A method for intelligent design of semiconductor catalysts and optimization of reaction mechanism based on graph grammar

Abstract

The application discloses a kind of based on graph syntax's semiconductor catalyst intelligent design and reaction mechanism optimization method, with following step composition: step 1 graph syntax model generation and optimization module: the molecular structure model of semiconductor catalyst is automatically generated, and the structure combination with best catalytic performance is screened out.Step 2 reaction path automatic derivation module: simulate the interaction between molecules, generate reaction path graph;Step 3 intelligent catalyst design module: analyze the relationship between semiconductor catalyst structure and performance, dynamically adjust the molecular structure of catalyst;Step 4 reaction mechanism visualization and explanation module: provide graphical display of reaction path and structural change, support interactive analysis of multidimensional data;Step 5 real-time monitoring and feedback optimization module: real-time monitoring key parameters in the process of catalytic reaction, dynamically adjust graph syntax model and design scheme.

Description

Technical Field

[0001] The present invention relates to the field of intelligent design of semiconductor catalysts, and in particular to a method for intelligent design and reaction mechanism optimization of semiconductor catalysts based on graph grammar. Background Art

[0002] Catalysts play a crucial role in catalytic chemical reactions, with their performance directly influencing reaction rate, selectivity, and product purity. Catalysts are particularly important in modern industry, particularly in areas such as energy conversion, environmental remediation, and chemical synthesis. Semiconductor materials, due to their unique electronic structures and optical properties, have been widely used in photocatalysis and electrocatalysis. Semiconductor catalysts have shown great potential in solar energy conversion, pollutant degradation, and green chemical synthesis.

[0003] However, traditional semiconductor catalyst design methods rely primarily on laboratory trial and error and researchers' experience, which is not only time-consuming and labor-intensive, but also hinders rapid and large-scale optimization and innovation. Especially when faced with complex semiconductor material systems, traditional methods often fail to fully account for the interplay between multiple physical and chemical factors, limiting the potential for improving catalyst performance. With the advancement of artificial intelligence and machine learning technologies, catalyst design is gradually moving towards automation and intelligence.

[0004] While some computer vision and machine learning techniques have been applied to catalyst design and optimization in existing research, these approaches often focus on basic data processing and analysis, such as structural feature extraction or optimization of a single reaction variable. These methods often exhibit limitations when dealing with complex semiconductor catalyst systems, struggling to fully address the challenges of multidimensional data. Furthermore, comprehensive evaluation and dynamic optimization of catalyst performance still require extensive manual intervention, limiting their widespread adoption in practical applications. Summary of the Invention

[0005] The present invention provides a method for intelligent design and reaction mechanism optimization of semiconductor catalysts based on graph grammar, which aims to overcome the shortcomings of the existing technology and provide a more efficient, accurate and applicable optimization method for a variety of catalytic reaction conditions. By integrating graph grammar modeling, machine learning and multidimensional data fusion technology, this method can automatically generate and optimize the molecular structure of semiconductor catalysts, deeply analyze the catalytic reaction path, and adjust the design parameters in real time in a dynamically changing environment. Compared with traditional catalyst design methods, the present invention has significant advantages. Traditional methods rely on experiments and the experience of researchers, which is not only time-consuming and labor-intensive, but also easily affected by subjective judgment when dealing with complex semiconductor materials and catalytic reactions. By introducing graph grammar and machine learning algorithms, the present invention can effectively process and analyze complex semiconductor catalyst data, reduce manual intervention, and achieve quantitative performance evaluation and optimization, thereby greatly improving the efficiency and effectiveness of catalyst design and promoting the widespread promotion of semiconductor catalysts in industrial applications.

[0006] The present invention provides a method for intelligent design of semiconductor catalysts and reaction mechanism optimization based on graph grammar, which specifically includes the following steps:

[0007] Step 1: Design a graph grammar model generation and optimization module to automatically generate molecular structure models of semiconductor catalysts and screen out structural combinations with the best catalytic performance;

[0008] Step 2: Design a reaction path automatic derivation module to simulate the interaction between molecules and generate a reaction path diagram;

[0009] Step 3: Provide an intelligent catalyst design module to analyze the relationship between catalyst structure and performance and dynamically adjust the catalyst's molecular structure;

[0010] Step 4: Design a reaction mechanism visualization and interpretation module to provide a graphical display of reaction pathways and structural changes, and support interactive analysis of multidimensional data;

[0011] Step 5: Real-time monitoring and feedback optimization, through real-time monitoring of key parameters in the catalytic reaction process, dynamically adjust the graph grammar model and design scheme.

[0012] Furthermore, the graph grammar model generation and optimization module in step 1 uses a graph convolutional network (GCN) to process catalyst molecular structure data and combines a greedy algorithm to screen and optimize the generated structure; specifically, the following steps are included:

[0013] Step 11: Design a graph convolutional network (GCN) module and use it to extract high-order relationship features between nodes and atoms and edges and chemical bonds in molecular graphs, thereby more accurately capturing the complex interactions between electronic structure and chemical bonds in semiconductor materials.

[0014] wherein, is the adjacency matrix after adding the loop, is the degree matrix, is the node feature matrix of the i-th layer, is the weight matrix of the i-th layer, is the activation function;

[0015] Step 12: design a greedy algorithm module, apply the greedy algorithm in the generated catalyst structure to gradually select a local optimal solution, and finally obtain a globally optimal catalyst structure combination; the algorithm is realized by the following steps: selection strategy: select the structure change optimization process that improves the maximum performance under the current conditions; optimization process: continuously screen the structure in multiple iterations until the preset performance indicators are met.

[0016] Further, the reaction path automatic derivation module in step 2 adopts the Markov Chain Monte Carlo (MCMC) method to sample the catalytic reaction path; specifically including the following steps:

[0017] Step 21: design an MCMC sampling module, use the MCMC method to sample potential reaction paths, and use the Metropolis-Hastings algorithm to generate samples; this module can efficiently explore possible semiconductor reaction paths in complex reaction environments by calculating the following steps:

[0018] ; wherein, denotes the probability of selecting a new state from the current state ; the value of the target distribution at state ; is the target distribution; is the proposal distribution from the current state ; the conditional probability of selecting the next candidate state ; is the proposal distribution from the candidate state ;

[0019] Step 22: use the path integral method to integrate the sampled reaction paths to generate a complete reaction path graph, and use the path integral method to calculate the total free energy change.

[0020] wherein, is the free energy gradient, is the reaction path. ​

[0021] Further, the intelligent catalyst design module provided in step 3 combines transfer learning and reinforcement learning strategies; specifically including the following steps:

[0022] Step 31: Design a transfer learning module to improve the learning efficiency of the model in new tasks and reduce training time by utilizing knowledge learned in similar semiconductor catalytic tasks.

[0023] where, is the model parameter of the source task, is the data of the target task;

[0024] Step 32: Design a reinforcement learning module to optimize design parameters using the Q-learning algorithm, reward function According to the performance definition of the catalyst: help the model find the optimal solution in the design space.

[0025] where, represents the value of performing action in state ; is used to measure the expected return under a given state and action; represents the immediate reward after performing action in state ; is the discount factor, used to discount the weight of future rewards, ranging from [0, 1], the closer the value is to 1, the more important the future reward is; the closer the value is to 0, the more emphasis on immediate rewards; represents the maximum value when selecting action in the next state . It represents the maximum future return that can be obtained from the optimal policy starting from the next state.

[0026] Further, the reaction mechanism visualization and explanation module in step 4 uses ConvLSTM to process multi-dimensional time series data and visualizes its results, specifically including the following steps:

[0027] Step 41: Use the ConvLSTM processing module to capture the time-dependent changes in the reaction process by processing the time series data of semiconductor catalysts, and help identify the key steps in the semiconductor catalytic reaction path;

[0028] where, is the hidden state at the current time, is the input data representing the current time step t, is the weight matrix input to the hidden state, which is used to map the input of the current time step to the hidden state space ; is the weight matrix of the hidden state to the hidden state. It projects the hidden state of the previous time step to the current hidden state , is the bias term

[0029] Step 42: Design dynamic reaction path visualization, map time series changes into dynamic reaction path by generating heat map and path graph, help researchers track and analyze key reaction steps.

[0030] Further, the real-time monitoring and feedback optimization module in step 5 integrates Bayesian optimization and sensor data fusion technology, including the following steps:

[0031] Step 51: Design Bayesian optimization module, dynamically adjust design parameters through Bayesian optimization algorithm, ensure that the semiconductor catalyst is always in the best working state during the reaction process.

[0032] , wherein is the parameter to be optimized, is the utility function

[0033] Step 52: Design sensor data fusion module, use Kalman filtering and particle filtering to fuse the data of multiple sensors, realize real-time monitoring and feedback of semiconductor catalytic reaction process.

[0034] The update formula of Kalman filtering is: , wherein is the estimated value, is the measured value, is the Kalman gain is the observation matrix; the update formula of particle filtering is:

[0035]

[0036] , wherein is the state estimation at the moment, is the weight of the th particle at the moment, is the state of the th particle at the moment, is the number of particles.

[0037] The beneficial effects of the present application are:

[0038] 1. By integrating graph convolutional networks (GCNs) with greedy algorithms, we have achieved significant breakthroughs in processing and optimizing molecular structural models of semiconductor catalysts. Compared to traditional optimization algorithms, GCNs can capture the complex relationships between catalyst molecules and provide deeper feature representation, while greedy algorithms are more efficient when solving problems with large search spaces and complex structures, ensuring rapid convergence to local optima. Other traditional optimization methods, such as stochastic gradient descent or simulated annealing, often lack processing speed and accuracy due to the complexity of molecular structural models. Our approach strikes a balance between accuracy and efficiency, significantly improving catalyst performance and stability.

[0039] 2. Using Markov Chain Monte Carlo (MCMC) and path integral methods, we are able to effectively derive optimal pathways for semiconductor catalytic reactions. MCMC methods excel at exploring complex reaction pathways, encompassing a wide range of state spaces and avoiding local extrema. Path integral methods provide comprehensive reaction energy analysis, ensuring accurate and predictable reaction pathways. In contrast, other methods, such as simple gradient tracing algorithms, are prone to local optima and struggle to fully explore the entire reaction path space, making it difficult to ensure comprehensive pathway derivation.

[0040] 3. By combining transfer learning with reinforcement learning strategies, the intelligent catalyst design module can dynamically adjust the catalyst's molecular structure under varying reaction conditions to better adapt to new catalytic tasks. Traditional methods, such as rule-based optimization, often lack flexibility and struggle to adapt to changing environments and new task demands. Our approach uses transfer learning to transfer knowledge from existing tasks and, combined with reinforcement learning, adaptively adjusts the structure, rapidly optimizing overall performance and significantly improving the catalyst's practicality and adaptability.

[0041] 4. By using Convolutional Long Short-Term Memory (ConvLSTM) networks and dynamic reaction path visualization technology, we can track and analyze the reaction processes of semiconductor catalysts in real time. Compared to static reaction path analysis methods, ConvLSTM can combine time series information to provide more accurate dynamic process predictions, providing a deeper understanding of the catalyst reaction process and facilitating timely adjustments to reaction conditions, thereby improving reaction efficiency.

[0042] 5. By integrating Bayesian optimization and sensor data fusion technology, we have achieved real-time monitoring and feedback adjustment of semiconductor catalyst performance. Bayesian optimization has unique advantages in handling high-dimensional and nonlinear problems, efficiently finding the optimal parameter combination. Sensor data fusion ensures the accuracy and consistency of monitoring data, overcoming the potential bias and error issues of single sensor data, making the entire monitoring and adjustment process more reliable and efficient. Compared to simple fixed parameter control methods, our method can make intelligent adjustments based on real-time data, significantly improving catalyst performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flow chart of the intelligent design and reaction mechanism optimization method for semiconductor catalysts based on graph grammar;

[0044] Figure 2 Generate and optimize module function graphs for graph grammar models;

[0045] Figure 3 Automatically derive modular functional diagrams for reaction pathways;

[0046] Figure 4 Design module function diagram for intelligent catalyst;

[0047] Figure 5 Functional diagram of modules for visualization and interpretation of reaction mechanisms. DETAILED DESCRIPTION

[0048] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are intended only to illustrate the present invention and are not intended to limit the scope of the present invention. It should be noted that the terms "front," "rear," "left," "right," "up," and "down" used in the following description refer to directions in the accompanying drawings, and the terms "inward" and "outward" refer to directions toward or away from the geometric center of a particular component, respectively.

[0049] This embodiment provides a method for intelligent design of semiconductor catalysts and reaction mechanism optimization based on graph grammar, comprising the following steps:

[0050] Step 1: Design a graph grammar model generation and optimization module to automatically generate molecular structure models of semiconductor catalysts and screen out structural combinations with the best catalytic performance;

[0051] Step 2: Design a reaction path automatic derivation module to simulate the interaction between molecules and generate a reaction path diagram;

[0052] Step 3: Provide an intelligent catalyst design module to analyze the relationship between catalyst structure and performance and dynamically adjust the catalyst's molecular structure;

[0053] Step 4: Design a reaction mechanism visualization and interpretation module to provide a graphical display of reaction pathways and structural changes, and support interactive analysis of multidimensional data;

[0054] Step 5: Real-time monitoring and feedback optimization, through real-time monitoring of key parameters in the catalytic reaction process, dynamically adjust the graph grammar model and design scheme.

[0055] In a specific embodiment, the present invention applies a semiconductor catalyst intelligent design and reaction mechanism optimization method based on graph grammar, and performs comprehensive catalyst structure optimization and reaction path derivation by combining advanced technologies such as graph convolutional network GCN, MCMC sampling, reinforcement learning and ConvLSTM. Figure 1 In this example, the system specifically analyzes the molecular structural characteristics of semiconductor catalysts and their performance under different reaction conditions, such as the electronic structure of the catalyst, the reaction pathway, and the generation of intermediates, thereby providing a deep understanding and optimization of catalyst design and reaction mechanisms.

[0056] In this specific example, assume a user uploads the following dataset, including molecular structure data, reaction condition data, and performance indicator data. Molecular structure data: 1000 semiconductor catalyst molecular structures, represented in SMILES format. Reaction condition data: 1000 sets of reaction condition data, including temperature, pressure, reaction time, light intensity, etc., with each set containing 10 different conditions. Performance indicator data: 1000 catalytic reaction efficiency, selectivity, and product purity, among other indicators.

[0057] First, data preprocessing is performed to standardize the input data. Molecular structure data: The molecular structure in SMILES format is converted into a molecular fingerprint vector to extract the characteristics of chemical bonds and atomic interactions in the molecule. Reaction condition data: Data such as temperature, pressure, reaction time, and light intensity are normalized, and the data range is compressed to between 0 and 1 to ensure that the model performs unified processing and analysis between data of different dimensions. Performance indicator data: Indicators such as catalytic reaction efficiency, selectivity, and product purity are standardized so that the model can effectively compare performance under different experimental conditions. The processed data (molecular fingerprint vector, normalized reaction condition data, and performance indicator data) are merged into an overall high-dimensional data matrix. This data matrix will serve as the input for the subsequent graph convolutional network (GCN) and greedy algorithm modules.

[0058] The processed data is input into the graph grammar model generation and optimization module, such as Figure 2 The GCN module is used to extract high-order relationship features between nodes and atoms and edges and chemical bonds in molecular graphs, with a particular focus on the distribution of electronic structures and the capture of chemical bond characteristics in semiconductor catalysts.

[0059] Design a graph convolutional network (GCN) module and use it to extract high-order relationship features between nodes and atoms and edges and chemical bonds in molecular graphs. Specifically, GCN updates the node representation using the following formula:

[0060] in, is the adjacency matrix after adding self-loops, is the degree matrix, It is The node feature matrix of the layer, is the weight matrix of this layer, is the activation function;

[0061] Step 12: Design a greedy algorithm module and apply the greedy algorithm to the generated catalyst structure to gradually select the local optimal solution and finally obtain the globally optimal catalyst structure combination; the algorithm is implemented through the following steps: Selection strategy: Select the structural change that maximizes performance under the current conditions Optimization process: Continuously screen the structure in multiple iterations until the preset performance indicators are met.

[0062] Step 11: Design a graph convolutional network (GCN) module and use it to extract high-order relationship features between nodes and atoms and edges and chemical bonds in the molecular graph. Specifically, GCN updates the node representation using the following formula:

[0063] in, is the adjacency matrix after adding self-loops, is the degree matrix, It is The node feature matrix of the layer, is the weight matrix of this layer, is the activation function;

[0064] By performing convolution operations on the molecular graph, the node representations are updated layer by layer, capturing important features of the molecular structure, such as the type and strength of chemical bonds. By calculating the feature weights for each node-atom and edge-bond, the structural features that most influence catalytic performance are identified. A greedy algorithm is applied to the extracted features for structural optimization. Starting from the current optimal solution, the algorithm gradually selects local optimal solutions, ultimately finding the global optimal solution. The optimized molecular structure model is output, including key molecular structures and their features, for use in subsequent reaction pathway derivation modules.

[0065] The Markov Chain Monte Carlo (MCMC) method is used to sample the reaction paths of the molecular structure model, such as Figure 3 The Metropolis-Hastings algorithm is used to generate samples: the calculation is done by the following steps:

[0066] ;in, Indicates at time Select a new status probability; Target distribution In state The value at is the target distribution; Is the proposed distribution From the current state Select the next candidate state The conditional probability of Is the proposed distribution From candidate status Select Back to Current State The conditional probability of

[0067] The path integral method is used to integrate the sampled reaction paths to generate a complete reaction path diagram, and the total free energy change is calculated using the path integral method: in, is the free energy gradient, is the reaction path.

[0068] Calculating pathway energy changes helps identify the lowest-energy pathway and predict the most likely reaction path. Intermediate identification ensures the accuracy and completeness of the reaction pathway, facilitating further analysis of the reaction mechanism. Outputting a complete reaction pathway diagram: including all possible reaction pathways and their intermediates, for subsequent catalyst optimization design modules.

[0069] During the model training process, transfer learning is used to accelerate the model adaptation process and reduce the training time of new tasks, such as Figure 4 As shown. Use the model weights trained in similar tasks to initialize the model in the new task. Transfer learning is achieved by the following formula:

[0070] Design a transfer learning module and migrate the pre-trained model using the following formula: ,in, are the model parameters of the source task, is the data of the target task;

[0071] Reinforcement learning dynamically adjusts design parameters through a reward and penalty mechanism to maximize the performance of the catalyst. The Q-learning algorithm is used to optimize the design parameters. The core formula of Q-learning is:

[0072] ,in, Indicates that the status Next action of value, Used to measure the expected reward given a state and action; Indicates that the status Next action Immediate rewards after The discount factor is used to discount the weight of future rewards. The range is between [0,1]. The closer the value is to 1, the more important the future rewards are; the closer the value is to 0, the more emphasis is placed on immediate rewards. Indicates the next state Select an action The maximum value when . It represents the maximum future reward that can be obtained by the optimal strategy starting from the next state.

[0073] Output optimal catalyst design: including the optimized molecular structure of the catalyst and its design parameters under different reaction conditions.

[0074] During the reaction, the behavior and performance of the catalyst change over time, generating time series data that includes dynamic changes in reaction rate, temperature, pressure, and other parameters. Convolutional Long Short-Term Memory (ConvLSTM) networks are used to process this time series data. ConvLSTM can simultaneously capture temporal and spatial dependencies:

[0075] Use the following formula to process time series data:

[0076] ,in, is the hidden state at the current moment, represents the input data at the current time step t, is the weight matrix input to the hidden state, which is used to transform the input of the current time step Mapping to hidden state space; The hidden state to hidden state weight matrix, which converts the hidden state of the previous time step to Shoot to the current hidden state, is the bias term;

[0077] This formula captures the spatial feature changes in the time series through convolution operations while preserving temporal dependencies to generate a representation of the catalyst state at each moment.

[0078] In practical applications, ConvLSTM can not only identify the instantaneous state of a catalyst under current reaction conditions but also predict its future state. For example, in a photocatalytic reaction, ConvLSTM can analyze the impact of changes in light intensity on the reaction rate and predict future reaction progress. ConvLSTM can also identify critical moments in complex reactions, such as sudden changes in reaction rate at a certain temperature threshold, helping researchers adjust experimental conditions in a timely manner to optimize reaction results.

[0079] Dynamic reaction path visualization Figure 5 As shown in the figure: By applying the time series features extracted from ConvLSTM to the reaction path, a dynamic reaction path diagram is generated. Heat map generation: Heat maps are generated based on data at different time points to show the dynamic changes of various physical and chemical parameters. Through color changes, researchers can intuitively see the impact of factors such as temperature and pressure on the reaction process. For example, in a heat map, the change in color can reflect the increase in reaction rate when the temperature increases. Pathway diagram generation: A dynamic reaction path diagram is generated to show the changes in the behavior of the catalyst in the reaction, such as the generation and consumption of intermediates, and the formation process of the final product. The path diagram shows the progress of each step of the reaction and can be updated in real time to reflect the differences in the reaction path under different conditions.

[0080] Sensor data: This includes real-time data from multiple sensors, such as temperature, pressure, and reaction rate, which are input into the real-time monitoring and feedback optimization module. A Bayesian optimization algorithm is used to dynamically adjust the design parameters to ensure the catalyst performs optimally during the reaction. Bayesian optimization establishes a proxy model in the parameter space and uses this model to predict and optimize reaction conditions. Calculation process:

[0081] ,in, is the parameter to be optimized, is the utility function;

[0082] Sensor Data Fusion Module: This module uses Kalman and particle filtering techniques to fuse data from multiple sensors, enabling real-time monitoring and feedback of reaction processes. Kalman filtering is used for state estimation in linear systems. For example, under conditions of constant temperature and pressure, it can smooth sensor data, remove noise, and provide more accurate temperature and pressure estimates. Particle filtering is more suitable for state estimation in nonlinear, non-Gaussian systems, such as nonlinear reactions occurring on catalyst surfaces. Particle filtering uses multiple "particles" to represent possible states, and through a series of prediction and update steps, it gradually approximates the system's true state.

[0083] The update formula of Kalman filter is: ,in, is an estimate, is a measurement value, is a Kalman gain; is an observation matrix; the update formula of the particle filter is:

[0084]

[0085] wherein, is the state estimation at the moment, is the weight of the particle at the moment, is the state of the particle at the moment, is the number of particles.

[0086] Through the technical scheme of the embodiment, the intelligent design of the semiconductor catalyst based on graph grammar and the reaction mechanism optimization method fully utilizes advanced technologies such as graph convolution network, Markov chain Monte Carlo sampling, reinforcement learning, and convolutional long short-term memory network, and systematically improves the molecular structure design of the catalyst, the reaction path derivation, the dynamic optimization, and the real-time monitoring, etc. The comprehensive application of these technologies significantly improves the design accuracy of the catalyst, the accuracy of the reaction path, and the adaptability of the catalyst under different reaction conditions, ultimately providing a deep understanding and innovative solution for the optimization design of the catalyst, ensuring the efficiency and stability of the catalyst in practical applications.

Claims

1. A method for intelligent design of semiconductor catalysts and reaction mechanism optimization based on graph grammar, characterized in that: The specific steps include: Step 1: Design a graph grammar model generation and optimization module to automatically generate molecular structure models of semiconductor catalysts and screen out structural combinations with the best catalytic performance; Step 2: Design a reaction path automatic derivation module to simulate the interaction between molecules and generate a reaction path diagram; Step 3: Provide an intelligent catalyst design module to analyze the relationship between catalyst structure and performance and dynamically adjust the catalyst's molecular structure; Step 4: Design a reaction mechanism visualization and interpretation module to provide a graphical display of reaction pathways and structural changes, and support interactive analysis of multidimensional data; Step 5: Real-time monitoring and feedback optimization: By real-time monitoring of key parameters in the catalytic reaction process, the graph grammar model and design scheme are dynamically adjusted.

2. The method for intelligent design and reaction mechanism optimization of semiconductor catalysts based on graph grammar according to claim 1, characterized in that: The graph grammar model generation and optimization module in step 1 uses a graph convolutional network (GCN) to process catalyst molecular structure data and combines a greedy algorithm to screen and optimize the generated structure. Specifically, the module includes the following steps: Step 11: Design a graph convolutional network (GCN) module and use it to extract high-order relationship features between nodes and atoms and edges and chemical bonds in the molecular graph. Specifically, GCN updates the node representation using the following formula: in, is the adjacency matrix after adding self-loops, is the degree matrix, It is The node feature matrix of the layer, is the weight matrix of this layer, is the activation function; Step 12: Design a greedy algorithm module and apply the greedy algorithm to the generated catalyst structure to gradually select the local optimal solution and finally obtain the globally optimal catalyst structure combination; the algorithm selects the structural change optimization process that maximizes performance under the current conditions through a selection strategy: the structure is continuously screened in multiple iterations until the preset performance indicators are met.

3. The method for intelligent design and reaction mechanism optimization of semiconductor catalysts based on graph grammar according to claim 1, characterized in that: The reaction path automatic derivation module in step 2 uses the Markov Chain Monte Carlo (MCMC) method to sample the catalytic reaction path; specifically, the following steps are included: Step 21: Design an MCMC sampling module to sample potential reaction paths using the MCMC method. Use the Metropolis-Hastings algorithm to generate samples and calculate the following steps: ;in, Indicates at time Select a new status probability; Target distribution In state The value at is the target distribution; Is the proposed distribution From the current state Select the next candidate state The conditional probability of Is the proposed distribution From candidate status Select Back to Current State The conditional probability of Step 22: Use the path integral method to integrate the sampled reaction paths to generate a complete reaction path diagram, and calculate the total free energy change using the path integral method: in, is the free energy gradient, is the reaction path.

4. The method for intelligent design and reaction mechanism optimization of semiconductor catalysts based on graph grammar according to claim 1, characterized in that: The intelligent catalyst design module provided in step 3 combines transfer learning and reinforcement learning strategies; specifically, it includes the following steps: Step 31: Design a transfer learning module and migrate the pre-trained model using the following formula: ,in, are the model parameters of the source task, is the data of the target task; Step 32: Design a reinforcement learning module and use the Q-learning algorithm to optimize the design parameters and reward function Definition based on catalyst performance: ,in, Indicates that the status Next action of value, Used to measure the expected reward given a state and action; Indicates that the status Next action Immediate rewards after The discount factor is used to discount the weight of future rewards. The range is between [0,1]. The closer the value is to 1, the more important the future rewards are; the closer the value is to 0, the more emphasis is placed on immediate rewards. Indicates the next state Select an action The maximum Value; represents the maximum future reward that can be obtained by the optimal strategy starting from the next state.

5. The method for intelligent design and reaction mechanism optimization of semiconductor catalysts based on graph grammar according to claim 1, characterized in that: The reaction mechanism visualization and interpretation module in step 4 uses a convolutional long short-term memory (ConvLSTM) network to process multidimensional time series data and visualize the results. Specifically, it includes the following steps: Step 41: Use the ConvLSTM processing module to process the time series data using the following formula: ,in, is the hidden state at the current moment, represents the input data at the current time step t, is the weight matrix input to the hidden state, which is used to transform the input of the current time step Mapping to hidden state space; The hidden state to hidden state weight matrix, which converts the hidden state of the previous time step to Mapped to the current hidden state, is the bias term; Step 42: Design dynamic reaction path visualization. By generating heat maps and path diagrams, map time series changes into dynamic reaction paths to help R&D personnel track and analyze key reaction steps.

6. The method for intelligent design and reaction mechanism optimization of semiconductor catalysts based on graph grammar according to claim 1, characterized in that: The real-time monitoring and feedback optimization module in step 5 integrates Bayesian optimization and sensor data fusion technology, and specifically includes the following steps: Step 51: Design a Bayesian optimization module and use the following formula to optimize parameters: ,in, is the parameter to be optimized, is the utility function; Step 52: Design a sensor data fusion module and use Kalman filtering and particle filtering to fuse data from multiple sensors. The update formula of Kalman filtering is: ,in, is an estimate, is the measured value, is the Kalman gain; is the observation matrix; the update formula of the particle filter is: ,in, It is The state estimate at time t, It is Particles in The weight of the moment, It is Particles in The state of the moment, is the number of particles.

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