Optimization method and system for copper indium gallium selenide thin film solar cell material

Abstract

The invention provides an optimization method and system for a CIGS thin-film solar cell material. The method comprises the following steps: constructing a thermodynamic database and verifying the thermodynamic database; predicting through an intelligent sampling strategy in combination with the verified thermodynamic database; setting a temperature range for the predicted candidate component list to perform phase equilibrium calculation; inputting the candidate component list and the phase equilibrium data table into a structured database for storage; performing phase change behavior analysis according to the stored data; sorting and screening the candidate materials according to the phase diagrams and the data reports; sequentially performing experimental synthesis and performance characterization on the candidate materials in the candidate material list. According to the method, the dynamic combination of variable elements, fixed elements and the balance elements is designed for the CIGS quaternary system, and efficient scanning of component gradient and temperature range is supported. The functions of phase balance calculation, data export and visual analysis are integrated, a phase diagram and a data report under multi-parameter combination are generated through one key, and the research and development period is remarkably shortened.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of new energy materials and computational materials science, and in particular to an optimization method and system for copper indium gallium selenide thin-film solar cell materials. Background Technology

[0002] Copper indium gallium selenide (CIGS) thin-film solar cells hold significant promise for applications in the solar cell field due to their high elemental abundance, high photoelectric conversion efficiency, low cost, flexibility, and good stability. However, the development of this type of solar cell is currently facing an unprecedented bottleneck, with no substantial breakthrough in actual photoelectric conversion efficiency over the past decade. The performance of CIGS materials is highly dependent on their composition ratios and preparation conditions. Traditional experimental trial-and-error methods for developing CIGS materials are time-consuming, costly, and make it difficult to systematically explore the broad range of material parameters.

[0003] Furthermore, fundamental theoretical research on CIGS multi-component related systems remains very weak, particularly regarding the phase equilibrium relationships and thermodynamic data of CIGS material alloy systems. This lack of research undermines the solid theoretical foundation for the composition design and fabrication process optimization of CIGS-based solar cell materials, leading to difficulties in the precise control of composition and other conditions. This limitation not only affects the photoelectric conversion efficiency of the battery devices but also restricts the practical application potential of CIGS-based solar cells.

[0004] While existing methods such as first-principles calculations can accurately calculate material properties, they are typically applicable to single or small numbers of specific components / structures, resulting in large computational loads and making it difficult to rapidly and systematically screen and evaluate the performance of a large number of candidate material systems. High-throughput computing technology provides an effective tool for accelerating material discovery and optimization by rapidly screening and analyzing a large number of material combinations. However, while existing high-throughput computing systems can assist in material design, when optimizing CIGS materials, they only support local parameter calculations, lack high-throughput analysis capabilities for multi-element collaborative optimization, separate calculation from data processing, cannot generate phase diagrams and data reports in real time, lack graphical interfaces, are complex to operate, and are difficult to meet the needs of researchers without computer science backgrounds. Summary of the Invention

[0005] In view of the above, the main objective of this invention is to provide an optimization method and system for copper indium gallium selenide thin-film solar cell materials to solve the aforementioned technical problems.

[0006] This invention proposes an optimization method for copper indium gallium selenide (CIGS) thin-film solar cell materials, the method comprising the following steps: Step 1: Collect and integrate literature, public databases and experimental data, and use phase diagram calculation technology to perform thermodynamic calculations to construct a thermodynamic model. Optimize the parameters of the thermodynamic model to obtain the optimized thermodynamic model. Use the optimized thermodynamic model to construct a thermodynamic database. Optimize, evaluate and verify the thermodynamic database in sequence to obtain the verified thermodynamic database. Step 2: Preset the elemental composition range, define stoichiometric constraints, and combine the verified thermodynamic database with an intelligent sampling strategy to predict and obtain a list of predicted candidate components. Step 3: Determine the temperature range for the predicted candidate component list and perform phase equilibrium calculations on the predicted candidate component list to obtain a phase equilibrium data table; Step 4: Using a relational database model, the candidate component list and phase equilibrium data table are input into the structured database for storage, resulting in a structured database with stored data. Step 5: Perform phase transition behavior analysis based on the stored data in the structured database after data storage, and generate phase diagrams and data reports; Step 6: Sort and filter the candidate materials according to the phase diagram and data report to obtain a list of candidate materials; Step 7: Perform experimental synthesis and performance characterization on the candidate materials in the candidate material list in sequence to obtain an experimental data table; the experimental data table includes phase composition, microstructure and performance data; Step 8: Compare the experimental data table with the phase equilibrium data table to obtain the comparison results. Using the comparison results in an iterative manner, adjust the thermodynamic model parameters in Step 1 and the intelligent sampling strategy in Step 2 respectively to obtain the thermodynamic database and the intelligent sampling strategy after feedback adjustment. Based on the thermodynamic database and the intelligent sampling strategy after feedback adjustment, obtain the final prediction results.

[0007] This invention also proposes an optimization system for copper indium gallium selenide (CIGS) thin-film solar cell materials, the system comprising: Thermodynamic database building module, used for: Literature, public databases and experimental data are collected and integrated, and thermodynamic calculations are performed using phase diagram calculation technology to construct a thermodynamic model. The parameters of the thermodynamic model are optimized to obtain a parameter-optimized thermodynamic model. The parameter-optimized thermodynamic model is used to construct a thermodynamic database. The thermodynamic database is then optimized, evaluated and verified in sequence to obtain a verified thermodynamic database. The intelligent sampling module is used for: The system presets the elemental composition range, defines stoichiometric constraints, and uses an intelligent sampling strategy to predict the predicted candidate components by combining a validated thermodynamic database. The phase equilibrium calculation module is used for: A temperature range is determined for the predicted candidate component list, and phase equilibrium calculations are performed on the predicted candidate component list to obtain a phase equilibrium data table; The structured database storage module is used for: A relational database model is used to construct a structured database. The candidate component list and phase equilibrium data table are input into the structured database for storage, resulting in a structured database with stored data. The data visualization module is used for: Phase transition behavior analysis is performed based on the stored data in the structured database after data storage, generating phase diagrams and data reports; The candidate material screening module is used for: Candidate materials are sorted and screened based on phase diagrams and data reports to obtain a list of candidate materials; Experimental synthesis module, used for: The candidate materials in the candidate material list were experimentally synthesized and their performance characterized in sequence to obtain an experimental data table; the experimental data table includes phase composition, microstructure and performance data; The experimental data table is compared with the phase equilibrium data table to obtain the comparison results. The thermodynamic model parameters and the intelligent sampling strategy are adjusted iteratively using the comparison results to obtain the thermodynamic database and the intelligent sampling strategy after feedback adjustment. The final prediction results are obtained based on the thermodynamic database and the intelligent sampling strategy after feedback adjustment.

[0008] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention targets the CIGS quaternary system (Cu–In–Ga–Se), designing a dynamic combination of variable elements, fixed elements, and balance elements to support efficient scanning of composition gradients and temperature ranges. It integrates phase equilibrium calculation, data export, and visualization analysis functions, generating phase diagrams and data reports under multiple parameter combinations with a single click, significantly shortening the R&D cycle. Based on a Python tkinter graphical interface, it provides modules for element definition, temperature setting, and real-time progress monitoring, lowering the barrier to entry for users. 2. This invention provides a high-throughput computing and analysis system specifically designed for the optimization of CIGS thin-film solar cell materials, integrating computation, data management, and analysis prediction. This system, through automated element combination generation, phase equilibrium calculation, and data visualization, helps researchers quickly determine the optimal material formulation. By analyzing phase composition, phase precipitation driving forces, and phase relationships, it delves into the compositional space, temperature range, and phase transition pathways of stable phase coexistence within the alloy system, obtaining the close correlation between phase equilibrium relationships, phase composition, and microstructure design, thus achieving precise control of composition and microstructure during the preparation process.

[0009] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by means of embodiments of the invention. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the steps of an optimization method for a copper indium gallium selenide (CIGS) thin-film solar cell material proposed in this invention.

[0011] Figure 2 This is a flowchart illustrating the optimization method for copper indium gallium selenide (CIGS) thin-film solar cell materials proposed in this invention.

[0012] Figure 3 This is a structured database ERD diagram of an optimization method for copper indium gallium selenide thin-film solar cell material proposed in this invention.

[0013] Figure 4 This is a functional flowchart of an optimization method for copper indium gallium selenide (CIGS) thin-film solar cell materials proposed in this invention.

[0014] Figure 5 This is a structural diagram of an optimized system for a copper indium gallium selenide (CIGS) thin-film solar cell material proposed in this invention. Detailed Implementation

[0015] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0016] These and other aspects of the embodiments of the present invention will become clear from the following description and accompanying drawings. In these descriptions and drawings, some specific embodiments of the present invention are specifically disclosed to provide some ways of implementing the principles of the embodiments of the present invention; however, it should be understood that the scope of the embodiments of the present invention is not limited thereto.

[0017] Please see Figure 1This invention proposes an optimization method for copper indium gallium selenide (CIGS) thin-film solar cell materials, which includes the following steps: Step 1: Collect and integrate literature, public databases and experimental data, and use phase diagram calculation technology to perform thermodynamic calculations to construct a thermodynamic model. Optimize the parameters of the thermodynamic model to obtain the optimized thermodynamic model. Use the optimized thermodynamic model to construct a thermodynamic database. Optimize, evaluate and verify the thermodynamic database in sequence to obtain the verified thermodynamic database.

[0018] Please see Figure 2 In step 1, literature, public databases, and experimental data are collected and integrated, and thermodynamic calculations are performed using phase diagram calculation techniques to construct a thermodynamic model. The parameters of the thermodynamic model are optimized to obtain a parameter-optimized thermodynamic model. A thermodynamic database is constructed using the parameter-optimized thermodynamic model, and the thermodynamic database is sequentially optimized, evaluated, and verified to obtain a verified thermodynamic database. The specific steps include the following: Thermodynamic data and experimental data from literature and public databases are collected and integrated to obtain integrated data. Based on thermodynamic data from literature and public databases in the integrated data, thermodynamic calculations are performed using phase diagram calculation techniques to construct a thermodynamic model. The parameters of the thermodynamic model are then optimized to obtain an optimized thermodynamic model, which is then used to construct a thermodynamic database. Based on thermodynamic data from literature and public databases, the thermodynamic models in the thermodynamic database are evaluated and corrected to obtain corrected thermodynamic models. The corrected thermodynamic models are then verified using experimental data to obtain a verified thermodynamic database.

[0019] Based on thermodynamic data from literature and public databases integrated into the dataset, thermodynamic calculations are performed using phase diagram calculation techniques. The corresponding relationships in the process are as follows: ; in, Represents total Gibbs free energy. This indicates taking the minimum value. Indicates the molar content of the phase. This indicates that there exists an arbitrary phase in equilibrium within the system. The Mohr Gibbs free energy, Represents pure elements Compounds exist at equilibrium Chemical sites in the phase Represents pure elements Compounds exist at equilibrium Chemical sites in the phase Represents pure elements Compounds exist at equilibrium Chemical sites in the phase Represents pure elements Compounds exist at equilibrium Chemical sites in the phase Represents pure elements The Mohr Gibbs free energy, , , , , , , and These represent the seven coefficients to be determined. Represents absolute temperature. Represents the natural logarithm. Represents absolute temperature The squared term, Represents absolute temperature The cubic term, Represents absolute temperature The reciprocal of the term, Represents absolute temperature The seventh term, Represents absolute temperature The negative ninth term, Represents pure elements The effect of magnetism on its standard molar Gibbs free energy. This indicates the total number of elements in the group. Represents the elements in a group mole fraction, Represents the elements in a group molar free energy, This represents the sum of the free energy components of a mechanical mixture in which there is no interaction between the components. Represents the gas constant. This represents the effect of ideal mixture entropy on free energy. This represents the excess Gibbs free energy in a non-ideal solution.

[0020] It should be noted that, To achieve the equilibrium state of the system when the total Gibbs free energy is minimized under isothermal and isobaric conditions, When the components form an ideal solution, the enthalpy of mixing is zero, and the entropy of mixing is the random entropy of mixing.

[0021] Furthermore, heterogeneous data from multiple sources, including literature, computational databases, and experimental measurements, were collected and integrated. A thermodynamic database of multi-component systems, including copper (Cu), indium (In), gallium (Ga), selenium (Se), and optional doped or impurity elements, was established using the phase diagram calculation (CALPHAD) method. Thermodynamic models were selected according to the different elements and compounds. Multiple thermodynamic models exist in a thermodynamic database, including pure component and multi-component thermodynamic models. The pure component thermodynamic models and multi-component thermodynamic models together constitute the thermodynamic database.

[0022] Step 2: Preset the elemental composition range, define stoichiometric constraints, and combine the verified thermodynamic database with an intelligent sampling strategy to predict and obtain a list of predicted candidate components.

[0023] In step 2, the predefined component range and stoichiometric ratio constraints are defined, and a prediction list of predicted candidate components is obtained by combining the validated thermodynamic database with an intelligent sampling strategy. This includes the following steps: The elemental composition range and stoichiometric ratio constraints are preset, and the key focus area and sparse exploration area are set according to the performance window of the copper indium gallium selenide device, resulting in a constrained and adjusted composition range definition table. The constrained component range definition table is used to calculate candidate components using a high-throughput combinatorial automatic generation algorithm to obtain a basic candidate component list; The predicted candidate component list can be obtained by adding sampling points to the basic candidate component list through uncertainty sampling, or by adding sampling points to the basic candidate component list through sensitivity sampling.

[0024] The basic candidate component list is supplemented with sampling points through uncertainty sampling to obtain the predicted candidate component list, which includes the following steps: Using the covariance matrix of thermodynamic model parameters in the validated thermodynamic database, the basic candidate component list is used to calculate the target prediction through error propagation theory, and the standard deviation of the target prediction calculation is obtained. By adding sampling points in the high uncertainty region of the standard deviation of the target prediction calculation, a list of candidate components for prediction is obtained; The basic candidate component list is augmented with more sampling points through sensitivity sampling to obtain the predicted candidate component list. This process includes the following steps: Using the phase diagram prediction data in the verified thermodynamic database, the Jacobian row vector of the prediction target with respect to the composition variable is calculated from the basic candidate component list, and the result of the Jacobian row vector calculation of the prediction target with respect to the composition variable is obtained. By adding sampling points to the high-sensitivity regions in the Jacobian row vector calculation results of the prediction target and the component variables, a list of predicted candidate components is obtained.

[0025] The constrained component range definition table is used to calculate candidate components through a high-throughput combinatorial automatic generation algorithm. The corresponding relationship in the process is as follows: ; in, Indicates the number of concentration values. This represents the floor function. Indicates the end value. Indicates the initial value. Indicates the step size. This represents the total number of final component combinations. This represents a product function.

[0026] Using the covariance matrix of thermodynamic model parameters in the validated thermodynamic database, the covariance matrix of the basic candidate component list is used to calculate the target prediction through error propagation theory, yielding the standard deviation of the target prediction calculation. The corresponding relationship in this process is as follows: ; in, Indicators representing uncertainty Indicates standard deviation, Represents the square root function. Represents the covariance matrix. Represents component variables, Represents the transpose matrix. Indicates the target of prediction Relative to thermodynamic model parameters Jacobian row vectors Indicates the target of prediction For the first Thermodynamic model parameters The partial derivatives, Indicates the target of prediction For the first Thermodynamic model parameters The partial derivatives; In the step of calculating the Jacobian row vector of the prediction target versus the composition variable using phase diagram prediction data from the verified thermodynamic database on the basic candidate composition list, the corresponding relationship in the process is as follows: ; in, Indicators of sensitivity Indicates the target of prediction Relative to component variables Jacobian row vectors The L1 norm, Indicates the target of prediction For the first Independent component variables The partial derivatives, Indicates the target of prediction For the first independent component variable The partial derivatives of .

[0027] Step 3: Determine the temperature range for the predicted candidate component list and perform phase equilibrium calculations on the predicted candidate component list to obtain a phase equilibrium data table.

[0028] In step 3, a temperature range is determined for the predicted candidate component list, and phase equilibrium calculations are performed on the predicted candidate component list to obtain a phase equilibrium data table. This specifically includes the following steps: A temperature range was set for the predicted candidate component list, and a single-point equilibrium calculation was performed using the optimized thermodynamic database to obtain the single-point equilibrium calculation results. The phase type and phase fraction are calculated for each element in the predicted candidate component list to obtain the stable phase and the mass fraction of the stable phase; Specific composition calculations are performed on the elements in the predicted candidate composition list to obtain the specific composition of the elements in each stable phase. The Gibbs free energy difference between the metastable phase and the stable phase is calculated for the elements in the predicted candidate component list using thermodynamic driving force, and the possibility of metastable phase formation is evaluated to obtain the evaluation results. The liquidus temperature, solidus temperature and secondary phase precipitation temperature are calculated for each element in the predicted candidate component list, and the results of the liquidus temperature calculation, solidus temperature calculation and secondary phase precipitation temperature calculation are obtained. A phase equilibrium data table is obtained based on the single-point equilibrium calculation results, the stable phases, the mass fraction of the stable phases, the specific composition of elements in each stable phase, the evaluation results, the liquidus temperature calculation results, the solidus temperature calculation results, and the secondary phase precipitation temperature calculation results.

[0029] Step 4: Using a relational database model, the candidate component list and phase equilibrium data table are input into the structured database for storage, resulting in a structured database with stored data.

[0030] Please see Figure 3 ,exist Figure 3In this table, PK represents the primary key, FK represents the foreign key, Element represents the element table, element_id represents the element ID, element_name represents the element name, start, end, and step represent the initial concentration, ending concentration, and step size of the element, respectively; Temperature represents the temperature table, temperature_id represents the temperature ID, and temperature represents the temperature value (unit: °C); Phase represents the phase table, phase_id represents the phase ID, and phase_name represents the phase name; Combination represents the combination table, combination_id represents the combination ID, fractions represent the fractions of elements and phases, and gibbs represents the Gibbs free energy.

[0031] Step 5: Perform phase transition behavior analysis based on the stored data in the structured database after data storage, and generate phase diagrams and data reports.

[0032] Furthermore, based on the calculation results stored in the structured database, a phase diagram showing the phase fraction as a function of temperature is generated, allowing for a visual analysis of phase transition behavior.

[0033] Step 6: Sort and filter the candidate materials according to the phase diagram and data report to obtain a list of candidate materials.

[0034] In step 6, the candidate materials are sorted and filtered according to the phase diagram and data report to obtain a candidate material list, which specifically includes the following steps: A multi-objective screening criterion was established by combining phase diagrams and data reports with material properties and process requirements. The multi-objective screening criterion includes target phase stability, harmful phase suppression, process window, and component tolerance. Candidate materials are sorted and screened based on multi-objective screening criteria to obtain a list of candidate materials.

[0035] Furthermore, target phase stability: a wide single-phase stable region or a region with a small amount of benign secondary phases within the target process temperature range; harmful phase suppression: minimize or avoid the formation of secondary phases known to be detrimental to device performance; process window: a suitable solidus temperature and a wide liquid-solid phase range; composition tolerance: the phase region should have a certain compositional width to allow for compositional fluctuations during the process.

[0036] Step 7: Perform experimental synthesis and performance characterization on the candidate materials in the candidate material list in sequence to obtain an experimental data table; the experimental data table includes phase composition, microstructure and performance data.

[0037] Step 8: Compare the experimental data table with the phase equilibrium data table to obtain the comparison results. Using the comparison results in an iterative manner, adjust the thermodynamic model parameters in Step 1 and the intelligent sampling strategy in Step 2 respectively to obtain the thermodynamic database and the intelligent sampling strategy after feedback adjustment. Based on the thermodynamic database and the intelligent sampling strategy after feedback adjustment, obtain the final prediction results.

[0038] Please see Figure 4 This invention explores the impact of the In / (Ga+In) ratio on the CIGS material system by adjusting the relative proportions of indium and gallium, and optimizes its phase equilibrium performance. First, a thermodynamic database for copper indium gallium selenide (CIGS) thin-film solar cells was constructed, covering the thermodynamic properties of elements such as Cu, In, Ga, and Se, including data on temperature, pressure, enthalpy of formation, and Gibbs free energy. Based on this, a high-throughput computing module was used to set the range and step size of the elemental composition. Cu was fixed at 0.25; Se at 0.5; In was variable from 0 to 0.25 with a step size of 0.025; and Ga was the balance element. Then, the temperature range was set to 800-1100℃ with a step size of 10℃, and four temperature points (800℃, 900℃, 1000℃, and 1100℃) were set. The liquidus and solidus temperatures at different temperatures were calculated, and the phase transition behavior of the CIGS material under different compositions and temperatures was studied, yielding phase diagrams and related data. The data visualization module generated a phase diagram showing the phase fraction as a function of temperature. The analysis showed that the effect was better when In / (Ga+In)≥0.6. Based on this, several CIGS material systems with potentially excellent performance were screened out. Finally, the phase equilibrium relationship and experimental data were combined to guide the synthesis and preparation of actual materials.

[0039] This invention adjusts the proportions of various elements in a CIGS material system and utilizes a high-throughput computing module to automatically generate composition ranges and perform phase equilibrium calculations. First, the ranges and step sizes of the elemental compositions are set: Cu ranges from 0.22 to 0.25 with a step size of 0.01; In ranges from 0.18 to 0.2 with a step size of 0.01; Se ranges from 0.48 to 0.5 with a step size of 0.01; and Ga is the balance element. The temperature range is set from 800°C to 1100°C with a step size of 10°C. The changes in the content of each phase under different compositions and temperatures are calculated. After the calculation results are stored in the data management module, phase diagrams and related data reports at different proportions are generated. By comparing the data of each system, the required composition proportions are identified.

[0040] Please see Figure 5The present invention also provides an optimization system for copper indium gallium selenide thin-film solar cell materials, the system comprising: Thermodynamic database building module, used for: Literature, public databases and experimental data are collected and integrated, and thermodynamic calculations are performed using phase diagram calculation technology to construct a thermodynamic model. The parameters of the thermodynamic model are optimized to obtain a parameter-optimized thermodynamic model. The parameter-optimized thermodynamic model is used to construct a thermodynamic database. The thermodynamic database is then optimized, evaluated and verified in sequence to obtain a verified thermodynamic database. The intelligent sampling module is used for: The system presets the elemental composition range, defines stoichiometric constraints, and uses an intelligent sampling strategy to predict the predicted candidate components by combining a validated thermodynamic database. The phase equilibrium calculation module is used for: A temperature range is determined for the predicted candidate component list, and phase equilibrium calculations are performed on the predicted candidate component list to obtain a phase equilibrium data table; The structured database storage module is used for: A relational database model is used to construct a structured database. The candidate component list and phase equilibrium data table are input into the structured database for storage, resulting in a structured database with stored data. The data visualization module is used for: Phase transition behavior analysis is performed based on the stored data in the structured database after data storage, generating phase diagrams and data reports; The candidate material screening module is used for: Candidate materials are sorted and screened based on phase diagrams and data reports to obtain a list of candidate materials; The candidate materials in the candidate material list were experimentally synthesized and their performance characterized in sequence to obtain an experimental data table; the experimental data table includes phase composition, microstructure and performance data; The experimental data table is compared with the phase equilibrium data table to obtain the comparison results. The thermodynamic model parameters and the intelligent sampling strategy are adjusted iteratively using the comparison results to obtain the thermodynamic database and the intelligent sampling strategy after feedback adjustment. The final prediction results are obtained based on the thermodynamic database and the intelligent sampling strategy after feedback adjustment.

[0041] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0042] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0043] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. An optimization method for copper indium gallium selenide (CIGS) thin-film solar cell materials, characterized in that, The method includes the following steps: Step 1: Collect and integrate literature, public databases and experimental data, and use phase diagram calculation technology to perform thermodynamic calculations to construct a thermodynamic model. Optimize the parameters of the thermodynamic model to obtain the optimized thermodynamic model. Use the optimized thermodynamic model to construct a thermodynamic database. Optimize, evaluate and verify the thermodynamic database in sequence to obtain the verified thermodynamic database. Step 2: Preset the elemental composition range, define stoichiometric constraints, and combine the verified thermodynamic database with an intelligent sampling strategy to predict and obtain a list of predicted candidate components. Step 3: Determine the temperature range for the predicted candidate component list and perform phase equilibrium calculations on the predicted candidate component list to obtain a phase equilibrium data table; Step 4: Using a relational database model, the candidate component list and phase equilibrium data table are input into the structured database for storage, resulting in a structured database with stored data. Step 5: Perform phase transition behavior analysis based on the stored data in the structured database after data storage, and generate phase diagrams and data reports; Step 6: Sort and filter the candidate materials according to the phase diagram and data report to obtain a list of candidate materials; Step 7: Perform experimental synthesis and performance characterization on the candidate materials in the candidate material list in sequence to obtain an experimental data table; the experimental data table includes phase composition, microstructure and performance data; Step 8: Compare the experimental data table with the phase equilibrium data table to obtain the comparison results. Using the comparison results in an iterative manner, adjust the thermodynamic model parameters in Step 1 and the intelligent sampling strategy in Step 2 respectively to obtain the thermodynamic database and the intelligent sampling strategy after feedback adjustment. Based on the thermodynamic database and the intelligent sampling strategy after feedback adjustment, obtain the final prediction results.

2. The method for optimizing the copper indium gallium selenide thin-film solar cell material according to claim 1, characterized in that, In step 1, literature, public databases, and experimental data are collected and integrated. Phase diagram calculations are used to perform thermodynamic calculations to construct a thermodynamic model. The thermodynamic model is then optimized to obtain a parameter-optimized thermodynamic model. This optimized model is used to construct a thermodynamic database. The thermodynamic database is then sequentially optimized, evaluated, and verified to obtain a verified thermodynamic database. Specifically, the steps include: Thermodynamic data and experimental data from literature and public databases are collected and integrated to obtain integrated data. Based on thermodynamic data from literature and public databases in the integrated data, thermodynamic calculations are performed using phase diagram calculation techniques to construct a thermodynamic model. The parameters of the thermodynamic model are then optimized to obtain an optimized thermodynamic model, which is then used to construct a thermodynamic database. Based on thermodynamic data from literature and public databases, the thermodynamic models in the thermodynamic database are evaluated and corrected to obtain corrected thermodynamic models. The corrected thermodynamic models are then verified using experimental data to obtain a verified thermodynamic database.

3. The method for optimizing the copper indium gallium selenide (CIGS) thin-film solar cell material according to claim 2, characterized in that, Based on thermodynamic data from literature and public databases integrated into the dataset, thermodynamic calculations are performed using phase diagram calculation techniques. The corresponding relationships in the process are as follows: ； in, Represents total Gibbs free energy. This indicates taking the minimum value. Indicates the molar content of the phase. This indicates that there exists an arbitrary phase in equilibrium within the system. The Mohr Gibbs free energy, Represents pure elements Compounds exist at equilibrium Chemical sites in the phase Represents pure elements Compounds exist at equilibrium Chemical sites in the phase Represents pure elements Compounds exist at equilibrium Chemical sites in the phase Represents pure elements Compounds exist at equilibrium Chemical sites in the phase Represents pure elements The Mohr Gibbs free energy, , , , , , , and These represent the seven coefficients to be determined. Represents absolute temperature. Represents the natural logarithm. Represents absolute temperature The squared term, Represents absolute temperature The cubic term, Represents absolute temperature The reciprocal of the term, Represents absolute temperature The seventh term, Represents absolute temperature The negative ninth term, Represents pure elements The effect of magnetism on its standard molar Gibbs free energy. This represents the total number of elements in the group. Represents the elements in a group mole fraction, Represents the elements in a group molar free energy, This represents the sum of the free energy components of a mechanical mixture in which there is no interaction between the components. Represents the gas constant. This represents the effect of ideal mixture entropy on free energy. This represents the excess Gibbs free energy in a non-ideal solution.

4. The method for optimizing the copper indium gallium selenide thin-film solar cell material according to claim 3, characterized in that, In step 2, the predefined component range and stoichiometric ratio constraints are used, and a prediction list of predicted candidate components is obtained by combining the validated thermodynamic database with an intelligent sampling strategy. Specifically, the steps include: The elemental composition range and stoichiometric ratio constraints are preset, and the key focus area and sparse exploration area are set according to the performance window of the copper indium gallium selenide device, resulting in a constrained and adjusted composition range definition table. The constrained component range definition table is used to calculate candidate components using a high-throughput combinatorial automatic generation algorithm to obtain a basic candidate component list; The predicted candidate component list can be obtained by adding sampling points to the basic candidate component list through uncertainty sampling, or by adding sampling points to the basic candidate component list through sensitivity sampling.

5. The method for optimizing the copper indium gallium selenide thin-film solar cell material according to claim 4, characterized in that, The basic candidate component list is supplemented with sampling points through uncertainty sampling to obtain the predicted candidate component list, which includes the following steps: Using the covariance matrix of thermodynamic model parameters in the validated thermodynamic database, the basic candidate component list is used to calculate the target prediction through error propagation theory, and the standard deviation of the target prediction calculation is obtained. By adding sampling points in the high uncertainty region of the standard deviation of the target prediction calculation, a list of candidate components for prediction is obtained; The basic candidate component list is augmented with more sampling points through sensitivity sampling to obtain the predicted candidate component list. This process includes the following steps: Using the phase diagram prediction data in the verified thermodynamic database, the Jacobian row vector of the prediction target with respect to the composition variable is calculated from the basic candidate component list, and the result of the Jacobian row vector calculation of the prediction target with respect to the composition variable is obtained. By adding sampling points to the high-sensitivity regions in the Jacobian row vector calculation results of the prediction target and the component variables, a list of predicted candidate components is obtained.

6. The method for optimizing the copper indium gallium selenide thin-film solar cell material according to claim 5, characterized in that, The constrained component range definition table is used to calculate candidate components through a high-throughput combinatorial automatic generation algorithm. The corresponding relationship in the process is as follows: ； in, Indicates the number of concentration values. This represents the floor function. Indicates the end value. Indicates the initial value. Indicates step size, This represents the total number of final component combinations. This represents a product function.

7. The method for optimizing the copper indium gallium selenide thin-film solar cell material according to claim 6, characterized in that, Using the covariance matrix of thermodynamic model parameters in the validated thermodynamic database, the covariance matrix of the basic candidate component list is used to calculate the target prediction through error propagation theory, yielding the standard deviation of the target prediction calculation. The corresponding relationship in this process is as follows: ； in, Indicators representing uncertainty Indicates standard deviation, Represents the square root function. Represents the covariance matrix. Represents component variables, Represents the transpose matrix. Indicates the target of prediction Relative to thermodynamic model parameters Jacobian row vectors Indicates the target of prediction For the Thermodynamic model parameters The partial derivatives, Indicates the target of prediction For the Thermodynamic model parameters The partial derivatives; In the step of calculating the Jacobian row vector of the prediction target versus the composition variable using phase diagram prediction data from the verified thermodynamic database on the basic candidate composition list, the corresponding relationship in the process is as follows: ； in, Indicators of sensitivity Indicates the target of prediction Relative to component variables Jacobian row vectors The L1 norm, Indicates the target of prediction For the Independent component variables The partial derivatives, Indicates the target of prediction For the first independent component variable The partial derivatives of .

8. The method for optimizing the copper indium gallium selenide thin-film solar cell material according to claim 7, characterized in that, In step 3, a temperature range is determined for the predicted candidate component list, and phase equilibrium calculations are performed on the predicted candidate component list to obtain a phase equilibrium data table. Specifically, this includes the following steps: A temperature range was set for the predicted candidate component list, and a single-point equilibrium calculation was performed using the optimized thermodynamic database to obtain the single-point equilibrium calculation results. The phase type and phase fraction are calculated for each element in the predicted candidate component list to obtain the stable phase and the mass fraction of the stable phase; Specific composition calculations are performed on the elements in the predicted candidate composition list to obtain the specific composition of the elements in each stable phase. The Gibbs free energy difference between the metastable phase and the stable phase is calculated for the elements in the predicted candidate component list using thermodynamic driving force, and the possibility of metastable phase formation is evaluated to obtain the evaluation results. The liquidus temperature, solidus temperature and secondary phase precipitation temperature are calculated for each element in the predicted candidate component list, and the results of the liquidus temperature calculation, solidus temperature calculation and secondary phase precipitation temperature calculation are obtained. A phase equilibrium data table is obtained based on the single-point equilibrium calculation results, the stable phases, the mass fraction of the stable phases, the specific composition of elements in each stable phase, the evaluation results, the liquidus temperature calculation results, the solidus temperature calculation results, and the secondary phase precipitation temperature calculation results.

9. The method for optimizing the copper indium gallium selenide thin-film solar cell material according to claim 8, characterized in that, In step 6, the candidate materials are sorted and screened according to the phase diagram and data report to obtain a candidate material list, which specifically includes the following steps: A multi-objective screening criterion was established by combining phase diagrams and data reports with material properties and process requirements. The multi-objective screening criterion includes target phase stability, harmful phase suppression, process window, and component tolerance. Candidate materials are sorted and screened based on multi-objective screening criteria to obtain a list of candidate materials.

10. An optimization system for copper indium gallium selenide (CIGS) thin-film solar cell materials, characterized in that, The system employs the optimization method for copper indium gallium selenide thin-film solar cell materials as described in any one of claims 1 to 9 above, and the system comprises: Thermodynamic database building module, used for: Literature, public databases and experimental data are collected and integrated, and thermodynamic calculations are performed using phase diagram calculation technology to construct a thermodynamic model. The parameters of the thermodynamic model are optimized to obtain a parameter-optimized thermodynamic model. The parameter-optimized thermodynamic model is used to construct a thermodynamic database. The thermodynamic database is then optimized, evaluated and verified in sequence to obtain a verified thermodynamic database. The intelligent sampling module is used for: The system presets the elemental composition range, defines stoichiometric constraints, and uses an intelligent sampling strategy to predict the predicted candidate components by combining a validated thermodynamic database. The phase equilibrium calculation module is used for: A temperature range is determined for the predicted candidate component list, and phase equilibrium calculations are performed on the predicted candidate component list to obtain a phase equilibrium data table; The structured database storage module is used for: A relational database model is used to construct a structured database. The candidate component list and phase equilibrium data table are input into the structured database for storage, resulting in a structured database with stored data. The data visualization module is used for: Phase transition behavior analysis is performed based on the stored data in the structured database after data storage, generating phase diagrams and data reports; The candidate material screening module is used for: Candidate materials are sorted and screened based on phase diagrams and data reports to obtain a list of candidate materials; The candidate materials in the candidate material list were experimentally synthesized and their performance characterized in sequence to obtain an experimental data table; the experimental data table includes phase composition, microstructure and performance data; The experimental data table is compared with the phase equilibrium data table to obtain the comparison results. The thermodynamic model parameters and the intelligent sampling strategy are adjusted iteratively using the comparison results to obtain the thermodynamic database and the intelligent sampling strategy after feedback adjustment. The final prediction results are obtained based on the thermodynamic database and the intelligent sampling strategy after feedback adjustment.

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