transition temperature detection device 100 can be considered as the communication unit 1002 of the superconducting transition temperature detection device 100, and the processor with processing functions can be considered as the processing unit 1001 of the superconducting transition temperature detection device 100. Optionally, the device in the communication unit 1002 that implements the receiving function can be considered as the communication unit. The communication unit is used to execute the receiving steps in the embodiments of this application, and the communication unit can be a receiver, a receiver circuit, etc. The device in the communication unit 1002 that implements the transmitting function can be considered as the transmitting unit. The transmitting unit is used to execute the transmitting steps in the embodiments of this application, and the transmitting unit can be a transmitter, a transmitter, a transmitting circuit, etc.

[0133] Figure 10If the integrated units in the process are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. Storage media for storing computer software products include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0134] Figure 10 The units in the process can also be called modules; for example, a processing unit can be called a processing module.

[0135] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0136] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A superconducting transition temperature detection system, characterized in that, include: The first, second, and third modules are cascaded in sequence; The first module is used to generate an input file that is compatible with the calculation steps for superconducting properties and has consistent parameters; The superconducting property calculation steps include structural relaxation, self-consistent field calculation, phonon calculation, non-self-consistent calculation, and superconducting density functional theory solution. The second module is used to calculate and output the electroacoustic coupling matrix and Coulomb interaction data based on the input file; The third module is used to call the SCDFT solver based on the electroacoustic coupling matrix and Coulomb interaction data, output the superconducting transition temperature, and generate a Fermi surface bandgap visualization file.

2. The superconducting transition temperature detection system according to claim 1, characterized in that, The first module includes: a structure parsing and transformation unit and a parameterization generation unit; The structure analysis and conversion unit is used to identify the structure file of the superconducting material and call the Phonopy tool to find the lattice unit cell; The parameterization generation unit has built-in expert knowledge and is used to receive configuration files and generate input files by combining the structural information of the lattice unit cells with the expert knowledge; the expert knowledge sets k-point grids of different densities but which are compatible with each other for different computing tasks.

3. The superconducting transition temperature detection system according to claim 1, characterized in that, The second module includes: a core computing unit, a closed-loop convergence testing unit, a job dependency management unit, and a data flow management unit; The core computing unit is used to sequentially perform structural relaxation, calculate the electro-acoustic coupling matrix, and calculate Coulomb interaction data. The closed-loop convergence test unit is used to generate multiple sets of self-consistent field calculation tasks with different plane wave cutoff energies, analyze the total energy, determine the value of the optimal plane wave cutoff energy according to the preset convergence threshold, and update it to the subsequent input file. The job dependency management unit is used to submit tasks according to the computational logic order of the core computing unit, and submit subsequent dependency steps only after the previous steps are successful. The data flow management unit is used to create a standard working directory for each step of the core computing unit.

4. The superconducting transition temperature detection system according to claim 1, characterized in that, The third module includes: a data aggregation unit, a Tc solution unit, and a result extraction and output unit; The data aggregation unit is used to acquire the electroacoustic coupling matrix and Coulomb interaction data, and to perform correlation and integration. The Tc solving unit is used to call the SCDFT solver, iteratively adjust the temperature, and obtain the superconducting transition temperature; The result extraction and output unit is used to generate a Fermi surface bandgap visualization file based on the superconducting transition temperature.

5. A method for detecting superconducting transition temperature, applied to the superconducting transition temperature detection system as described in any one of claims 1-4, characterized in that, The method is implemented using a scripting language and includes: Obtain the structure file and configuration file of the superconducting material; the structure file is a standard crystal structure file in CIF or POSCAR format, used to provide atomic arrangement and periodicity information of the superconducting material; the configuration file is a file that records the global parameters set by the user; the global parameters include pressure, magnetism, and spin-orbit coupling parameters; Based on the structure file and the configuration file, an input file is generated; Based on the input file, structural relaxation, parameter convergence test, calculation of electroacoustic coupling matrix, and calculation of Coulomb interaction data are performed sequentially. By aggregating the electroacoustic coupling matrix and the Coulomb interaction data, the SCDFT solver is invoked to solve for the superconducting transition temperature using the bisection method.

6. The method according to claim 5, characterized in that, After aggregating the electroacoustic coupling matrix and the Coulomb interaction data, calling the SCDFT solver, and solving the superconducting transition temperature using the bisection method, the method further includes: generating a Fermi surface bandgap visualization file based on the superconducting transition temperature.

7. The method according to claim 5, characterized in that, Based on the structure file and the configuration file, an input file is generated, including: Identify the structure file and use the Phonopy tool to find the lattice unit cell; Extract the global parameters from the configuration file; Based on the global parameters and the structural information of the lattice unit cell, combined with the built-in expert knowledge, a multi-level k-point grid configuration with different densities and mutual compatibility is set for structural relaxation, self-consistent field calculation, phonon calculation, non-self-consistent calculation, and superconducting density functional theory solution. Generate an input file that is compatible with all calculation steps; the prefix, lattice parameters, and atomic coordinate basic information in the input file are consistent, and the multi-level k-point grid configurations of different densities that are compatible with each other are embedded.

8. The method according to claim 7, characterized in that, The multi-level k-point grid configurations with different densities and mutual compatibility include: Set a coarse q-point mesh for phonon calculations; Set up a medium-density k-point grid for self-consistent field calculations; Set up a fine k-point grid for non-self-consistent calculations; The coarse q-point mesh, the medium-density k-point mesh, and the fine k-point mesh satisfy an integer multiple relationship.

9. The method according to claim 5, characterized in that, Based on the input file, structural relaxation, parameter convergence testing, calculation of the electroacoustic coupling matrix, and calculation of Coulomb interaction data are performed sequentially, including: The ground-state lattice constant and atomic coordinates of the material were obtained through geometric optimization. Generate multiple sets of self-consistent field calculation tasks with different plane wave cutoff energies, analyze the total energy and determine the value of the optimal plane wave cutoff energy according to the preset convergence threshold, and update it to the subsequent input files; The electro-acoustic coupling matrix is ​​obtained by sequentially performing self-consistent field calculations for medium-density k-point grids, density functional perturbation theory calculations for coarse q-point grids, and calculations of the electro-acoustic coupling matrix elements for coarse k-point grids. The following steps were performed sequentially: fine k-point mesh non-self-consistent calculation, twin mesh generation, and shielded Coulomb-spin fluctuation interaction calculation to obtain Coulomb interaction data.

10. The method according to claim 5, characterized in that, By aggregating the electroacoustic coupling matrix and the Coulomb interaction data, and calling the SCDFT solver, the superconducting transition temperature is solved using the bisection method, including: The electroacoustic coupling matrix and the Coulomb interaction data are correlated and integrated; Call the SCDFT solver in the superconducting toolkit, load the integrated electro-acoustic coupling matrix and Coulomb interaction data, and initialize the search range for the superconducting transition temperature and the preset convergence threshold. Based on the bisection logic, the current temperature search interval is continuously bisected, and the linearized Eliashberg equation is solved at the midpoint temperature to calculate the superconducting gap at the corresponding temperature. If the superconducting gap is greater than the preset convergence threshold, the temperature search range is adjusted according to the sign of the superconducting gap, and the binary search iteration steps are repeated; if the superconducting gap is less than or equal to the preset convergence threshold, the iteration terminates, and the midpoint temperature is determined as the superconducting transition temperature.

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