A method and system for predicting the two-photon absorption cross section of push-pull organic molecules

Abstract

This invention proposes a method and system for predicting the two-photon absorption cross section of push-pull organic molecules, relating to the field of optical detection technology. The method includes: constructing and optimizing a push-pull organic molecule model with an electron donor-π center-electron acceptor structure; simulating the molecule's dynamics in solution and sampling molecular configurations based on the simulation results; constructing a dataset based on the sampled molecular configurations and training a neural network model; predicting optical absorption parameters using the trained neural network model and calculating the two-photon absorption cross section of the push-pull molecule. This invention, based on neural networks and quantum chemical calculations, predicts the optical absorption parameters of push-pull organic molecules and calculates the two-photon absorption cross section based on these parameters, significantly improving the calculation speed of the two-photon absorption cross section of push-pull organic molecules and achieving rapid and effective prediction of the dynamic two-photon absorption cross section of push-pull organic molecules.

Description

Technical Field

[0001] This invention belongs to the field of optical detection technology, and in particular relates to a method and system for predicting the two-photon absorption cross section of push-pull organic molecules. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Two-photon absorption, with its characteristics of long-wavelength absorption, short-wavelength emission, and absorption intensity proportional to the square of the incident light intensity, has shown promising application prospects in two-photon fluorescence microscopy, two-photon upconversion lasing, three-dimensional high-density information storage, and photodynamic cancer treatment, and has become a hot topic in materials engineering, chemical synthesis, and life sciences. In recent years, with the continuous development of experimental techniques, a large number of two-photon absorbing organic functional materials have been synthesized. Among them, organic molecules formed by electron donor groups and electron acceptor groups connected by π-central conjugated groups have advantages such as simple structure, flexible tunability, and easy tailoring and modification, and have attracted great attention.

[0004] A crucial indicator for measuring two-photon absorption capacity is the two-photon absorption cross section. Currently, the two-photon absorption cross section of organic molecules can be obtained experimentally using methods including nonlinear transmittance methods, Z-scan techniques, two-photon transient absorption spectroscopy, and theoretical methods based on first-principles calculations. However, experimental measurements and theoretical calculations are time-consuming, complex, and demanding on instruments and skilled personnel. Furthermore, the two-photon absorption cross section of molecules is measured in solvents, and the molecular configuration is influenced by the interactions between solvent molecules and evolves over time, limiting the effective characterization of the two-photon absorption cross section of push-pull organic molecules. If a universal neural network could be used to predict the two-photon absorption cross section of push-pull organic molecules, it would not only significantly reduce experimental, labor, and time costs but also help researchers understand the intrinsic nature of the relationship between structure and properties, study the dynamics of its changes, and provide effective molecular design basis for practical applications, thereby guiding and accelerating the discovery of suitable materials. Therefore, there is an urgent need to develop a method for predicting the two-photon absorption cross section of push-pull organic molecules based on neural networks. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, this invention provides a method and system for predicting the two-photon absorption cross section of push-pull organic molecules. Based on neural networks and quantum chemical calculations, the method predicts the light absorption property parameters of push-pull organic molecules and calculates the two-photon absorption cross section based on the parameters. This greatly improves the calculation speed of the two-photon absorption cross section of push-pull organic molecules and realizes the rapid and effective prediction of the dynamic two-photon absorption cross section of push-pull organic molecules.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0007] The first aspect of this invention provides a method for predicting the two-photon absorption cross section of push-pull organic molecules;

[0008] A method for predicting the two-photon absorption cross section of push-pull organic molecules, comprising:

[0009] A push-pull model of an organic molecule with an electron donor-π center-electron acceptor was constructed and optimized.

[0010] The dynamics of molecules in solution are simulated, and molecular configurations are sampled based on the simulation results;

[0011] Based on the molecular configurations obtained from sampling, a dataset is constructed to train the neural network model;

[0012] By using a trained neural network model, the optical absorption property parameters are predicted, and the two-photon absorption cross section of push-pull molecules is calculated.

[0013] Furthermore, a push-pull molecular framework of "donor-π center-acceptor" was adopted, and a push-pull organic molecular model was constructed using the molecular modeling software GaussView 6. The configuration of the constructed molecular system was then optimized using the Gaussian 16 software package.

[0014] Furthermore, the dynamic evolution of push-pull molecules in solution was simulated using the GROMACS software package, and the molecular configuration was sampled at equal time intervals based on the simulation results.

[0015] Furthermore, the dataset includes input parameters and output parameters;

[0016] The input parameters include the bond length, bond angle, dihedral angle, and Coulomb matrix of the molecule;

[0017] The output parameters are optical absorption property parameters, including the molecular excitation energy, transition dipole moment, and intrinsic dipole moment.

[0018] Furthermore, based on the structure of the push-pull organic molecules obtained from the sampling, the bond lengths, bond angles, and dihedral angles of the molecules after removing hydrogen atoms are statistically analyzed, and the Coulomb matrix of the molecules is calculated according to the following formula:

[0019]

[0020] Where i and j represent atomic labels, Z i and R i Let represent the nuclear charge number and coordinate of the i-th atom, respectively.

[0021] Furthermore, based on the structure of the sampled push-pull organic molecules, the excitation energy E and transition dipole moment μ of the push-pull organic molecules were calculated using the density functional method. 0n Inherent dipole moment μ 00 and μ nn .

[0022] Furthermore, the method for calculating the two-photon absorption cross section of push-pull molecules is as follows:

[0023]

[0024] Among them, E, μ 0n μ 00 and μ nn These are the excitation energy, transition dipole moment, and intrinsic dipole moment of the ground state and excited state of push-pull organic molecules, respectively.

[0025] A second aspect of the present invention provides a two-photon absorption cross section prediction system for push-pull organic molecules.

[0026] A two-photon absorption cross-section prediction system for push-pull organic molecules includes a model building module, a molecular simulation module, a model training module, and a prediction calculation module.

[0027] The model building module is configured to: construct push-pull organic molecule models with electron donor-π center-electron acceptor, and optimize the models;

[0028] The molecular simulation module is configured to: simulate the dynamics of molecules in solution and, based on the simulation results, sample molecular configurations.

[0029] The model training module is configured to: construct a dataset based on the sampled molecular configurations and train the neural network model;

[0030] The prediction and calculation module is configured to predict light absorption property parameters and calculate the two-photon absorption cross section of push-pull molecules using a trained neural network model.

[0031] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a method for predicting the two-photon absorption cross section of a push-pull organic molecule as described in the first aspect of the present invention.

[0032] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the method for predicting the two-photon absorption cross section of a push-pull organic molecule as described in the first aspect of the present invention.

[0033] The above one or more technical solutions have the following beneficial effects:

[0034] This invention provides a method for predicting the two-photon absorption cross section of push-pull organic molecules based on neural networks and quantum chemical calculations. This method significantly improves the calculation speed of the two-photon absorption cross section of push-pull organic molecules and enables rapid and effective prediction of the dynamic two-photon absorption cross section of push-pull organic molecules.

[0035] While saving time and manpower costs, the system accurately obtains real-time dynamic results of the two-photon absorption cross section of push-pull organic molecules, providing a theoretical reference for the experimental design and synthesis of materials with strong two-photon absorption capabilities.

[0036] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0037] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0038] Figure 1 This is a flowchart of the method in the first embodiment.

[0039] Figure 2 This is a schematic diagram of the DNAS molecular model in the first embodiment.

[0040] Figure 3 This is a diagram of the neural network structure constructed in the first embodiment.

[0041] Figure 4 The excitation energy E(A) and transition dipole moment μ of the DANS molecule calculated in the first embodiment are... 0n (B) and the inherent dipole moment μ 00 (C), μ 11 (D)

[0042] Figure 5 This is a system structure diagram of the second embodiment. Detailed Implementation

[0043] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0044] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the invention; unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0045] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention; as used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise; furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0046] Definitions:

[0047] Push-pull organic molecules: These are organic molecules in which electron-donating and electron-accepting groups are connected through a π-center conjugated group.

[0048] Two-photon absorption cross section: This is an important indicator for measuring the two-photon absorption capacity of a system.

[0049] Example 1

[0050] This embodiment uses 4-dimethylamino-4'-nitrostilbene (DANS) molecule as the research object and discloses a method for predicting the two-photon absorption cross section of push-pull organic molecules;

[0051] like Figure 1 As shown, a method for predicting the two-photon absorption cross section of push-pull organic molecules includes:

[0052] Step S1: Construct a push-pull organic molecule model with electron donor-π center-electron acceptor, and optimize the model;

[0053] Using dimethylamino as the electron donor, stilbene as the π-center, and nitro as the electron acceptor, a push-pull molecular framework of "donor-π-center-acceptor" was adopted. A push-pull organic molecular model was constructed using the molecular modeling software GaussView6. The constructed molecular model is shown below. Figure 2 As shown, N is a nitrogen atom, O is an oxygen atom, and the numbers are atom labels.

[0054] The configuration of the constructed molecular system was optimized using the Gaussian 16 software package: the constructed push-pull organic molecular model was used as input, and the Gaussian 16 software package was used to optimize the molecular system in the gas phase with B3LYP functional and 6-31+g(d,p) basis set, and the molecular vibrational frequencies were calculated to ensure that there were no imaginary frequencies to verify the stability of the molecular configuration.

[0055] Step S2: Simulate the dynamics of molecules in solution, and sample molecular configurations based on the simulation results;

[0056] The dynamic evolution of push-pull molecules in solution was simulated using the GROMACS software package, and the molecular configuration was sampled at equal time intervals based on the simulation results.

[0057] Based on the optimized molecular structure, the dynamic evolution of the molecule in aqueous solution was simulated using the GROMACS software package. The specific details are as follows:

[0058] (1) Generate a topology file for push-pull organic molecules using the acpype command;

[0059] (2) Use the `genrestr` command to generate a confinement potential file for push-pull organic molecules;

[0060] (3) Use the editconf command to set a cube with a side length of 0.8nm and place the push-pull organic molecule in the center of the box;

[0061] (4) Add water solvent to the box using the solvate command;

[0062] (5) Set the number of push-pull organic molecules and water molecules and the confinement potential in the topology file;

[0063] (6) Use the genion command to add ions to the solution to neutralize it;

[0064] (7) After performing energy minimization and 100ps-limited kinetic simulations on the solution system, a conventional kinetic simulation was performed for 50ns.

[0065] After the simulation is completed, the simulation results are recorded, and a set of molecular configurations is taken from the simulation results every 2 ps, resulting in a total of 25,000 sets of molecular configurations.

[0066] Step S3: Based on the molecular configurations obtained from the sampling, construct a dataset and train the neural network model;

[0067] The dataset includes input parameters and output parameters;

[0068] The input parameters include the bond length, bond angle, dihedral angle, and Coulomb matrix of the molecule;

[0069] The output parameters are optical absorption property parameters, including the molecular excitation energy, transition dipole moment, and intrinsic dipole moment.

[0070] Based on the structure of the push-pull organic molecules obtained from sampling, the bond lengths, bond angles, and dihedral angles of the molecules after removing hydrogen atoms were statistically analyzed, and the Coulomb matrix of the molecules was calculated according to the following formula:

[0071]

[0072] Where i and j represent atomic labels, Z i and R i Let represent the nuclear charge number and coordinate of the i-th atom, respectively.

[0073] Based on the structure of the sampled push-pull organic molecules, the excitation energy E of the push-pull organic molecules was calculated using the Gaussian 16 software package with the B3LYP functional and the 6-31+g(d,p) basis set. Furthermore, the transition dipole moment μ of the molecules was obtained using the Multiwfn software. 0n Inherent dipole moment μ 00 and μ nn .

[0074] Using 55 parameters—bond lengths, bond angles, dihedral angles, and Coulomb matrix elements—for each molecular configuration as descriptors (inputs), the excitation energy E and transition dipole moment μ of the molecule are predicted. 0n The ground state intrinsic dipole moment μ 00 and the intrinsic dipole moment μ of the excited state nn (Output), and the entire dataset is randomly divided into training and test sets in a 4:1 ratio.

[0075] In other words, based on data obtained from quantum chemical calculations, a neural network model is constructed to perform machine learning on the light absorption properties of DANS molecules.

[0076] The constructed neural network model, such as Figure 3 As shown, it includes one input layer, four hidden layers, and one output layer. Each hidden layer has 128, 64, 32, and 16 neurons respectively, and uses the Rectified Linear Unit as the activation function.

[0077] During training, L2 regularization is used to prevent overfitting, and the Adam algorithm is used to optimize the learning iteration process of the entire neural network.

[0078] The accuracy of the model was evaluated using the Pearson correlation coefficient (r) and the mean relative error (MRE), and the robustness of the model was measured using cross-validation. The prediction results are as follows: Figure 4 As shown in the figure, the horizontal axis represents the results obtained from quantum chemical calculations on the test set, and the vertical axis represents the results predicted based on neural networks; it can be seen that for the excitation energy E and the transition dipole moment μ of the molecule... 0n The ground state intrinsic dipole moment μ 00 and the intrinsic dipole moment μ of the excited state nn The prediction errors (MREs) of the neural network were 3.2%, 8.0%, 6.0%, and 1.8%, respectively. In addition, the linear fitting curves in the figure show a high correlation between the prediction results and the quantum chemical calculation results.

[0079] The construction and training of the neural network model are both implemented on the Tensorflow artificial intelligence framework platform.

[0080] Step S4: Using the trained neural network model, predict the light absorption property parameters and calculate the two-photon absorption cross section of the push-pull molecule.

[0081] The method for calculating the two-photon absorption cross section of push-pull molecules is as follows:

[0082]

[0083] Among them, E, μ 0n μ 00 and μ nn These are the excitation energy, transition dipole moment, and intrinsic dipole moment of the ground state and excited state of push-pull organic molecules, respectively.

[0084] Example 2

[0085] This embodiment discloses a two-photon absorption cross section prediction system for push-pull organic molecules;

[0086] like Figure 5 As shown, a two-photon absorption cross-section prediction system for push-pull organic molecules includes a model building module, a molecular simulation module, a model training module, and a prediction calculation module.

[0087] The model building module is configured to: construct push-pull organic molecule models with electron donor-π center-electron acceptor, and optimize the models;

[0088] The molecular simulation module is configured to: simulate the dynamics of molecules in solution and, based on the simulation results, sample molecular configurations.

[0089] The model training module is configured to: construct a dataset based on the sampled molecular configurations and train the neural network model;

[0090] The prediction and calculation module is configured to predict light absorption property parameters and calculate the two-photon absorption cross section of push-pull molecules using a trained neural network model.

[0091] Example 3

[0092] The purpose of this embodiment is to provide a computer-readable storage medium.

[0093] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in a method for predicting the two-photon absorption cross section of a push-pull organic molecule as described in Embodiment 1 of this disclosure.

[0094] Example 4

[0095] The purpose of this embodiment is to provide an electronic device.

[0096] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in a method for predicting the two-photon absorption cross section of a push-pull organic molecule as described in Embodiment 1 of this disclosure.

[0097] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the two-photon absorption cross section of push-pull organic molecules, characterized in that, include: A push-pull model of an organic molecule with an electron donor-π center-electron acceptor was constructed and optimized. The dynamics of molecules in solution are simulated, and molecular configurations are sampled based on the simulation results. Specifically, the dynamic evolution of push-pull molecules in solution is simulated using the GROMACS software package, and molecular configurations are sampled at equal time intervals based on the simulation results. Based on the molecular configurations obtained from sampling, a dataset is constructed to train the neural network model. The dataset includes input parameters and output parameters. The input parameters include the bond length, bond angle, dihedral angle, and Coulomb matrix of the molecule; The output parameters are optical absorption property parameters, including the excitation energy, transition dipole moment and intrinsic dipole moment of the molecule; By using a trained neural network model, the optical absorption property parameters are predicted, and the two-photon absorption cross section of push-pull molecules is calculated.

2. The method for predicting the two-photon absorption cross section of push-pull organic molecules as described in claim 1, characterized in that, A push-pull molecular framework of "donor-π center-acceptor" was adopted. A push-pull organic molecular model was constructed using the molecular modeling software GaussView 6, and the configuration of the constructed molecular system was optimized using the Gaussian 16 software package.

3. The method for predicting the two-photon absorption cross section of push-pull organic molecules as described in claim 1, characterized in that, Based on the structure of the push-pull organic molecules obtained from sampling, the bond lengths, bond angles, and dihedral angles of the molecules after removing hydrogen atoms were statistically analyzed, and the Coulomb matrix of the molecules was calculated according to the following formula: Where i and j represent atomic labels, Z i and R i Let represent the nuclear charge number and coordinate of the i-th atom, respectively.

4. The method for predicting the two-photon absorption cross section of push-pull organic molecules as described in claim 1, characterized in that, Based on the structure of the sampled push-pull organic molecules, the excitation energy E and transition dipole moment μ of the push-pull organic molecules were calculated using the density functional method. 0n Inherent dipole moment μ 00 and μ nn .

5. The method for predicting the two-photon absorption cross section of push-pull organic molecules as described in claim 1, characterized in that, The method for calculating the two-photon absorption cross section of push-pull molecules is as follows: Among them, E, μ 0n μ 00 and μ nn These are the excitation energy, transition dipole moment, and intrinsic dipole moment of push-pull organic molecules, respectively.

6. A two-photon absorption cross-section prediction system for push-pull organic molecules, characterized in that, It includes a model building module, a molecular simulation module, a model training module, and a prediction calculation module: The model building module is configured to: construct push-pull organic molecule models with electron donor-π center-electron acceptor, and optimize the models; The molecular simulation module is configured to: simulate the dynamics of molecules in solution, and sample molecular configurations based on the simulation results. Specifically, it uses the GROMACS software package to simulate the dynamic evolution of push-pull molecules in solution, and samples molecular configurations at equal time intervals based on the simulation results. The model training module is configured to: construct a dataset based on the sampled molecular configurations and train the neural network model, wherein the dataset includes input parameters and output parameters; The input parameters include the bond length, bond angle, dihedral angle, and Coulomb matrix of the molecule; The output parameters are optical absorption property parameters, including the excitation energy, transition dipole moment and intrinsic dipole moment of the molecule; The prediction and calculation module is configured to predict light absorption property parameters and calculate the two-photon absorption cross section of push-pull molecules using a trained neural network model.

7. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the method for predicting the two-photon absorption cross section of a push-pull organic molecule as described in any one of claims 1-5.

8. An electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the two-photon absorption cross section prediction method for push-pull organic molecules as described in any one of claims 1-5.

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