High-entropy optoelectronic nanomaterial synthesis method and system

Abstract

The application relates to a high-entropy photoelectric nanomaterial synthesis method and system. First, experimental plan data containing formula parameters and process parameters are generated under the condition of setting a high-entropy constraint; then, liquid-phase synthesis and colloidal purification are performed according to the experimental plan data, high-entropy photoelectric nanomaterial samples are obtained, and synthesis process parameters and purification parameters are recorded; the high-entropy photoelectric nanomaterial samples are subjected to optical characterization and stability testing, and photoelectric performance index parameters are obtained; then, the experimental plan data, the synthesis process parameters, the purification parameters and the photoelectric performance index parameters are stored in association, and a process pedigree is obtained; finally, multi-objective optimization is performed under the condition of setting a high-entropy constraint according to a preset data-driven model and the process pedigree, and optimal formula parameters and optimal process parameters are determined. The scheme replaces manual low-throughput trial and error, significantly compresses the experimental iteration period, and improves the research and development efficiency and reproducibility of high-entropy photoelectric nanomaterials.

Description

Technical Field

[0001] This application relates to the field of materials preparation technology, and in particular to a method and system for synthesizing high-entropy optoelectronic nanomaterials. Background Technology

[0002] Optoelectronic nanomaterials are widely used in display lighting, photoelectric detection, photovoltaics, and sensing due to their tunable bandgap, high luminous efficiency, and strong integrability. In recent years, multi-component high-entropy strategies have been developed by introducing five or more components into target lattice sites and forming a near-uniform molar distribution. By utilizing configurational entropy stabilization and local environment regulation, higher defect tolerance, wider tunability, and potentially better stability can be achieved.

[0003] However, the synthesis of high-entropy optoelectronic nanomaterials typically involves multiple precursors, multiple ligands, and multiple parameter couplings, resulting in a combinatorial explosion in the formulation space. Currently, the research and development of high-entropy optoelectronic nanomaterials mainly relies on human experience and low-throughput trial and error, leading to low research and development efficiency. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and system for synthesizing high-entropy optoelectronic nanomaterials to address the technical problem of low research and development efficiency of high-entropy optoelectronic nanomaterials.

[0005] This application provides a method for synthesizing high-entropy optoelectronic nanomaterials, comprising: generating experimental plan data including formulation parameters and process parameters under set high-entropy constraints; performing liquid-phase synthesis and colloidal purification according to the experimental plan data to obtain high-entropy optoelectronic nanomaterial samples, and recording synthesis process parameters and purification parameters; performing optical characterization and stability testing on the high-entropy optoelectronic nanomaterial samples to obtain optoelectronic performance index parameters; storing the experimental plan data, the synthesis process parameters, the purification parameters, and the optoelectronic performance index parameters in association to obtain a process spectrum; and performing multi-objective optimization under the set high-entropy constraints according to a preset data-driven model and the process spectrum to determine the optimal formulation parameters and the optimal process parameters.

[0006] In one embodiment, the step of performing multi-objective optimization under the set high-entropy constraint based on a preset data-driven model and the process spectrum to determine the optimal formulation parameters and optimal process parameters includes: performing multi-objective optimization under the set high-entropy constraint based on a preset data-driven model and the process spectrum; determining the experimental plan data for the next round if the optimization results show that the preset convergence condition is not met, and returning to the step of performing liquid-phase synthesis and colloidal purification based on the experimental plan data to obtain a high-entropy optoelectronic nanomaterial sample, and recording the synthesis process parameters and purification parameters; and taking the formulation parameters and process parameters corresponding to the current experimental plan data as the optimal formulation parameters and optimal process parameters if the optimization results show that the preset convergence condition is met.

[0007] In one embodiment, the method further includes: determining that a preset convergence condition is met when the photoelectric performance index parameter reaches a preset performance threshold or the number of iterations of the experimental plan data reaches a preset iteration limit.

[0008] In one embodiment, the high entropy constraint is set as follows: the number of components participating in mixing at the target lattice site is greater than or equal to a preset quantity threshold, and the configuration entropy is greater than or equal to a preset configuration entropy threshold; or, the high entropy constraint is set as follows: the number of components participating in mixing at the target lattice site is greater than or equal to a preset quantity threshold, and the normalized entropy is greater than or equal to a preset normalized entropy threshold.

[0009] In one embodiment, the high-entropy constraint further includes: the mole fraction of any component is within a preset fraction range.

[0010] In one embodiment, the high-entropy constraint further includes: the maximum mole fraction deviation of each component is less than or equal to a preset deviation threshold.

[0011] In one embodiment, the process of liquid-phase synthesis and colloidal purification based on the experimental plan data to obtain a high-entropy optoelectronic nanomaterial sample, and recording synthesis and purification parameters, includes: metering precursors, ligands, and solvents according to the formulation parameters, recording raw material batches and concentrations, and generating a feeding event sequence; performing a liquid-phase synthesis reaction according to the process parameters, recording temperature-time curves, stirring speed, and inert atmosphere conditions in real time to obtain a crude product; and performing at least two of the following operations on the crude product: precipitation, centrifugation, washing, redispersion, filtration, and dispensing to obtain a high-entropy optoelectronic nanomaterial sample, and recording centrifugation speed, time, number of washes, and solvent type as purification parameters.

[0012] In one embodiment, the optical characterization and stability testing of the high-entropy optoelectronic nanomaterial sample to obtain optoelectronic performance parameters includes: measuring the absorption and emission spectra of the high-entropy optoelectronic nanomaterial sample to obtain the emission peak position, full width at half maximum (FWHM), and emission intensity as optoelectronic performance indicators; conducting aging tests on the high-entropy optoelectronic nanomaterial sample under humidity, temperature, or light stress conditions, measuring the emission intensity before and after aging, and calculating the intensity retention rate to obtain optical stability indicators; and using the optoelectronic performance indicators and the optical stability indicators together as the optoelectronic performance parameter.

[0013] In one embodiment, the method further includes: monitoring the statistical distribution changes of the photoelectric performance index parameters; triggering a calibration experiment or recalibration when the statistical drift exceeds a preset drift threshold to obtain the correction parameters; and feeding the correction parameters back to the preset data-driven model for model correction.

[0014] This application also provides a high-entropy optoelectronic nanomaterial synthesis system, including a precursor library and formulation execution module, a reaction control module, a post-processing module, a rapid characterization module, a process spectrum data management module, and a closed-loop optimization decision module, wherein the closed-loop optimization decision module is used to execute the steps of the above method.

[0015] The aforementioned method and system for synthesizing high-entropy optoelectronic nanomaterials first generates experimental plan data containing formulation and process parameters under high-entropy constraints. Then, based on the experimental plan data, liquid-phase synthesis and colloidal purification are performed to obtain high-entropy optoelectronic nanomaterial samples, and the synthesis and purification parameters are recorded. Next, the high-entropy optoelectronic nanomaterial samples are subjected to optical characterization and stability testing to obtain optoelectronic performance parameters. Then, the experimental plan data, synthesis parameters, purification parameters, and optoelectronic performance parameters are linked and stored to obtain a process spectrum. Finally, based on a preset data-driven model and process spectrum, multi-objective optimization is performed under high-entropy constraints to determine the optimal formulation and process parameters. This scheme eliminates invalid experiments from the source by directly embedding high-entropy constraints into the formulation generation and optimization process. Simultaneously, the process spectrum is used to structurally link the formulation, process parameters, post-processing parameters, and characterization results, making the data traceable and reproducible. Then, the data-driven model automatically optimizes under multiple objectives (optoelectronic performance and stability) to ultimately output the optimal parameters. The entire process replaces manual low-throughput trial and error, significantly compresses the experimental iteration cycle, and improves the R&D efficiency and reproducibility of high-entropy optoelectronic nanomaterials. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the process for synthesizing high-entropy optoelectronic nanomaterials in one embodiment of this application;

[0018] Figure 2 This is a schematic diagram of the process for synthesizing high-entropy optoelectronic nanomaterials in another embodiment of this application;

[0019] Figure 3 This is a schematic diagram of the post-synthesis processing flow in one embodiment of this application;

[0020] Figure 4 This is a schematic diagram of the performance evaluation process in one embodiment of this application;

[0021] Figure 5 This is a schematic diagram of the process for synthesizing high-entropy optoelectronic nanomaterials in another embodiment of this application. Detailed Implementation

[0022] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings. Preferred embodiments of this application are shown in the drawings. However, this application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of this application.

[0023] The method for synthesizing high-entropy optoelectronic nanomaterials provided in this application can be used to synthesize various high-entropy optoelectronic nanomaterials, including but not limited to high-entropy halide / perovskite nanocrystals, high-entropy chalcogenide or alloy quantum dots, high-entropy multi-component nanocrystals and their core-shell or gradient structures. The scheme in this embodiment integrates formulation generation, automated reaction execution, automated post-processing, rapid optoelectronic characterization and stability assessment, and model-based closed-loop optimization decision-making to achieve high-throughput, traceable, and reproducible material development under high-entropy constraints.

[0024] Please see Figure 1 This application provides a method for synthesizing high-entropy optoelectronic nanomaterials, including steps 101, 102, 103, 104 and 105.

[0025] Step 101: Under the condition of setting high entropy constraints, generate experimental plan data containing formula parameters and process parameters.

[0026] Step 102: Perform liquid-phase synthesis and colloidal purification according to the experimental plan data to obtain high-entropy optoelectronic nanomaterial samples, and record the synthesis process parameters and purification parameters.

[0027] Step 103: Perform optical characterization and stability testing on the high-entropy optoelectronic nanomaterial sample to obtain optoelectronic performance parameters.

[0028] Step 104: Link and store the experimental plan data, synthesis process parameters, purification parameters, and photoelectric performance index parameters to obtain the process spectrum.

[0029] Step 105: Based on the preset data-driven model and process spectrum, perform multi-objective optimization under the set high-entropy constraint to determine the optimal formulation parameters and optimal process parameters.

[0030] Optimal formulation and process parameters are used to synthesize high-entropy optoelectronic nanomaterials. High-entropy constraints refer to the quantitative constraints applied during the generation and screening of candidate formulations to ensure that the material system meets the true high-entropy characteristics. The type of constraint is not unique and can be determined by combining configurational entropy, normalized entropy, or mole fraction, etc., and is not limited here.

[0031] Formulation parameters refer to the adjustable variables related to the components involved in the synthesis (metal source, halogen source, chalcogen source, etc.) and their molar fractions, precursor concentrations, ligand types and amounts, solvent types and amounts, etc. Process parameters refer to the programmable control variables in the synthesis and post-processing, including injection temperature, reaction temperature, reaction time, heating and cooling profiles, stirring speed, inert atmosphere conditions, feeding sequence, precipitant ratio, centrifugation speed and time, number of washing cycles, redispersing solvent, etc.

[0032] Experimental plan data refers to a set of digital instructions output by the closed-loop optimization decision module, containing a specific set of formulation and process parameters. This instruction can be parsed and automatically executed by the formulation execution module and the reaction execution module. Liquid-phase synthesis refers to the process of generating nanocrystals through chemical reactions in a solution phase. This can be achieved using methods such as batch hot-injection reactions, closed-stage reactors, or continuous-flow microreactors. Colloidal purification involves precipitating, centrifuging, washing, redispersing, filtering, and dispensing the crude product obtained from liquid-phase synthesis to remove unreacted precursors, byproducts, and excess ligands, thus obtaining a stable colloidal sample.

[0033] Synthesis process parameters refer to data recorded in real time during the liquid-phase synthesis reaction, including the sequence of feeding events (feeding order, feeding time, feeding volume or mass, feeding flow rate and precursor concentration), temperature-time curves, stirring speed, and inert atmosphere conditions. Purification parameters are the operating conditions recorded during colloidal purification, including the type and volume ratio of precipitant, centrifugation speed and time, number of washes and solvent type, type of redispersing solvent, filtration method and dispensing volume, etc.

[0034] Optical characterization refers to spectral analysis of samples using absorption and emission spectroscopy measurement units, outputting indicators such as emission peak position, full width at half maximum (FWHM), and emission intensity. Optional outputs include quantum yield or lifetime. Stability testing involves aging the sample under humidity, temperature, or light stress conditions, outputting intensity retention rate, decay constant, or peak position shift as stability indicators. Photoelectric performance parameters are numerical performance vectors obtained by combining optical characterization and stability test results, including at least emission peak position, FWHM, emission intensity, and intensity retention rate, used for subsequent multi-objective optimization.

[0035] A process spectrum refers to a data set formed by structurally linking and storing the formulation parameters, raw material batches, feeding event sequences, temperature-time curves, stirring and atmosphere conditions, post-processing parameters, and characterization results of each experiment, enabling full-process traceability. The pre-defined data-driven model is a surrogate model trained based on the existing process spectrum data, used to predict photoelectric performance parameters under different combinations of formulation and process parameters. It can be implemented using frameworks such as Bayesian optimization, active learning, genetic algorithms, or reinforcement learning.

[0036] Multi-objective optimization refers to simultaneously considering at least one photoelectric performance objective (such as maximizing quantum yield or luminescence intensity, minimizing full width at half maximum) and a stability objective (such as maximizing intensity retention) during the optimization process, seeking a Pareto optimal solution set under high entropy constraints. Optimal formulation parameters are a set of formulation parameters determined through multi-objective closed-loop iteration that enables the target performance to meet preset requirements or convergence criteria under the given high entropy constraints. Optimal process parameters are a set of process parameters corresponding to the optimal formulation parameters that can stably reproduce the target performance.

[0037] First, the closed-loop optimization decision-making module generates initial experimental plan data based on set high-entropy constraints. This data includes both formulation parameters (such as mole fraction of each component, precursor concentration, and ligand ratio) and process parameters (such as injection temperature, reaction time, and stirring speed). Subsequently, liquid-phase synthesis is automatically executed according to this experimental plan. For example, precursors are injected into a hot-injection reactor according to a preset feeding sequence, and the temperature curve and stirring status are recorded in real time. After the reaction is completed, colloidal purification is automatically performed, such as adding a precipitant according to a set ratio, centrifuging and washing, while recording purification parameters such as centrifugation speed and number of washings.

[0038] The obtained high-entropy optoelectronic nanomaterial samples immediately undergo optical characterization and stability testing. Absorption and emission spectra provide the emission peak position, full width at half maximum (FWHM), and emission intensity, while humidity or temperature stress aging tests provide the intensity retention rate, collectively constituting the optoelectronic performance parameters. All of the above data—i.e., experimental plan data, synthesis process parameters, purification parameters, and optoelectronic performance parameters—are linked and stored as a process family, ensuring the complete traceability of each round of experiments. Finally, a pre-defined data-driven model (such as a Gaussian process proxy model) uses this process family to learn the mapping relationship between formulation / process and performance, and performs multi-objective optimization (e.g., simultaneously maximizing emission intensity and intensity retention rate, and minimizing FWHM) under the same high-entropy constraints, thereby outputting the optimal formulation parameters and optimal process parameters.

[0039] After this, for this type of high-entropy optoelectronic nanomaterials, they can be repeatedly prepared based on the optimal formulation parameters and optimal process parameters, thus realizing the machine synthesis of high-entropy optoelectronic nanomaterials.

[0040] The aforementioned method for synthesizing high-entropy optoelectronic nanomaterials first generates experimental plan data containing formulation and process parameters under high-entropy constraints. Then, based on the experimental plan data, liquid-phase synthesis and colloidal purification are performed to obtain high-entropy optoelectronic nanomaterial samples, and the synthesis and purification parameters are recorded. Next, the high-entropy optoelectronic nanomaterial samples are subjected to optical characterization and stability testing to obtain optoelectronic performance parameters. Then, the experimental plan data, synthesis parameters, purification parameters, and optoelectronic performance parameters are linked and stored to obtain a process spectrum. Finally, based on a preset data-driven model and process spectrum, multi-objective optimization is performed under high-entropy constraints to determine the optimal formulation and process parameters. This scheme eliminates invalid experiments from the source by directly embedding high-entropy constraints into the formulation generation and optimization process. Simultaneously, the process spectrum is used to structurally link the formulation, process parameters, post-processing parameters, and characterization results, making the data traceable and reproducible. Then, the data-driven model automatically seeks optimization under multiple objectives (optoelectronic performance and stability) to ultimately output the optimal parameters. The entire process replaces manual low-throughput trial and error, significantly compresses the experimental iteration cycle, and improves the R&D efficiency and reproducibility of high-entropy optoelectronic nanomaterials.

[0041] Please see Figure 2 In one embodiment, step 105 includes steps 202, 204 and 206.

[0042] Step 202: Based on the preset data-driven model and process spectrum, perform multi-objective optimization under the set high-entropy constraint.

[0043] Step 204: If the optimization results indicate that the preset convergence conditions are not met, determine the experimental plan data for the next round. Then return to step 102.

[0044] Step 206: If the optimization results determine that the preset convergence conditions are met, the formula parameters and process parameters corresponding to the current experimental plan data are taken as the optimal formula parameters and optimal process parameters.

[0045] The preset convergence criteria refer to the standards used to determine whether the optimization process should be terminated. These include the photoelectric performance parameters reaching a preset performance threshold, the number of iterations of the experimental plan data reaching a preset iteration limit, or other user-defined quantifiable convergence criteria (such as performance improvements falling below a set threshold for multiple consecutive rounds). The experimental plan data for the next round refers to a new set of formulation and process parameters regenerated by the closed-loop optimization decision module based on the updated data-driven model and multi-objective optimization algorithm when the current optimization result is determined not to meet the preset convergence criteria. This new set of parameters is used to trigger the next round of synthesis-characterization-optimization iteration.

[0046] In this embodiment, after multi-objective optimization, the result is not directly output. Instead, a judgment is made based on preset convergence conditions. If the current optimization result does not meet the conditions (e.g., the photoelectric performance index has not reached the target threshold and the number of iterations has not reached the upper limit), the closed-loop optimization decision module automatically determines the experimental plan data for the next round and returns to the steps of liquid-phase synthesis and colloidal purification, forming a new iteration cycle. Conversely, if the current optimization result meets the preset convergence conditions, the formulation parameters and process parameters in the current experimental plan data are output as the optimal result. This mechanism allows the entire R&D process to be continuously and automatically optimized without manual intervention until convergence, avoiding the inefficient operation of repeatedly manually adjusting parameters.

[0047] The above scheme, after performing multi-objective optimization based on a preset data-driven model and process family, determines whether the preset convergence conditions are met. If not, it automatically determines the next round of experimental plan data and returns to the synthesis step, forming a closed-loop iteration. If the conditions are met, the formulation and process parameters corresponding to the current experimental plan data are taken as the optimal result. This achieves continuous optimization and automatic convergence without human intervention, avoiding the inefficiency of repeated manual adjustments and significantly accelerating the convergence speed of the optimal formulation and process.

[0048] In one embodiment, the method further includes: determining that a preset convergence condition is met when the photoelectric performance index parameter reaches a preset performance threshold or the number of iterations of the experimental plan data reaches a preset iteration limit.

[0049] Preset performance thresholds refer to the specific values ​​or ranges of optoelectronic performance parameters that users set according to the target application requirements, such as quantum yield ≥80%, luminescence intensity retention ≥85%, and full width at half maximum (FWHM) ≤35nm, which serve as one of the conditions for determining convergence. Preset iteration upper limits are the maximum number of iterations set to avoid infinite loops or excessive resource consumption during the optimization process.

[0050] After each round of optimization, two independent conditions are checked: first, whether the photoelectric performance parameters of the sample corresponding to the current experimental plan data have reached the preset performance threshold; second, whether the number of iterations of the experimental plan data has reached the preset iteration limit. As long as either condition is met, the closed-loop optimization is determined to have converged and the iteration stops, and the current optimal formula parameters and process parameters are used as the final output. If neither condition is met, the next round of experimental plan is generated.

[0051] The above scheme determines that a preset convergence condition is met when the photoelectric performance parameters reach a preset performance threshold or the number of iterations of the experimental plan data reaches a preset iteration limit. This provides a clear termination criterion for the optimization process: output results promptly when performance targets are met to avoid over-iteration; and terminate appropriately when the iteration limit is reached to prevent resource waste.

[0052] In one embodiment, the high entropy constraint is set as follows: the number of components participating in mixing at the target lattice site is greater than or equal to a preset quantity threshold, and the configuration entropy is greater than or equal to a preset configuration entropy threshold; or, the high entropy constraint is set as follows: the number of components participating in mixing at the target lattice site is greater than or equal to a preset quantity threshold, and the normalized entropy is greater than or equal to a preset normalized entropy threshold.

[0053] The preset quantity threshold refers to the lower limit of the number of components required to participate in mixing at the target lattice site under high entropy constraints. For example, in one embodiment, this value is at least 5. Configuration entropy refers to the thermodynamic entropy generated by the mixing of multiple components at the lattice site. For example, in one embodiment, configuration entropy can be expressed as… ,in, Represents configuration entropy. This indicates the number of components that participate in mixing at the target lattice site. Represents the ideal gas constant. Indicates the first The mole fractions of all components, and the sum of the mole fractions of all components is 1. In one embodiment, the high-entropy constraint can be expressed as: and , This indicates the preset configuration entropy threshold.

[0054] Normalized entropy refers to the dimensionless value obtained by dividing the configuration entropy by the maximum possible entropy. The calculation formula can be expressed as follows: ,in, Represents the normalized entropy. Indicates the first The mole fraction of each component, This indicates the number of components involved in mixing at the target lattice site. The preset normalized entropy threshold refers to the minimum allowable value set for the normalized entropy. Its value is not unique. For example, in one embodiment, it can be 0.80–0.98, such as 0.8, 0.85, 0.9, 0.95, 0.98, etc.

[0055] Here are two optional high-entropy constraint forms. The first requires that the number of components n participating in the mixture is not less than a preset quantity threshold, and the configuration entropy is not less than a preset configuration entropy threshold. The second also requires that the number of components n participating in the mixture is not less than a preset quantity threshold, but uses normalized entropy H as the metric, requiring that H is not less than a preset normalized entropy threshold. Both constraints can be used independently.

[0056] The above scheme explicitly defines the high-entropy constraint as either the number of components being greater than or equal to a preset quantity threshold and the configuration entropy being greater than or equal to a preset configuration entropy threshold, or the number of components being greater than or equal to a preset quantity threshold and the normalized entropy being greater than or equal to a preset normalized entropy threshold. By quantifying the entropy threshold, the true high-entropy characteristics of the material system are guaranteed at the quantitative level, effectively eliminating pseudo-high-entropy formulations and improving the scientific rigor of candidate formulation screening and the directionality of the optimization process.

[0057] In one embodiment, the high-entropy constraint further includes: the mole fraction of any component is within a preset fraction range.

[0058] The preset fraction interval refers to the mole fraction of any component. The allowed value range, for example, in one embodiment, can be 0.05-0.35, more preferably 0.08-0.25.

[0059] Based on the above-mentioned schemes involving configuration entropy or normalized entropy constraints, this embodiment further adds restrictions on the mole fraction range for both constraints. That is, when the number of components participating in mixing at the target lattice site is greater than or equal to a preset quantity threshold, and the configuration entropy is greater than or equal to a preset configuration entropy threshold, the mole fraction of any component must also be within a preset fraction range; or when the number of components participating in mixing at the target lattice site is greater than or equal to a preset quantity threshold, and the normalized entropy is greater than or equal to a preset normalized entropy threshold, the mole fraction of any component must also be within a preset fraction range.

[0060] The above scheme further limits the molar fraction of any component to within a preset range to prevent the system from deviating from high-entropy mixing due to an excessively high proportion of a certain component. This constraint strengthens the uniform distribution of multiple components on lattice sites, enhances the configuration entropy stabilization effect, and thus improves the consistency of photoelectric performance and process robustness of high-entropy optoelectronic nanomaterials.

[0061] In one embodiment, the high-entropy constraint further includes: the maximum mole fraction deviation of each component is less than or equal to a preset deviation threshold.

[0062] Maximum mole fraction deviation refers to the deviation of the mole fraction of each component from the equimolar ratio. The maximum value of the absolute values ​​of the deviations between them can be expressed by the following formula: ,in, This indicates the maximum deviation in mole fraction. Indicates the first The absolute deviation of the mole fraction of each component from the equimolar ratio reflects the degree to which the component deviates from a uniform distribution. This indicates that the maximum value is taken for all components. The preset deviation threshold refers to the upper limit allowed for the deviation of the maximum mole fraction. For example, in one embodiment, its value is 0.10-0.20.

[0063] This embodiment further introduces a uniformity index constraint. Specifically, in addition to satisfying the requirements that the number of components n≥5, the entropy value meets the standard, and the mole fraction of each component is within a preset range, it also requires that the maximum deviation of the mole fraction of all components from the equimolar ratio does not exceed a preset deviation threshold.

[0064] The above scheme limits the maximum mole fraction deviation of each component to less than or equal to a preset deviation threshold, forcing the mole fraction of each component to approach the equimolar average. This uniformity constraint further suppresses local segregation or behavior dominated by a few components, making the microstructure of high-entropy optoelectronic nanomaterials more uniform, which helps to obtain narrower emission half-width, more stable emission intensity, and better reproducibility.

[0065] Please see Figure 3In one embodiment, step 102 includes steps 301, 302 and 303.

[0066] Step 301: Measure the precursor, ligand and solvent according to the formulation parameters, record the batch and concentration of raw materials, and generate a feeding event sequence.

[0067] Step 302: Perform liquid-phase synthesis reaction according to process parameters, record temperature-time curves, stirring speed and inert atmosphere conditions in real time, and obtain crude product.

[0068] Step 303 involves performing at least two of the following operations on the crude product: precipitation, centrifugation, washing, redispersion, filtration, and dispensing, to obtain a high-entropy optoelectronic nanomaterial sample. The centrifugation speed, time, number of washes, and solvent type are recorded as purification parameters.

[0069] Precursors refer to the raw materials used to synthesize high-entropy optoelectronic nanomaterials, including metal sources (such as salts or complexes of Pb, Sn, Mn, etc.), halogen sources, chalcogen sources, ligands (such as oleic acid and oleylamine), and solvents (such as octadecene and octene). These are stored in a precursor library and their concentration, batch size, and expiration date are managed. Ligands are organic molecules used to stabilize the nanocrystal surface and control growth kinetics and dispersion, such as oleic acid, oleylamine, and phosphonic acid. Solvents are liquids used to dissolve the precursors and serve as the reaction medium, such as high-boiling-point nonpolar solvents (octadecene), ethers, or aromatic solvents.

[0070] Raw material batch and concentration refer to the unique barcode or identifier recorded for each batch of precursor and its actual concentration value, used to trace the impact of raw material differences on experimental results. The feeding event sequence is time-series data formed by recording the feeding order, time of each feeding, feeding volume or mass, feeding flow rate, and precursor concentration information according to the experimental plan. The temperature-time curve refers to the data curve showing the change in reaction system temperature over time, recorded in real time by a temperature sensor during the liquid-phase synthesis reaction, including the heating phase, isothermal phase, and cooling phase.

[0071] The stirring speed refers to the revolutions per minute (rpm) set by the stirrer during the reaction process, which can be 200 rpm to 2000 rpm, used to control the uniformity of mixing. The inert atmosphere condition refers to the type of protective gas (such as nitrogen or argon) introduced to prevent interference from water and oxygen, and its flow rate or pressure. A vacuum degassing process can be selected. The crude product refers to the initial reaction mixture after the liquid-phase synthesis reaction has ended, without purification, containing the target nanocrystals, unreacted precursors, byproducts, and excess ligands.

[0072] Precipitation, centrifugation, washing, redispersion, filtration, and dispensing are the basic operational units of colloidal purification. Precipitation involves adding a precipitant to cause nanocrystals to precipitate; centrifugation uses centrifugal force to separate the solid and liquid phases; washing replaces residual impurities with fresh solvent; redispersion redisperses the centrifuged precipitate into the target solvent; filtration removes large particle agglomerates; and dispensing involves dispensing the final sample into portions for storage as needed.

[0073] Centrifugation speed, time, number of washes, and solvent type are all key parameters in the purification process. For example, in one embodiment, the centrifugation speed is 2000 rpm-12000 rpm, the centrifugation time is 1 minute-20 minutes, the number of washes is 1-5 times, and the solvent types include precipitants (such as alcohols or esters), washing solvents, and redispersing solvents (such as non-polar solvents).

[0074] This embodiment automatically measures the required precursors, ligands, and solvents from a pre-set precursor library according to the formulation parameters. It simultaneously records the batch number and measured concentration of each raw material and generates a feeding event sequence according to the feeding order in the experimental plan (e.g., injecting the A-site source first, then the B-site metal mixture, and finally the halogen source). Next, the liquid-phase synthesis reaction is controlled according to process parameters, and the heating module is adjusted in real time to achieve the preset temperature-time curve. Simultaneously, the set stirring speed is maintained, and an inert atmosphere (such as argon) is continuously introduced. All data are recorded throughout the process. After the reaction, a crude product containing nanocrystals is obtained. Finally, an automatic colloidal purification process is executed: a precipitant is added according to a preset ratio, centrifuged, the supernatant is discarded, a washing solvent is added, and washing is repeated. Finally, the precipitate is redispersed in the target solvent, with optional filtration and dispensing. During this process, the centrifugation speed, time, number of washes, and the type of solvent used each time are all recorded as purification parameters.

[0075] The above-mentioned scheme automates the entire process and records the synthesis and purification parameters in a structured manner, which significantly reduces batch variations caused by manual operation and provides highly reliable process lineage data for subsequent data-driven modeling and closed-loop optimization.

[0076] Please see Figure 4 In one embodiment, step 103 includes steps 402, 404 and 406.

[0077] Step 402: Measure the absorption and emission spectra of the high-entropy optoelectronic nanomaterial sample to obtain the emission peak position, full width at half maximum (FWHM), and emission intensity as indicators of optoelectronic performance.

[0078] Step 404: The high-entropy optoelectronic nanomaterial sample is subjected to aging tests under humidity, temperature or light stress conditions. The luminescence intensity before and after aging is measured and the intensity retention rate is calculated to obtain the optical stability index.

[0079] Step 406: Use both photoelectric performance indicators and optical stability indicators as photoelectric performance parameters.

[0080] Absorption spectroscopy measures the absorption intensity of a sample to light of different wavelengths, used to analyze the band gap, size distribution, and absorption peak positions of nanocrystals. Emission spectroscopy measures the emission spectrum of a sample under excitation at a specific wavelength, used to obtain the emission peak position, full width at half maximum (FWHM), and emission intensity. The emission peak position refers to the wavelength or energy corresponding to the maximum intensity in the emission spectrum, reflecting the band gap and composition of the nanocrystals. The FWHM is the peak width (usually in nm or eV) corresponding to half the emission peak intensity, characterizing the sample's size distribution and compositional uniformity; a smaller value indicates better monodispersity. The emission intensity refers to the peak intensity of the emission spectrum, which can be compared with a reference sample to obtain the relative quantum efficiency.

[0081] Humidity, temperature, or light stress conditions refer to the environmental stresses used in accelerated aging tests. For example, humidity can be controlled between 20%RH (relative humidity) and 90%RH, temperature can be set between 20°C and 120°C, and light intensity can be set to simulate sunlight or a specific wavelength light source. Aging tests involve placing the sample under these stress conditions for a preset time and periodically measuring changes in its luminescence intensity. Intensity retention rate refers to the ratio (usually expressed as a percentage) of the sample's luminescence intensity after the aging test to its initial luminescence intensity, used to quantify stability.

[0082] Optical stability indicators refer to the strength retention rate, decay constant (fitted exponential decay curve), or peak shift obtained from aging tests, which serve as targets for stability optimization.

[0083] In the optical characterization section, the absorption spectrum of the high-entropy optoelectronic nanomaterial sample after colloidal purification is first measured to obtain the absorption peak position to help determine the band gap. Then, the emission spectrum is measured to extract three core indicators: emission peak position (used to confirm the target emission wavelength), half-maximum width at half maximum (used to evaluate size and composition uniformity), and emission intensity (used to compare quantum efficiency). In the stability test section, the system places the sample under controlled humidity (e.g., 60%RH), temperature (e.g., 85℃), or light (e.g., 1 Sun intensity) stress conditions for aging tests. The emission intensity is measured at set time intervals (e.g., 0, 1, 2, 4, 8, 24, 48 hours), and the intensity retention rate at each time point is calculated. The decay constant or peak position shift can be further fitted as an optical stability indicator. Finally, this method incorporates the above-mentioned stability indicators, such as emission peak position, half-maximum width at half maximum, emission intensity (as an optoelectronic performance indicator), and intensity retention rate, into the optoelectronic performance parameter set for subsequent multi-objective optimization.

[0084] The above scheme first measures the absorption and emission spectra to obtain the emission peak position, full width at half maximum (FWHM), and emission intensity as photoelectric performance indicators. Then, aging tests are conducted under humidity, temperature, or light stress conditions to measure the emission intensity before and after aging and calculate the intensity retention rate, yielding an optical stability indicator. Finally, the photoelectric performance indicators and optical stability indicators are combined as the photoelectric performance parameter. This combination allows multi-objective optimization to simultaneously consider luminous efficiency, spectral quality, and long-term stability, better meeting the practical needs of high-entropy optoelectronic nanomaterials in engineering applications such as displays and lighting.

[0085] Please see Figure 5 In one embodiment, the method further includes steps 502, 504 and 506.

[0086] Step 502: Monitor the statistical distribution changes of photoelectric performance parameters.

[0087] Step 504: If the statistical drift is detected to exceed the preset drift threshold, a calibration experiment or recalibration is triggered to obtain the correction parameters.

[0088] Step 506: Feed back the correction parameters to the preset data-driven model for model correction.

[0089] Statistical distribution change refers to the changing trend of statistical characteristics such as mean, variance, and quantiles of photoelectric performance parameters (e.g., luminous intensity, half-width at half-maximum) monitored by the system in multiple repeated or continuous experiments as the number of experimental rounds increases. Statistical drift refers to the change in statistical distribution exceeding the normal fluctuation range, which may be caused by batch differences in raw materials, changes in environmental temperature and humidity, equipment aging, or the accumulation of system errors. The preset drift threshold is a quantitative limit set by the user to determine whether statistical drift has reached the level requiring intervention, such as a mean deviation of luminous intensity exceeding 3 standard deviations, or a standard deviation of half-width at half-maximum increasing by more than 50%.

[0090] Calibration experiments refer to a set of standardized experiments automatically triggered by the system when statistical drift is detected. These experiments are used to recalibrate equipment parameters, verify the effectiveness of raw materials, or correct system models. Recalibration is an operation performed in parallel with or as a follow-up to calibration experiments. It involves re-measuring and resetting parameters in measuring equipment (such as spectrometers and temperature sensors), actuators (such as feed pumps and heating modules), or data models to eliminate detected drift. Correction parameters are values ​​obtained through calibration experiments used to correct data-driven models or adjust equipment execution commands, such as reactor temperature deviation compensation values ​​and precursor concentration correction factors.

[0091] In this embodiment, after each round or every few rounds of experiments, the statistical distribution changes of photoelectric performance parameters are automatically monitored. For example, the system can maintain a sliding window database to store the luminescence intensity, full width at half maximum (FWHM), and intensity retention rate measured in the most recent N experiments, and calculate the mean, standard deviation, and trend line of these indicators in real time. These statistics are compared with historical benchmark distributions (e.g., a benchmark established in the first 50 rounds of experiments) to determine whether statistical drift exists. When the detected drift exceeds a preset drift threshold (e.g., the mean of luminescence intensity continues to decrease and the cumulative deviation exceeds 3 standard deviations, or the standard deviation of FWHM suddenly doubles), a calibration experiment or recalibration process is immediately and automatically triggered.

[0092] Calibration experiments can involve synthesizing and characterizing a set of standard reference samples to verify whether the equipment deviates from its original state. Recalibration may include zero-point calibration of the temperature sensor, recalibration of the spectrometer wavelength, or remeasurement of the raw material concentration in the precursor library. Through these operations, the system obtains a set of specific calibration parameters, such as temperature offset, spectral intensity correction factor, or precursor concentration correction value. Finally, the system feeds these calibration parameters back to the preset data-driven model for model correction: for example, adding offset compensation to the temperature variable at the model's input layer, multiplying the predicted luminescence intensity by a correction factor at the output layer, or directly updating the likelihood function in the Gaussian process model to reflect the new noise level.

[0093] The above solution, through anomaly detection and drift correction mechanisms, effectively addresses distribution drift caused by environmental changes, raw material batch differences, or equipment aging during long-term operation, maintains the predictive accuracy of the data-driven model, and improves the robustness and long-term reliability of the closed-loop optimization system.

[0094] This application also provides a high-entropy optoelectronic nanomaterial synthesis system, including a precursor library and formulation execution module, a reaction control module, a post-processing module, a rapid characterization module, a process spectrum data management module, and a closed-loop optimization decision module, which is used to execute the steps of the above method.

[0095] The synthesis method of high-entropy optoelectronic nanomaterials is as shown in the above embodiments and figures, and will not be repeated here. In the precursor library and formulation execution module, the precursor library is used to store various metal sources, halogen sources, chalcogen sources, ligands and solvents, and to manage the concentration, batch and expiration date. The formulation execution module is used to automatically meter and add each precursor according to the experimental plan.

[0096] The reaction control module is used to achieve programmable control of the liquid-phase synthesis reaction. It can include any one or more of the following: batch hot injection reactor, closed-loop reactor, or continuous flow microreactor. It can programmatically control the injection temperature, reaction temperature, reaction time, heating and cooling profiles, stirring speed, and inert atmosphere. The post-processing module is configured to automatically perform at least two of the following operations: precipitation, centrifugation, washing, redispersion, filtration, and dispensing, and record the corresponding post-processing parameters.

[0097] The rapid characterization module includes an absorption spectroscopy measurement unit and a emission spectroscopy measurement unit, used to output emission peak position, full width at half maximum (FWHM), and emission intensity indicators, and optionally output quantum yield or lifetime indicators; it may further include a stability assessment unit, used to output intensity retention rate, decay constant, or peak position drift as stability indicators under humidity, temperature, or light stress conditions. The process lineage data management module is used to structurally associate and store the formulation parameters, raw material batches, feeding event sequences, temperature-time curves, stirring and atmosphere conditions, post-processing parameters, and characterization results of each experiment, forming a traceable process lineage database.

[0098] The closed-loop optimization decision module is configured to apply high-entropy constraints or high-entropy penalties during the candidate formulation generation and iterative update process, perform multi-objective optimization, and automatically translate the next round of formulation and process parameters output by the algorithm into equipment instructions to trigger the next round of synthesis and characterization until the preset convergence conditions are met.

[0099] The closed-loop optimization decision-making module generates an initial experimental plan (including formulation and process parameters) under high-entropy constraints and sends the plan to the precursor library and formulation execution module. The latter automatically selects the specified batches of precursors, ligands, and solvents from the library for metering and feeding. The reaction control module controls the reactor temperature, time, stirring, and atmosphere according to the process parameters to execute the liquid-phase synthesis reaction. The obtained crude product is automatically transported to the post-processing module, which performs purification operations such as precipitation, centrifugation, washing, and redispersion, and records the purification parameters. The purified sample enters the rapid characterization module for absorption spectroscopy, emission spectroscopy measurement, and stability aging test, and outputs photoelectric performance parameters.

[0100] All the aforementioned data, including formulation parameters, process parameters, purification parameters, and characterization results, are sent to the process spectrum data management module for structured and correlated storage. The closed-loop optimization decision module reads the process spectrum data, updates the preset data-driven model, and performs multi-objective optimization under high-entropy constraints to generate the next round of experimental plans, which are then executed again through the various modules. In this way, a fully automated closed loop is achieved from formulation generation to performance feedback and optimization decision-making, completely replacing the manual low-throughput trial-and-error mode.

[0101] The above-mentioned scheme, through the collaborative work of various modules, enables the system to achieve a fully integrated process from formulation generation, automated synthesis and purification, rapid characterization and stability testing to data-driven multi-objective closed-loop optimization under high entropy constraints. It completely replaces the traditional manual low-throughput trial-and-error mode and significantly improves the R&D efficiency, repeatability and process traceability of high-entropy optoelectronic nanomaterials.

[0102] To facilitate understanding, this application will be explained and described below with reference to more detailed embodiments.

[0103] Example 1: The target high-entropy optoelectronic nanomaterial is a high-entropy halide or perovskite nanocrystal, and its high-entropy mixing sites can be B-site cation sites, X-site halogen sites, or a mixture of both.

[0104] The precursor library includes A-site sources (e.g., Cs, MA, FA-related precursors), multiple B-site metal sources (no fewer than five, typically including any combination of Pb, Sn, Ge, Mn, Zn, Cd, Bi, Sb, Cu, Ag, etc., depending on the system's solid solubility), halogen sources (Cl / Br / I and their salts or complexes), ligands (e.g., oleic acid, oleylamine and their combinations), and high-boiling-point solvents (e.g., octene / octadecene / ethers or aromatic solvents). The precursor concentration is preferably in the range of 0.01 mol / L – 1.0 mol / L, more preferably 0.05 mol / L – 0.50 mol / L. The concentration is calibrated and batch information is recorded.

[0105] The closed-loop optimization decision module generates initial experimental plan data under the premise of meeting the set high-entropy constraints. The preferred high-entropy constraints are that the number of components is no less than five, the normalized entropy is no less than a preset threshold, and the mole fraction of each component is limited to between 0.05 and 0.35. The experimental plan data jointly encodes formulation parameters and process parameters. Formulation parameters include the mole fraction of each component, precursor concentration, ligand ratio, etc.; process parameters include at least the injection temperature, reaction temperature, reaction time, heating rate, stirring speed, ligand to metal molar ratio, halogen ratio, and post-treatment parameters. For batch hot injection reactions, the injection temperature can be set between 80℃ and 220℃, the reaction temperature between 20℃ and 200℃, and the reaction time between 5 seconds and 60 minutes; for continuous flow microreactors, the residence time can be set between 0.5 seconds and 20 minutes, and the flow rate and mixing ratio are controlled by the formulation execution module. The stirring speed can be set in the range of 200rpm–2000rpm, and the inert atmosphere can be nitrogen or argon. The reaction system can be equipped with a vacuum degassing process to reduce water and oxygen interference.

[0106] The liquid-phase synthesis is automatically executed according to the experimental plan data: the feeding sequence is automatically metered and executed, and the time, volume, concentration, and flow rate of each feeding are recorded as synthesis process parameters, along with temperature-time curves and stirring / atmosphere conditions. After the liquid-phase synthesis reaction is completed, the post-processing module performs colloidal purification according to preset rules: the precipitant can be an alcohol or ester solvent, the preferred precipitant-to-reaction liquid volume ratio is 0.5:1–10:1, the preferred centrifugation speed is 2000 rpm–12000 rpm, the preferred centrifugation time is 1 minute–20 minutes, the number of washings is 1–5 times, and the redispersing solvent can be a non-polar solvent or the target application solvent. Finally, all the above purification parameters are fully recorded and correlated with the formulation parameters and synthesis process parameters.

[0107] The rapid characterization module performs optical characterization and stability testing on the obtained high-entropy optoelectronic nanomaterial samples: it measures absorption and emission spectra, extracts optoelectronic performance indicators such as emission peak position, full width at half maximum (FWHM), and emission intensity, and optionally measures quantum yield and lifetime; the stability test is carried out under controlled humidity (20%RH–90%RH), temperature (20℃–120℃), or set light intensity, and outputs the intensity retention rate after a preset time (e.g., 1 hour–168 hours) as an optical stability indicator, which together constitute the optoelectronic performance parameters.

[0108] All the above data are linked and stored to obtain the process spectrum. Closed-loop optimization is performed in the form of multi-objective optimization, aiming to simultaneously maximize quantum yield or intensity retention rate and minimize half-maximum width. Under the condition of setting high entropy constraints, the system outputs the experimental plan data for the next round based on the preset data-driven model and process spectrum. Through iteration, the system obtains the optimal formulation parameters and optimal process parameters of the high-entropy optoelectronic nanomaterial that meet the target performance, and solidifies the corresponding process spectrum for reproduction experiments or scale-up verification.

[0109] Example 2: The target high-entropy optoelectronic nanomaterial is a high-entropy chalcogenide or alloy quantum dot, and its high-entropy mixed sites can be cation sites or multi-site coupling.

[0110] The precursor library includes at least five metal source precursors (which may be metal salts, organometallic compounds, metal carboxylates, or complexes, etc.), sulfur / selenium / tellurium sources (which may be organosulfur / selenium / tellurium sources, phosphorus ligand-complexed chalcogen sources, or silane chalcogen sources, etc.), ligand systems (amines, acids, phosphines, and combinations thereof), and solvent systems. The total metal concentration is preferably 0.01 mol / L–1.0 mol / L, more preferably 0.05 mol / L–0.50 mol / L; the molar ratio of chalcogen sources to total metal is preferably 0.5–5.0, more preferably 0.8–2.0; the molar ratio of ligands to metal is preferably 0.5–20, more preferably 1–10. The high-entropy constraint is preferably set with at least five components and a normalized entropy H ≥ 0.85, and the molar fraction of each metal component is limited to the range of 0.05–0.35. If necessary, a maximum molar fraction deviation constraint is applied to ensure uniformity.

[0111] The closed-loop optimization decision module generates initial experimental plan data under the set high-entropy constraints. The formulation parameters in the experimental plan data include the molar fraction of each metal component, the ratio of chalcogen source to metal, and the ratio of ligand to metal; process parameters can be set according to the reaction route. Liquid-phase synthesis can be achieved using hot injection, one-pot heating, solvothermal, or continuous flow microreactors. For the hot injection route, the preferred injection temperature is 120℃–330℃, the preferred reaction temperature is 80℃–320℃, and the preferred reaction time is 10 seconds–120 minutes, with nucleation and growth kinetics controlled by a heating rate of 0.5℃–30℃ / min; for the continuous flow route, the preferred residence time is 0.5 seconds–60 minutes, and the nucleation window can be controlled by an online mixing module. The system automatically executes the feeding sequence and records the feeding event sequence, temperature-time curves, stirring speed, and inert atmosphere conditions as synthesis process parameters; it automatically completes colloidal purification and dispensing, and records purification parameters such as centrifugation speed, time, number of washes, and solvent type.

[0112] The rapid characterization module performs optical characterization and stability testing on the obtained high-entropy optoelectronic nanomaterial samples: absorption and emission spectra are measured, and emission peak position, half-peak width, and emission intensity are extracted as optoelectronic performance indicators; the stability test uses the intensity retention rate and peak position drift under air exposure or light stress conditions as optical stability indicators, which together constitute the optoelectronic performance index parameters.

[0113] All the above data are linked and stored to obtain the process spectrum. Closed-loop optimization is carried out in the form of multi-objective optimization, which aims to simultaneously maximize luminescence intensity or quantum yield and maximize intensity retention under stress conditions, while minimizing the full width at half maximum (FWHM) or defect correlation ratio. Under the condition of setting high entropy constraints, the system outputs the experimental plan data for the next round based on the preset data-driven model and process spectrum. Through iteration, the system obtains the optimal formulation parameters and optimal process parameters of high-entropy optoelectronic nanomaterials that meet the performance and stability requirements, and can be further extended to core-shell or gradient structures by introducing high-entropy mixing into the core, shell, or interface layer and jointly optimizing the process parameters under the same closed-loop framework.

[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for synthesizing high-entropy optoelectronic nanomaterials, characterized in that, include: Under high-entropy constraints, experimental plan data containing formulation parameters and process parameters is generated. Based on the experimental plan data, liquid-phase synthesis and colloidal purification were performed to obtain high-entropy optoelectronic nanomaterial samples, and the synthesis process parameters and purification parameters were recorded. The high-entropy optoelectronic nanomaterial sample was subjected to optical characterization and stability testing to obtain optoelectronic performance parameters. The experimental plan data, the synthesis process parameters, the purification parameters, and the photoelectric performance index parameters are associated and stored to obtain the process spectrum. Based on the preset data-driven model and the process spectrum, multi-objective optimization is performed under the set high-entropy constraint to determine the optimal formulation parameters and optimal process parameters.

2. The method according to claim 1, characterized in that, The step of determining the optimal formulation parameters and optimal process parameters by performing multi-objective optimization under the set high-entropy constraint conditions based on the preset data-driven model and the process spectrum includes: Based on the preset data-driven model and the process spectrum, multi-objective optimization is performed under the set high-entropy constraint conditions. If the optimization results indicate that the preset convergence conditions are not met, determine the experimental plan data for the next round, and return to the steps of performing liquid-phase synthesis and colloidal purification based on the experimental plan data to obtain high-entropy optoelectronic nanomaterial samples, and record the synthesis process parameters and purification parameters. If the optimization results determine that the preset convergence conditions are met, the formula parameters and process parameters corresponding to the current experimental plan data are taken as the optimal formula parameters and optimal process parameters.

3. The method according to claim 2, characterized in that, The method further includes: determining that a preset convergence condition is met when the photoelectric performance index parameter reaches a preset performance threshold or the number of iterations of the experimental plan data reaches a preset iteration limit.

4. The method according to claim 1, characterized in that, The set high-entropy constraint condition is: the number of components participating in mixing at the target lattice site is greater than or equal to a preset number threshold, and the configuration entropy is greater than or equal to a preset configuration entropy threshold; or, The set high-entropy constraint condition is: the number of components participating in mixing at the target lattice site is greater than or equal to a preset number threshold, and the normalized entropy is greater than or equal to a preset normalized entropy threshold.

5. The method according to claim 4, characterized in that, The high-entropy constraint also includes: the mole fraction of any component is within a preset fraction range.

6. The method according to claim 5, characterized in that, The high-entropy constraint also includes: the maximum mole fraction deviation of each component is less than or equal to a preset deviation threshold.

7. The method according to any one of claims 1-6, characterized in that, The process involves liquid-phase synthesis and colloidal purification based on the experimental plan data to obtain high-entropy optoelectronic nanomaterial samples, and the synthesis and purification parameters are recorded, including: The precursor, ligand and solvent are measured according to the formulation parameters, the batch and concentration of raw materials are recorded, and a feeding event sequence is generated. The liquid-phase synthesis reaction was carried out according to the process parameters, and the temperature-time curve, stirring speed and inert atmosphere conditions were recorded in real time to obtain the crude product. The crude product is subjected to at least two of the following operations: precipitation, centrifugation, washing, redispersion, filtration, and dispensing, to obtain a high-entropy optoelectronic nanomaterial sample. The centrifugation speed, time, number of washes, and solvent type are recorded as purification parameters.

8. The method according to any one of claims 1-6, characterized in that, The optical characterization and stability testing of the high-entropy optoelectronic nanomaterial sample yields optoelectronic performance parameters, including: The absorption and emission spectra of the high-entropy optoelectronic nanomaterial sample were measured to obtain the emission peak position, full width at half maximum (FWHM), and emission intensity as indicators of optoelectronic performance. The high-entropy optoelectronic nanomaterial sample was subjected to aging tests under humidity, temperature or light stress conditions. The luminescence intensity before and after aging was measured and the intensity retention rate was calculated to obtain the optical stability index. The photoelectric performance index and the optical stability index are used together as the photoelectric performance index parameter.

9. The method according to any one of claims 1-6, characterized in that, The method further includes: Monitor the statistical distribution changes of the aforementioned photoelectric performance parameters; If the statistical drift exceeds the preset drift threshold, a calibration experiment or recalibration is triggered to obtain the correction parameters. The correction parameters are fed back to the preset data-driven model for model correction.

10. A high-entropy optoelectronic nanomaterial synthesis system, characterized in that, It includes a connected precursor library and formulation execution module, a reaction control module, a post-processing module, a rapid characterization module, a process spectrum data management module, and a closed-loop optimization decision module, wherein the closed-loop optimization decision module is used to execute the steps of the method according to any one of claims 1-9.

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