ASE threshold and wavelength prediction model training method, prediction method and device

Abstract

The invention relates to the technical field of organic laser materials, in particular to an ASE threshold and wavelength prediction model training method and an organic laser material performance prediction method, and the method comprises the steps: obtaining the molecular structures and optical physical parameters of a plurality of reference organic laser materials; identifying an amplified spontaneous emission ASE threshold and wavelength in the optical physical parameters; generating a data set according to the molecular structure of the reference organic laser material, the ASE threshold and the wavelength; and constructing an ASE threshold and wavelength prediction model based on a machine learning algorithm, training the prediction model by using the data set, and predicting the ASE threshold and wavelength of the target organic laser material based on the trained prediction model. Therefore, the problems of time consumption, high cost, difficulty in quickly and effectively screening suitable molecules and the like in the aspect of searching the organic laser molecules with the low amplified spontaneous emission threshold in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the technical field of organic laser materials, and in particular to a method for training an ASE threshold and wavelength prediction model and a method for predicting the performance of organic laser materials. Background Art

[0002] Organic electrically pumped lasers have become a research hotspot in the field of optoelectronics in recent years due to their excellent energy conversion efficiency, flexible manufacturing process and wide application potential. However, the existing technology has made limited progress in finding organic laser molecules with low amplified spontaneous emission threshold, resulting in the reduction of the amplified spontaneous emission threshold still being a difficult problem to be solved. Traditional methods are usually time-consuming and costly, and it is difficult to quickly and effectively screen suitable molecules. Summary of the invention

[0003] The present application provides a method for training an ASE threshold and wavelength prediction model and a method for predicting the performance of an organic laser material, in order to solve the problems that the related technology is usually time-consuming and costly in finding low amplified spontaneous emission threshold organic laser molecules, and it is difficult to quickly and effectively screen out suitable molecules.

[0004] The first aspect of the present application provides a method for training an ASE threshold and wavelength prediction model, comprising the following steps: obtaining molecular structures and photophysical parameters of multiple reference organic laser materials; identifying the amplified spontaneous emission ASE threshold and wavelength in the photophysical parameters; generating a data set based on the molecular structure and ASE threshold and wavelength of the reference organic laser material; constructing an ASE threshold and wavelength prediction model based on a machine learning algorithm, training the prediction model using the data set, and predicting the ASE threshold and wavelength of the target organic laser material based on the trained prediction model.

[0005] Optionally, generating a data set according to the molecular structure, ASE threshold and wavelength of the reference organic laser material includes: generating corresponding samples according to the molecular structure, ASE threshold and wavelength of each reference organic laser material; generating a data set according to the corresponding samples of each reference organic laser material.

[0006] Optionally, training a prediction model using a data set includes: dividing the data set into a training set and a test set; training the prediction model using the training set, testing the prediction performance of the prediction model using the test set, and iteratively updating model parameters of the prediction model during the training process; and stopping the training of the prediction model if a preset stop condition is met.

[0007] Optionally, the preset stop condition includes: the number of iterative training reaches a target number, or the prediction performance of the prediction model reaches a target performance.

[0008] Optionally, the machine learning algorithm includes at least one of the eXtreme Gradient Boosting model and the Gradient Boosting model.

[0009] An embodiment of the second aspect of the present application provides a method for predicting the performance of an organic laser material, including: obtaining the molecular structure of a target organic laser material; inputting the molecular structure into a prediction model of the ASE threshold and wavelength, and the prediction model outputs the ASE threshold and wavelength of the target organic laser material, where the prediction model is trained based on the ASE threshold and wavelength prediction model training method of the first aspect; predicting the optical performance of the target organic laser material according to the ASE threshold and wavelength.

[0010] Optionally, the target organic laser material is a material for an electrically pumped organic laser.

[0011] An embodiment of the third aspect of the present application provides an ASE threshold and wavelength prediction model training device, including: a first acquisition module, configured to acquire the molecular structures and photophysical parameters of a plurality of reference organic laser materials; an identification module, configured to identify the amplified spontaneous emission ASE threshold and wavelength in the photophysical parameters; a generation module, configured to generate a data set according to the molecular structures of the reference organic laser materials and the ASE threshold and wavelength; a first prediction module, configured to construct a prediction model of the ASE threshold and wavelength based on a machine learning algorithm, train the prediction model using the data set, and predict the ASE threshold and wavelength of the target organic laser material based on the trained prediction model.

[0012] An embodiment of the fourth aspect of the present application provides an organic laser material performance prediction device, including: a second acquisition module, configured to acquire the molecular structure of a target organic laser material; an input module, configured to input the molecular structure into a prediction model of the ASE threshold and wavelength, and the prediction model outputs the ASE threshold and wavelength of the target organic laser material, where the prediction model is trained based on the ASE threshold and wavelength prediction model training method of the first aspect; a second prediction module, configured to predict the optical performance of the target organic laser material according to the ASE threshold and wavelength.

[0013] An embodiment of the fifth aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the ASE threshold and wavelength prediction model training method of the first aspect and the organic laser material performance prediction method of the second aspect.

[0014] An embodiment of the sixth aspect of the present application provides a computer-readable storage medium, on which a computer program or instruction is stored, and when the computer program or instruction is executed, it implements the ASE threshold and wavelength prediction model training method of the first aspect and the organic laser material performance prediction method of the second aspect.

[0015] Accordingly, the present application has the following beneficial effects:

[0016] In the embodiments of the present application, the molecular structures and photophysical parameters of multiple reference organic laser materials can be obtained, the amplified spontaneous emission (ASE) thresholds and wavelengths among them can be identified, a data set is generated based on the molecular structures, ASE thresholds and wavelengths of the reference organic laser materials, a prediction model for the ASE thresholds and wavelengths constructed based on a machine learning algorithm is trained using the data set, and the ASE thresholds and wavelengths of a target organic laser material are predicted using the trained prediction model, so as to rapidly screen candidate molecules by predicting the amplified spontaneous emission thresholds and wavelengths of organic molecules. Accordingly, the problems in the related art that it is usually time-consuming and costly to find organic laser molecules with low amplified spontaneous emission thresholds and it is difficult to rapidly and effectively screen out suitable molecules are solved.

[0017] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, wherein:

[0019] Figure 1 is a flowchart of a method for training an ASE threshold and wavelength prediction model according to an embodiment of the present application;

[0020] Figure 2 is a flowchart of a method for predicting the performance of an organic laser material according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of model construction provided by an embodiment of the present application;

[0022] Figure 4 is an example diagram of an apparatus for training an ASE threshold and wavelength prediction model according to an embodiment of the present application;

[0023] Figure 5 is an example diagram of an apparatus for predicting the performance of an organic laser material according to an embodiment of the present application;

[0024] Figure 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where 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 intended to explain the present application, and should not be construed as limiting the present application.

[0026] The training method for the ASE threshold and wavelength prediction model and the performance prediction method for organic laser materials according to the embodiments of the present application will be described below with reference to the accompanying drawings. In view of the problems in the related art mentioned in the above background art that it is usually time-consuming and costly to find organic laser molecules with a low amplified spontaneous emission threshold, and it is difficult to quickly and effectively screen out suitable molecules, etc., the present application provides a training method for the ASE threshold and wavelength prediction model. In this method, the molecular structures and photophysical parameters of multiple reference organic laser materials can be obtained, the amplified spontaneous emission ASE threshold and wavelength among them can be identified, a data set can be generated based on the molecular structures and the ASE threshold and wavelength of the reference organic laser materials, and a prediction model for the ASE threshold and wavelength constructed based on a machine learning algorithm can be trained using the data set, and the ASE threshold and wavelength of the target organic laser materials can be predicted using the trained prediction model, so as to realize the rapid screening of candidate molecules by predicting the amplified spontaneous emission threshold and wavelength of organic molecules. Thus, the problems in the related art that it is usually time-consuming and costly to find organic laser molecules with a low amplified spontaneous emission threshold, and it is difficult to quickly and effectively screen out suitable molecules, etc., are solved.

[0027] Specifically, Figure 1 FIG. is a schematic flow chart of a training method for an ASE threshold and wavelength prediction model provided by an embodiment of the present application.

[0028] As Figure 1 shown, the training method for the ASE threshold and wavelength prediction model includes the following steps:

[0029] In step S101, the molecular structures and photophysical parameters of multiple reference organic laser materials are obtained.

[0030] Among them, the molecular structures and photophysical parameters of the reference organic laser materials are collected from existing literature, and the molecular structures of the reference organic laser materials are described using electrotopological state fingerprints and chemoinformatics descriptors. Among them, the electrotopological state fingerprint is a descriptor used to quantify the electronic environment of each atom in a molecule, and the chemoinformatics descriptor is a series of numerical representation methods used to quantify the molecular structure characteristics and is used to describe various properties of the molecule, including size, shape, charge distribution, hydrophobicity, etc.

[0031] It can be understood that in the embodiments of the present application, a large number of molecular structures and photophysical parameters of reference organic laser materials are obtained by consulting existing literature, and the molecular structures of the reference organic laser materials are described using electrotopological state fingerprints and chemoinformatics descriptors.

[0032] In step S102, the amplified spontaneous emission (ASE) threshold and wavelength in the photophysical parameters are identified.

[0033] Among them, the ASE threshold refers to the minimum pump power or excitation density required to observe a significant ASE signal.

[0034] It can be understood that in the embodiments of the present application, after obtaining the photophysical parameters of the molecules of a large number of reference organic laser materials by consulting existing literature, the amplified spontaneous emission (ASE) threshold and wavelength are identified from the photophysical parameters.

[0035] In step S103, a data set is generated according to the molecular structure of the reference organic laser material and the ASE threshold and wavelength.

[0036] It can be understood that in the embodiments of the present application, the molecular structure of the reference organic laser material described using electrotopological state fingerprints and chemoinformatics descriptors, the ASE threshold, and the wavelength are integrated to generate a data set for training a prediction model.

[0037] In the embodiments of the present application, generating a data set according to the molecular structure of the reference organic laser material and the ASE threshold and wavelength includes: generating corresponding samples according to the molecular structure of each reference organic laser material and the ASE threshold and wavelength; generating a data set according to the corresponding samples of each reference organic laser material.

[0038] Among them, the corresponding sample is generated by integrating the molecular structure of the reference organic laser material described using electrotopological state fingerprints and chemoinformatics descriptors and the amplified spontaneous emission (ASE) threshold and wavelength.

[0039] It can be understood that in the embodiments of the present application, corresponding samples are generated by integrating the molecular structure of the reference organic laser material and the ASE threshold and wavelength, and then the corresponding samples of each reference organic laser material are integrated to generate a data set.

[0040] In step S104, a prediction model for the ASE threshold and wavelength is constructed based on a machine learning algorithm, the prediction model is trained using the data set, and the ASE threshold and wavelength of the target organic laser material are predicted based on the trained prediction model.

[0041] Among them, machine learning algorithms include many models, such as Linear Regression, RandomForest Regression, Xgboost (eXtreme Gradient Boosting), GBoost (Gradient Boosting Trees), etc. In this application, the Xgboost model and the GBoost model are selected to achieve prediction, which will be described in detail below and will not be elaborated here.

[0042] It can be understood that in the embodiments of this application, based on machine learning algorithms, the Xgboost model and the GBoost model are selected to construct a prediction model for the ASE threshold and wavelength. The above-generated data set is used to train this model, and the trained prediction model of the ASE threshold and wavelength is used to achieve the prediction of the ASE threshold and wavelength of the target organic laser material.

[0043] In the embodiments of this application, using the data set to train the prediction model includes: dividing the data set into a training set and a test set; using the training set to train the prediction model, using the test set to test the prediction performance of the prediction model, and iteratively updating the model parameters of the prediction model during the training process; stopping the training of the prediction model if the preset stop condition is satisfied.

[0044] It can be understood that the embodiments of this application divide the data set into two parts, a training set and a test set. For example, 70% is divided into the training set and 30% is divided into the test set, etc. The specific division situation can be set specifically and is not specifically limited. The training set is used to train the prediction model of the ASE threshold and wavelength, and the test set is used to test the prediction performance of the prediction model. During the training of the prediction model of the ASE threshold and wavelength, the model parameters of the prediction model are continuously iteratively updated until the model parameters meet the preset stop condition and the training of the prediction model stops.

[0045] In the embodiments of this application, the preset stop condition includes: the number of iterative training reaches the target number, or the prediction performance of the prediction model reaches the target performance.

[0046] Among them, the target number is the maximum number of training times set according to actual needs. After reaching it, the training of the prediction model stops, which is not specifically limited here; the target performance is the performance of the prediction model required to meet the preset requirements, which is set according to actual needs and is not specifically limited here.

[0047] It can be understood that the preset stop conditions in the embodiments of the present application may include that the number of iterative trainings reaches the target number and the prediction performance of the prediction model reaches the target performance. As long as any one of the number of iterative trainings reaching the target number or the prediction performance of the prediction model reaching the target performance is satisfied during the training of the prediction model for the ASE threshold and wavelength, the training of the prediction model can be stopped.

[0048] In the embodiments of the present application, the machine learning algorithm includes at least one of the eXtreme Gradient Boosting model and the Gradient Boosting model.

[0049] Among them, the Xgboost model is used to implement the prediction of the ASE threshold, and the GBoost model is used to implement the prediction of the ASE wavelength.

[0050] It can be understood that the machine learning algorithms selected in the embodiments of the present application include the Xgboost model and the GBoost model. The Xgboost model is used to implement the prediction of the ASE threshold, and the GBoost model is used to implement the prediction of the ASE wavelength.

[0051] According to the method for training the ASE threshold and wavelength prediction model proposed in the embodiments of the present application, the molecular structures and photophysical parameters of multiple reference organic laser materials can be obtained, the amplified spontaneous emission ASE threshold and wavelength among them can be identified, a data set can be generated based on the molecular structures of the reference organic laser materials and the ASE threshold and wavelength, the prediction model of the ASE threshold and wavelength constructed based on the machine learning algorithm can be trained using the data set, and the ASE threshold and wavelength of the target organic laser material can be predicted using the trained prediction model, so as to realize the rapid screening of candidate molecules by predicting the amplified spontaneous emission threshold and wavelength of organic molecules.

[0052] Next, a method for predicting the performance of an organic laser material proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0053] Figure 2 It is a schematic flowchart of the method for predicting the performance of an organic laser material according to an embodiment of the present application.

[0054] As Figure 2 shown, the method for predicting the performance of the organic laser material includes the following steps:

[0055] In step S201, the molecular structure of the target organic laser material is obtained.

[0056] Among them, the molecular structure of the target organic laser material is searched from untested molecular structures.

[0057] It can be understood that in the embodiments of the present application, the molecular structure of the target organic laser material is obtained from an untested database.

[0058] In the embodiments of the present application, the target organic laser material is the material of an electrically pumped organic laser.

[0059] It can be understood that in the embodiments of the present application, a material suitable as the material of an electrically pumped organic laser is obtained from an untested database as the target organic laser material.

[0060] In step S202, the molecular structure is input into the prediction model of the ASE threshold and wavelength, and the prediction model outputs the ASE threshold and wavelength of the target organic laser material, where the prediction model is trained based on the above-mentioned training method of the ASE threshold and wavelength prediction model.

[0061] It can be understood that after the molecular structure of the target organic laser material is obtained in the embodiments of the present application, it is input into the prediction model of the ASE threshold and wavelength trained based on the above-mentioned training method of the ASE threshold and wavelength prediction model, and the ASE threshold and wavelength of the target organic laser material are predicted and output by the prediction model of the ASE threshold and wavelength.

[0062] In step S203, the optical properties of the target organic laser material are predicted according to the ASE threshold and wavelength.

[0063] It can be understood that in the embodiments of the present application, by inputting the molecular structure of the target organic laser material into the prediction model of the ASE threshold and wavelength, the ASE threshold and wavelength of the target organic laser material are predicted and output by the prediction model of the ASE threshold and wavelength, and by analyzing the ASE threshold and wavelength of the target organic laser material, it is determined whether the material is an organic laser molecule suitable for an electrically pumped laser, so as to screen out organic laser molecules suitable for an electrically pumped laser.

[0064] According to the method for predicting the performance of an organic laser material proposed in the embodiments of the present application, the molecular structure of the target organic laser material can be obtained, and the molecular structure is input into the prediction model of the ASE threshold and wavelength. The prediction model outputs the ASE threshold and wavelength of the target organic laser material. Finally, the optical properties of the target organic laser material are predicted according to the ASE threshold and wavelength, and potential low-threshold organic laser molecules can be effectively identified.

[0065] The training method of the ASE threshold and wavelength prediction model and the method for predicting the performance of an organic laser material are further described below through a specific embodiment, as Figure 3 shown:

[0066] In the embodiments of the present application, a large amount of data on organic laser molecules and their corresponding amplified spontaneous emission (ASE) thresholds and wavelengths is collected from existing literature. The molecular structures are described using electrotopological state fingerprints and cheminformatics descriptors. Then, the data is divided into a training set (70%) and a test set (30%) and input into an Xgboost model to predict the ASE threshold, and a GBoost model is used to predict the ASE wavelength.

[0067] After training is completed, the embodiments of the present application further apply the model to an untested database to predict the ASE threshold and wavelength of organic molecules, and screen out organic laser molecules suitable for electrically pumped lasers. Theoretically, six novel organic laser molecules are screened out through this model. These molecules have good performance in photophysical properties, showing potential for future applications in lasers.

[0068] Figure 4 It is a block diagram of an apparatus for training an ASE threshold and wavelength prediction model according to the embodiments of the present application.

[0069] As Figure 4 shown, the apparatus 10 for training an ASE threshold and wavelength prediction model includes: a first acquisition module 301, an identification module 302, a generation module 303, and a first prediction module 304.

[0070] Among them, the first acquisition module 301 is configured to acquire the molecular structures and photophysical parameters of a plurality of reference organic laser materials; the identification module 302 is configured to identify the amplified spontaneous emission (ASE) threshold and wavelength in the photophysical parameters; the generation module 303 is configured to generate a data set according to the molecular structures of the reference organic laser materials and the ASE threshold and wavelength; the first prediction module 304 is configured to construct a prediction model for the ASE threshold and wavelength based on a machine learning algorithm, train the prediction model using the data set, and predict the ASE threshold and wavelength of a target organic laser material based on the trained prediction model.

[0071] In the embodiments of the present application, the generation module 303 is further configured to: generate corresponding samples according to the molecular structures and the ASE threshold and wavelength of each reference organic laser material; generate a data set according to the corresponding samples of each reference organic laser material.

[0072] In the embodiments of the present application, the first prediction module 304 is further configured to: divide the data set into a training set and a test set; train the prediction model using the training set, test the prediction performance of the prediction model using the test set, and iteratively update the model parameters of the prediction model during the training process; stop training the prediction model if a preset stop condition is satisfied.

[0073] In the embodiments of the present application, the preset stop condition includes: the number of iterative training reaches a target number, or the prediction performance of the prediction model reaches a target performance.

[0074] In an embodiment of the present application, the machine learning algorithm includes at least one of an eXtreme Gradient Boosting model and a Gradient Boosting model.

[0075] According to the ASE threshold and wavelength prediction model training device proposed in the embodiment of the present application, through the coordinated action of the first acquisition module, the recognition module, the generation module, and the first prediction module, the molecular structures and photophysical parameters of multiple reference organic laser materials can be obtained, the amplified spontaneous emission (ASE) threshold and wavelength therein can be recognized, a data set can be generated based on the molecular structures of the reference organic laser materials and the ASE threshold and wavelength, a prediction model of the ASE threshold and wavelength constructed based on a machine learning algorithm can be trained using the data set, and the ASE threshold and wavelength of a target organic laser material can be predicted using the trained prediction model, so as to realize the rapid screening of candidate molecules by predicting the amplified spontaneous emission threshold and wavelength of organic molecules.

[0076] Figure 5 It is a block diagram of a performance prediction device for an organic laser material according to an embodiment of the present application.

[0077] As Figure 5 shown, the performance prediction device 20 for the organic laser material includes: a second acquisition module 401, an input module 402, and a second prediction module 403.

[0078] Among them, the second acquisition module 401 is used to acquire the molecular structure of the target organic laser material; the input module 402 is used to input the molecular structure into the prediction model of the ASE threshold and wavelength, and the prediction model outputs the ASE threshold and wavelength of the target organic laser material, where the prediction model is trained according to the above-mentioned ASE threshold and wavelength prediction model training method; the second prediction module 403 is used to predict the optical performance of the target organic laser material according to the ASE threshold and wavelength.

[0079] In an embodiment of the present application, the target organic laser material is a material for an electrically pumped organic laser.

[0080] According to the performance prediction device for an organic laser material proposed in the embodiment of the present application, through the coordinated action of the second acquisition module, the input module, and the second prediction module, the molecular structure of the target organic laser material can be obtained, the molecular structure can be input into the prediction model of the ASE threshold and wavelength, the prediction model outputs the ASE threshold and wavelength of the target organic laser material, and finally the optical performance of the target organic laser material can be predicted according to the ASE threshold and wavelength, which can effectively identify potential low-threshold organic laser molecules.

[0081] Figure 6Schematic diagram of the structure of the electronic device provided by the embodiment of the present application. The electronic device may include:

[0082] A memory 501, a processor 502, and a computer program stored on the memory 501 and executable on the processor 502.

[0083] When the processor 502 executes the program, it implements the ASE threshold and wavelength prediction model training method and the organic laser material performance prediction method provided in the above embodiments.

[0084] Furthermore, the electronic device further includes:

[0085] A communication interface 503 for communication between the memory 501 and the processor 502.

[0086] The memory 501 is used to store a computer program executable on the processor 502.

[0087] The memory 501 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0088] If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 can be interconnected through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 6 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0089] Optionally, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a chip, the memory 501, the processor 502, and the communication interface 503 can communicate with each other through an internal interface.

[0090] The processor 502 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0091] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program or instruction is stored. When the computer program or instruction is executed, the above ASE threshold and wavelength prediction model training method and the above performance prediction method of the organic laser material are implemented.

[0092] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0093] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0094] Any process or method description in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or N executable instructions for implementing a customized logic function or process. The scope of the preferred embodiments of the present application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0095] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques well known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays, field programmable gate arrays, and the like.

[0096] Those of ordinary skill in the art can understand that all or part of the steps carried by the method for implementing the above embodiments can be completed by instructing relevant hardware through a program. The above program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0097] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for training an ASE threshold and wavelength prediction model, characterized in that: The following steps are involved: Obtain the molecular structures and photophysical parameters of multiple reference organic laser materials; Identifying the amplified spontaneous emission (ASE) threshold and wavelength among the photophysical parameters; generating a data set based on the molecular structure and ASE threshold and wavelength of the reference organic laser material; A prediction model for ASE threshold and wavelength is constructed based on a machine learning algorithm, the prediction model is trained using the data set, and the ASE threshold and wavelength of the target organic laser material are predicted based on the trained prediction model.

2. The ASE threshold and wavelength prediction model training method according to claim 1, characterized in that: The step of generating a data set according to the molecular structure, ASE threshold and wavelength of the reference organic laser material comprises: Generate corresponding samples according to the molecular structure, ASE threshold and wavelength of each reference organic laser material; The data set is generated based on a corresponding sample of each reference organic laser material.

3. The ASE threshold and wavelength prediction model training method according to claim 1, characterized in that: The using the data set to train the prediction model comprises: Dividing the data set into a training set and a test set; The prediction model is trained using the training set, the prediction performance of the prediction model is tested using the test set, and the model parameters of the prediction model are iteratively updated during the training process; If the preset stopping condition is met, the training of the prediction model is stopped.

4. The ASE threshold and wavelength prediction model training method according to claim 3, characterized in that: The preset stop condition includes: the number of iterative training reaches the target number, or the prediction performance of the prediction model reaches the target performance.

5. The ASE threshold and wavelength prediction model training method according to claim 1, characterized in that: The machine learning algorithm includes at least one of an eXtreme Gradient Boosting model and a Gradient Boosting model.

6. A method for predicting the performance of an organic laser material, characterized in that: The following steps are involved: Obtain the molecular structure of the target organic laser material; Inputting the molecular structure into a prediction model of ASE threshold and wavelength, the prediction model outputting the ASE threshold and wavelength of the target organic laser material, wherein the prediction model is trained based on the ASE threshold and wavelength prediction model training method according to any one of claims 1 to 5; The optical performance of the target organic laser material is predicted according to the ASE threshold and the wavelength.

7. The method for predicting the performance of an organic laser material according to claim 6, characterized in that: The target organic laser material is a material for an electrically pumped organic laser.

8. An ASE threshold and wavelength prediction model training device, characterized in that: include: A first acquisition module, used to acquire molecular structures and photophysical parameters of a plurality of reference organic laser materials; An identification module, used to identify the amplified spontaneous emission (ASE) threshold and wavelength in the photophysical parameters; A generating module, used for generating a data set according to the molecular structure, ASE threshold and wavelength of the reference organic laser material; The first prediction module is used to build a prediction model of ASE threshold and wavelength based on a machine learning algorithm, train the prediction model using the data set, and predict the ASE threshold and wavelength of the target organic laser material based on the trained prediction model.

9. A performance prediction device for an organic laser material, characterized in that: include: A second acquisition module is used to acquire the molecular structure of the target organic laser material; An input module, used to input the molecular structure into a prediction model of ASE threshold and wavelength, wherein the prediction model outputs the ASE threshold and wavelength of the target organic laser material, wherein the prediction model is trained based on the ASE threshold and wavelength prediction model training method according to any one of claims 1 to 5; The second prediction module is used to predict the optical performance of the target organic laser material according to the ASE threshold and the wavelength.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the ASE threshold and wavelength prediction model training method described in any one of claims 1 to 5, or the performance prediction method of the organic laser material described in any one of claims 6 to 7.

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