Semiconductor catalyst intelligent design and reaction mechanism optimization method based on graph grammar

Abstract

The invention discloses a semiconductor catalyst intelligent design and reaction mechanism optimization method based on graph grammar, which comprises the following steps: step 1, a graph grammar model generation and optimization module: automatically generating a molecular structure model of a semiconductor catalyst, and screening out a structure combination with optimal catalytic performance; 2, a reaction path automatic derivation module which simulates the interaction between molecules and generates a reaction path diagram; 3, an intelligent catalyst design module: analyzing the relationship between the structure and the performance of the semiconductor catalyst, and dynamically adjusting the molecular structure of the catalyst; 4, a reaction mechanism visualization and interpretation module which provides graphical display of reaction paths and structure changes and supports interaction analysis of multi-dimensional data; 5, a real-time monitoring and feedback optimization module is used for monitoring key parameters in the catalytic reaction process in real time and dynamically adjusting a graph grammar model and a 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 of semiconductor catalysts and reaction mechanism optimization based on graph grammar. Background Art

[0002] Catalysts play a vital role in catalytic chemical reactions, and their performance directly affects the reaction rate, selectivity, and purity of the product. In modern industry, especially in the fields of energy conversion, environmental governance, and chemical synthesis, the role of catalysts is particularly important. Semiconductor materials have been widely used in photocatalysis, electrocatalysis, and other fields due to their unique electronic structure and optical properties. Semiconductor catalysts have shown great application potential in solar energy conversion, pollutant degradation, and green chemical synthesis.

[0003] However, traditional semiconductor catalyst design methods mainly rely on repeated experiments in the laboratory and the experience of researchers, which is not only time-consuming and labor-intensive, but also difficult to achieve large-scale optimization and innovation in a short period of time. Especially when faced with complex semiconductor material systems, traditional methods often find it difficult to fully consider the interactions between multiple physical and chemical factors, resulting in limited potential for improving catalyst performance. With the development of artificial intelligence and machine learning technologies, catalyst design is gradually moving towards automation and intelligence.

[0004] In existing research, although some computer vision and machine learning technologies have been applied to catalyst design optimization, these methods mostly focus on basic data processing and analysis, such as structural feature extraction or optimization of a single reaction variable. These methods often show limitations when dealing with complex semiconductor catalyst systems, and it is difficult to fully cope with the challenges of multidimensional data. In addition, when conducting a comprehensive evaluation and dynamic optimization of catalyst performance, a lot of manual intervention is still required, which limits their promotion in practical applications. Summary of the invention

[0005] The present invention provides a semiconductor catalyst intelligent design and reaction mechanism optimization method based on graph grammar, aiming to overcome the deficiencies in the prior art 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, the 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 effect 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 comprises the following steps: Step 1: Design a graph grammar model generation and optimization module to automatically generate a molecular structure model of a semiconductor catalyst and screen out the structural combination 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 molecular structure of the catalyst; Step 4: Design a reaction mechanism visualization and interpretation module to provide a graphical display of reaction paths and structural changes and support interactive analysis of multidimensional data; 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.

[0007] Furthermore, the graph grammar model generation and optimization module in step 1 uses a graph convolutional network GCN to process the catalyst molecular structure data, and combines a greedy algorithm to screen and optimize the generated structure; specifically, the following steps are included: 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, thereby more accurately capturing the complex interaction between electronic structure and chemical bonds in semiconductor materials.

[0008] 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;

[0009] 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 global optimal catalyst structure combination; the algorithm is implemented through the following steps: Selection strategy: Select the structural change 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.

[0010] Furthermore, 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 and generate samples using the Metropolis-Hastings algorithm. This module can efficiently explore possible semiconductor reaction paths in a complex reaction environment and is calculated through the following steps: ;in, Indicates at time Select new status The probability of Target distribution In Status 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 .

[0011] Step 22: Using the path integration method, the sampled reaction paths are integrated to generate a complete reaction path diagram, and the total free energy change is calculated using the path integration method.

[0012] in, is the free energy gradient, is the reaction path.

[0013] Furthermore, the intelligent catalyst design module provided in step 3 combines transfer learning and reinforcement learning strategies; specifically includes the following steps: Step 31: Design a transfer learning module to improve the model’s learning efficiency in new tasks and reduce training time by leveraging knowledge learned in similar semiconductor catalysis tasks.

[0014] ,in, are the model parameters of the source task, is the data of the target task;

[0015] Step 32: Design a reinforcement learning module and use Q-learning algorithm to optimize design parameters and reward function Defined by the performance of the catalyst: Helps the model find the optimal solution in the design space.

[0016] ,in, Indicates in status Next action of value, Used to measure the expected reward given a state and action; Indicates in status Next action Immediate rewards after is a discount factor used to discount the weight of future rewards. Its 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 that in the next state Select Action The maximum Value. It represents the maximum future reward that can be obtained by the optimal strategy starting from the next state.

[0017] Furthermore, the reaction mechanism visualization and interpretation module in step 4 uses the convolutional long short-term memory network ConvLSTM to process the multidimensional time series data and visualize the results, which specifically includes the following steps: Step 41: Using a ConvLSTM processing module to process the time series data of the semiconductor catalyst, capture the time-dependent changes in the reaction process, and help identify the key steps in the semiconductor catalytic reaction path; ,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 weight matrix from hidden state to hidden state. It converts the hidden state of the previous time step to Shoot to the current hidden state, is the bias term; Step 42: Design dynamic reaction path visualization. By generating heat maps and path diagrams, time series changes are mapped into dynamic reaction paths to help R&D personnel track and analyze key reaction steps.

[0018] Furthermore, 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 dynamically adjust the design parameters through the Bayesian optimization algorithm to ensure that the semiconductor catalyst is always in the best working state during the reaction process.

[0019] ,in, is the parameter to be optimized, is the utility function;

[0020] Step 52: Design a sensor data fusion module, use Kalman filtering and particle filtering to fuse data from multiple sensors, and achieve real-time monitoring and feedback of the semiconductor catalytic reaction process.

[0021] The update formula of Kalman filter is: ,in, is an estimate, is the measured value, is the Kalman gain; is the observation matrix; the update formula of particle filter is:

[0022] in, It is The state estimate at time It is Particles in The weight of the moment, It is Particles in The state of the moment, is the number of particles.

[0023] Beneficial effects of the present invention: 1. By integrating graph convolutional networks (GCN) and greedy algorithms, we have achieved significant breakthroughs in processing and optimizing semiconductor catalyst molecular structure models. Compared with traditional optimization algorithms, GCN can capture the complex relationships between catalyst molecules and provide deeper feature expressions, while greedy algorithms are more efficient in solving problems with large search spaces and complex structures, ensuring rapid convergence of local optimality. Other traditional optimization methods, such as stochastic gradient descent or simulated annealing, often lack processing speed and accuracy due to the complexity of molecular structure models, while our method strikes a balance between accuracy and efficiency, significantly improving the performance and stability of catalysts.

[0024] 2. Using the Markov Chain Monte Carlo MCMC method and the path integral method, we can effectively derive the optimal path for semiconductor catalytic reactions. The MCMC method performs well in exploring complex reaction paths, covering a wide range of state spaces and avoiding falling into local extremes, while the path integral method provides a comprehensive reaction energy analysis, ensuring the accuracy and predictability of the reaction path. In contrast, other algorithms such as simple gradient tracking are prone to falling into local optimality and it is difficult to fully explore the entire reaction path space, so it is difficult to ensure the comprehensiveness of the path derivation.

[0025] 3. Combining transfer learning and reinforcement learning strategies, the intelligent catalyst design module can dynamically adjust the molecular structure of the catalyst under different reaction conditions to better adapt to new catalytic tasks. Traditional methods such as rule-based optimization methods often lack flexibility and are difficult to adapt to the changing environment and the needs of new tasks. Our method transfers knowledge from existing tasks through transfer learning, and combines reinforcement learning to adaptively adjust the structure, quickly optimize the overall performance, and greatly improve the practicality and adaptability of the catalyst.

[0026] 4. Through the convolutional long short-term memory network ConvLSTM and dynamic reaction path visualization technology, we can track and analyze the reaction process of semiconductor catalysts in real time. Compared with the static reaction path analysis method, ConvLSTM can combine time series information to provide more accurate dynamic process prediction, which makes the understanding of the catalyst reaction process more in-depth and helps to adjust the reaction conditions in time, thereby improving the reaction efficiency.

[0027] 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 dealing with high-dimensional and nonlinear problems, and can efficiently find the optimal parameter combination, while sensor data fusion ensures the accuracy and consistency of monitoring data, overcomes the deviation and error problems that may exist in single sensor data, and makes the entire monitoring and adjustment process more reliable and efficient. Compared with simple fixed parameter control methods, our method can make intelligent adjustments based on real-time data, thereby significantly improving the performance of the catalyst. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flow chart of the intelligent design and reaction mechanism optimization method of semiconductor catalysts based on graph grammar; Figure 2 Generate and optimize module function graphs for graph grammar models; Figure 3 Automatically derive modular functional diagrams for reaction pathways; Figure 4 Design module function diagram for intelligent catalyst; Figure 5 Functional diagram of modules for visualization and interpretation of reaction mechanisms. DETAILED DESCRIPTION

[0029] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. It should be noted that the words "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to directions in the accompanying drawings, and the words "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0030] This embodiment provides a method for intelligent design of semiconductor catalysts and reaction mechanism optimization based on graph grammar, comprising the following steps: Step 1: Design a graph grammar model generation and optimization module to automatically generate a molecular structure model of a semiconductor catalyst and screen out the structural combination 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 molecular structure of the catalyst; Step 4: Design a reaction mechanism visualization and interpretation module to provide a graphical display of reaction paths and structural changes and support interactive analysis of multidimensional data; 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.

[0031] 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 embodiment, the system specifically analyzes the molecular structure characteristics of semiconductor catalysts and their performance under different reaction conditions, such as the electronic structure of the catalyst, the reaction path, and the generation of intermediates, so as to provide a deep understanding and optimization of catalyst design and reaction mechanism.

[0032] In a specific embodiment, it is assumed that the user uploads the following data sets, including molecular structure data, reaction condition data, and performance index data. Among them, molecular structure data: 1000 molecular structures of semiconductor catalysts, represented in SMILES format. Reaction condition data: 1000 groups of reaction condition data, including temperature, pressure, reaction time, light intensity, etc., each group of data contains 10 different conditions. Performance index data: 1000 catalytic reaction efficiency, selectivity, product purity and other indicators.

[0033] First, the data is preprocessed and the input data is standardized. 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 the efficiency, selectivity and product purity of the catalytic reaction are standardized so that the model can effectively compare the 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.

[0034] The processed data is input into the graph grammar model generation and optimization module, such as Figure 2 As shown in Figure 2, the GCN module is used to extract high-order relationship features between nodes-atoms and edges-chemical bonds in molecular graphs; special attention is paid to the distribution of electronic structures and the capture of chemical bond characteristics in semiconductor catalysts.

[0035] 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 through 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 global optimal catalyst structure combination; the algorithm is implemented through the following steps: Selection strategy: Select the structural change 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.

[0036] 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; By performing convolution operations on the molecular graph, the node representation is updated layer by layer to capture important features in the molecular structure, such as the type and strength of chemical bonds. By calculating the feature weights of each node-atom and edge-chemical bond, the structural features that have the greatest impact on catalytic performance are identified. A greedy algorithm is applied to the extracted features for structural optimization. The algorithm starts from the current optimal solution, gradually selects the local optimal solution, and finally finds the global optimal solution. The optimized molecular structure model is output: including the key molecular structure and its features, for use by the subsequent reaction path derivation module.

[0037] 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: Calculated by the following steps: ;in, Indicates at time Select new status The probability of Target distribution In Status 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 The sampled reaction paths are integrated to generate a complete reaction path diagram using the path integration method, and the total free energy change is calculated using the path integration method: in, is the free energy gradient, is the reaction path.

[0038] Calculate the energy change of the path to help identify the lowest energy path and predict the most likely way the reaction will occur. Intermediate identification ensures the accuracy and completeness of the reaction path and helps further analyze the reaction mechanism. Output a complete reaction path diagram: including all possible reaction paths and their intermediates for use in subsequent catalyst optimization design modules.

[0039] 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 through the following formula: 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; Reinforcement learning dynamically adjusts design parameters through reward and penalty mechanisms 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: ,in, Indicates in status Next action of value, Used to measure the expected reward given a state and action; Indicates in status Next action Immediate rewards after is a discount factor used to discount the weight of future rewards. Its 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 that in the next state Select Action It represents the maximum future reward that can be obtained by the optimal strategy starting from the next state.

[0040] Output optimal catalyst design: including the optimized molecular structure of the catalyst and its design parameters under different reaction conditions.

[0041] During the reaction, the behavior and performance indicators of the catalyst change over time, and the generated time series data include dynamic changes in reaction rate, temperature, pressure, etc. The convolutional long short-term memory network ConvLSTM is used to process these time series data. ConvLSTM can capture the dependence changes in time and space at the same time: Use the following formula to process time series data: ,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 transforms the hidden state of the previous time step Shoot to the current hidden state, is the bias term; This formula captures the spatial feature changes in the time series through convolution operations while preserving the temporal dependencies to generate a representation of the catalyst state at each moment.

[0042] In practical applications, ConvLSTM can not only identify the instantaneous state of the catalyst under the current reaction conditions, but also predict the future state. For example, in a photocatalytic reaction, ConvLSTM can analyze the effect of changes in light intensity on the reaction rate and predict the future progress of the reaction. ConvLSTM can also identify key time points in complex reactions, such as sudden changes in reaction rate under a certain temperature threshold, helping researchers to adjust experimental conditions in a timely manner to optimize reaction results.

[0043] Dynamic reaction path visualization Figure 5As shown in the figure: The time series features extracted from ConvLSTM are applied to the reaction path to generate a dynamic reaction path diagram. Heat map generation: Generate a heat map based on the 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 rises. Path map generation: Generate a dynamic reaction path map 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 map 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.

[0044] Sensor data: includes real-time data from multiple sensors, such as temperature, pressure, reaction rate, etc., which are input into the real-time monitoring and feedback optimization module. The Bayesian optimization algorithm is used to dynamically adjust the design parameters to ensure the best working state of the catalyst during the reaction process. Bayesian optimization establishes a proxy model in the parameter space and uses the model to predict and optimize the reaction conditions. Calculation process: ,in, is the parameter to be optimized, is the utility function; Sensor data fusion module: Use Kalman filter and particle filter technology to fuse the data of multiple sensors to achieve real-time monitoring and feedback of the reaction process. Kalman filter is used for state estimation of linear systems. For example, under constant temperature and pressure conditions, Kalman filter can smooth sensor data, remove noise, and provide more accurate temperature and pressure estimates. Particle filter is suitable for state estimation of nonlinear and non-Gaussian systems, such as nonlinear reaction processes occurring on the surface of catalysts. Particle filter uses multiple "particles" to represent possible states, and gradually approaches the true state of the system through a series of prediction and update steps.

[0045] The update formula of Kalman filter is: ,in, is an estimate, is the measured value, is the Kalman gain; is the observation matrix; the update formula of particle filter is:

[0046] in, It is The state estimate at time It is Particles in The weight of the moment, It is Particles in The state of the moment, is the number of particles.

[0047] Through the technical solution of this embodiment, the semiconductor catalyst intelligent design and reaction mechanism optimization method based on graph grammar makes full use of advanced technologies such as graph convolutional networks, Markov chain Monte Carlo sampling, reinforcement learning, and convolutional long short-term memory networks, and makes systematic improvements in the molecular structure design, reaction path derivation, dynamic optimization, and real-time monitoring of catalysts. The comprehensive application of these technologies significantly improves the design accuracy of catalysts, the accuracy of reaction paths, and the adaptability of catalysts under different reaction conditions, ultimately providing a deep understanding and innovative solutions for the optimization design of catalysts, ensuring the efficiency and stability of catalysts in practical applications.

Claims

1. A method for intelligent design and reaction mechanism optimization of semiconductor catalysts 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 a molecular structure model of a semiconductor catalyst and screen out the structural combination 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 molecular structure of the catalyst; Step 4: Design a reaction mechanism visualization and interpretation module to provide a graphical display of reaction paths and structural changes and support interactive analysis of multidimensional data; 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.

2. According to claim 1, a semiconductor catalyst intelligent design and reaction mechanism optimization method based on graph grammar is 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, it 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 global optimal catalyst structure combination; the algorithm selects the structural change optimization process that maximizes the performance under the current conditions through the 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, it includes the following steps: Step 21: Design the MCMC sampling module, use the MCMC method to sample potential reaction paths, use the Metropolis-Hastings algorithm to generate samples, and calculate through the following steps: ;in, Indicates at time Select new status probability; Target distribution In Status 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: Using the path integral method, the sampled reaction paths are integrated 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.

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 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 Q-learning algorithm to optimize design parameters and reward function Definition based on catalyst performance: ,in, Indicates in status Next action of value, Used to measure the expected reward given a state and action; Indicates in status Next action Immediate rewards after is a discount factor used to discount the weight of future rewards. Its 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 that in the next state Select 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 the convolutional long short-term memory network ConvLSTM to process multidimensional time series data and visualize its results, which specifically 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 transforms the hidden state of the previous time step Shoot to the current hidden state, is the bias term; Step 42: Design dynamic reaction path visualization. By generating heat maps and path diagrams, time series changes are mapped 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 the data of 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 particle filter is: ,in, It is The state estimate at time 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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