Method for predicting advantages and disadvantages of acetylene hydrochlorination catalyst ligand based on random forest algorithm

Abstract

The invention discloses an acetylene hydrochlorination catalyst ligand quality prediction method based on a random forest algorithm, and belongs to the field of material informatics. The method comprises the following steps of (1) collecting existing public literatures to establish a database, (2) extracting SMILES character strings, reaction conditions and performance improvement rates of ligands and constructing a tensor matrix, (3) scoring and ranking by adopting a random deep forest algorithm, and (4) inputting unknown ligands to obtain effect ranking. The invention further provides a computer readable storage medium storing the method and a device composed of the medium. According to the method, reaction conditions are increased, and the accuracy of predicting the advantages and disadvantages of the catalyst ligand in the acetylene hydrochlorination reaction is improved.

Description

Technical Field

[0001] The present invention relates to a method for predicting the quality of acetylene hydrochlorination reaction catalyst ligands based on random forests, in particular to a method combining catalysis and random forest knowledge design and realizing a set of artificial intelligence-assisted screening of the quality of acetylene hydrochlorination reaction catalyst ligands, belonging to the technical field of materials informatics. Background Art

[0002] Vinyl chloride is an important chemical raw material that can be used to polymerize into polyvinyl chloride (PVC). Polyvinyl chloride is widely used in plastic products and is one of the most widely used plastics in the world. As a monomer of polyvinyl chloride, it is particularly important to synthesize vinyl chloride efficiently and in large quantities. At present, the most commonly used method is the hydrochlorination of acetylene to prepare vinyl chloride, and the most commonly used catalyst is mercury (Hg). As a heavy metal element, Hg is very harmful to the human body and the environment. Therefore, finding a catalyst that is equally efficient and clean and pollution-free has become the most important issue at present. At present, the main research directions of mercury-free catalysts are: ruthenium-based, platinum-based, gold-based and copper-based. Based on these four types of metals as the substrate, designing a class of efficient hydrochlorination catalysts is the main research direction at present.

[0003] The design of catalysts usually requires a lot of experiments, and the cost of manpower and material resources is high. With the rapid development of artificial intelligence, artificial intelligence has been widely used in various fields. The use of artificial intelligence can greatly reduce time costs and unnecessary trial and error processes. It is an efficient and fast tool. At present, it is mainly through training artificial intelligence models to design and predict mercury-free catalysts. Seeing the advantages of artificial intelligence in time and cost, it is decided to design and predict mercury-free catalysts with the assistance of artificial intelligence.

[0004] The design of catalysts using artificial intelligence learning is the main research direction, and the main algorithm used is the random forest algorithm. The advantages of the random forest algorithm are mainly high tolerance to errors and better prediction effects than linear regression in some fields. Factors such as reaction conditions are included in the scope of the model to be considered, and the model is trained. The random forest model is mainly used to predict the effect of the ligand of the mercury-free catalyst on the performance of the catalytic reaction. The present invention shows excellent prediction effect and prediction value under sparse data set training and out-of-sample prediction. Summary of the invention

[0005] The technical problem to be solved by the present invention is to propose a prediction method for the quality of ligands of acetylene hydrochlorination reaction catalysts based on a random forest algorithm, and to establish a model for existing papers and experimental data using an integrated learning algorithm to obtain a prediction model, thereby predicting the conversion rate of catalysts with unknown ligands added, and guiding catalyst synthesis. The method first constructs a ligand database by reading chemical ligands obtained from various related documents, and then proposes a novel prediction model for the conversion rate of acetylene hydrochlorination of catalysts with unknown ligands added, which combines a feature extractor and a random forest algorithm. The model can calculate its ECFP fingerprint based on the SMILES string of the relevant ligand, and can perform specialized prediction of the conversion rate in combination with a specific experimental environment, thereby improving the accuracy of the prediction.

[0006] In order to solve the technical problem of the present invention, the present invention is implemented by the following technical solution: a method for predicting the quality of ligands of acetylene hydrochlorination catalyst based on random forest algorithm, the method comprising the following steps:

[0007] (1) collecting and extracting data from literature as a data set for training a model; catalyst ligands, reaction conditions and ligand properties are used to train the model; the reaction conditions are temperature and HCl ratio;

[0008] (2) Extract the composite SMILES string of the ligand molecule and construct a tensor matrix, that is, extract the 64-bit ECFP fingerprint of the ligand and separate it bit by bit and then reassemble it to construct a tensor matrix; convert the temperature and HCl ratio experimental conditions into vectors and add them to the feature matrix; the complete feature matrix contains not only the molecular structure information of the ligand, but also different experimental conditions;

[0009] (3) Input the complete feature matrix into the model based on the random forest algorithm for scoring training; each column in the tensor matrix is ​​a feature, and the importance attribute of each feature to the model performance is set. The importance parameter of each feature is obtained by preliminarily training the random forest model using database data, and the parameters are arranged in descending order. The features with lower importance are removed in proportion to reduce the dimension and prevent overfitting;

[0010] (4) The simplified feature matrix is ​​input into the random forest for model scoring training and the SMILES string of the unknown performance ligand is input to obtain the result of the ligand quality.

[0011] Preferably, the catalyst ligands, reaction conditions and ligand properties are used to train the model: each atom and chemical bond in the SMILES string is a word, and a corpus is naturally formed by collecting many compounds, using ECFP (Extended Connectivity Fingerprint) technology to collect information, combined with reaction conditions, to train scoring weights.

[0012] Preferably, the ligand complex SMILES string and reaction conditions are embedded in the model to construct a tensor matrix, and the trained LDT model is used to embed the SMILES string of the ligand into the tensor to output the score.

[0013] Preferably, the method comprises the following steps:

[0014] Step 1: Establish a data set and collect information on known catalyst ligands from the literature. The molecular structure of the ligand and the corresponding composite SMILES string, temperature, HCl ratio, and conversion rate are recorded in the database;

[0015] Step 2: Convert the molecular structure of the ligand into an ECFP fingerprint

[0016] The composite SMILES string of each ligand is converted into a 64-bit binary vector called "ECFP fingerprint". For the SMILES string of known ligands, a search method is used to map each functional group to a molecular fingerprint composed of 0 and 1 from the RDKit open source library, and then combine them into a 64-bit molecular fingerprint. Different sequences of 0 and 1 represent different functional groups, thus reflecting the influence of different ligand structures.

[0017] Step 3: Construct the feature tensor matrix

[0018] Each ligand collected has its own 64-bit molecular fingerprint. The fingerprint data of all ligands are integrated into a tensor matrix. Each row of the matrix represents a ligand and each column is a feature bit.

[0019] The experimental conditions temperature and HCl ratio were converted into vectors and added to the feature matrix;

[0020] Step 4: Build and tune the model architecture

[0021] Input the complete feature matrix into the random forest model for preliminary training. The random forest model automatically calculates the importance of each feature. These scores indicate the contribution of each feature to the prediction.

[0022] By adjusting the number of trees, we tried the number of trees (5, 10, 15, 20), the minimum number of samples for leaf merging (1, 2, 3), and the minimum gain for splitting (0, 0.05, 0.1). After model training, we found that the contribution of some features was extremely low. Among the 64-bit fingerprints, 7 bits had low contributions. We removed these unimportant features and only retained the features with greater influence. This feature screening can effectively reduce noise and prevent model overfitting.

[0023] Step 5: Model Evaluation

[0024] The simplified feature matrix is ​​input into the random forest model and training continues. The target variable is the conversion rate. Through model training, the model gradually learns the relationship between ligand structure, experimental conditions and conversion rate. After training, the model is evaluated using the validation set. Assume that the conversion rate of ligand A is predicted to be 78%, while the actual conversion rate is 80%, with an error of 2%.

[0025] Through model training, the parameters were determined as follows: number of trees: 10, minimum number of samples for leaf merging: 1, minimum gain for splitting: 0.05. After training, the model was evaluated using the validation set. Finally, the probability that the difference between the predicted conversion rate and the actual conversion rate of different ligands was less than 5% reached more than 50%;

[0026] Step 6: Predict the conversion rate of unknown ligands

[0027] After training, the model can be used to predict the conversion rate of a new ligand, ligand X; RDKit is used to generate a 64-bit fingerprint of ligand X, which is then merged into the feature matrix with the corresponding experimental conditions; after the temperature and HCl ratio of the experimental conditions of X are input into the model, the model outputs the predicted conversion rate improvement rate; this output result can help determine whether ligand X has a good catalytic effect under the reaction conditions.

[0028] The method for predicting the quality of ligands for acetylene hydrochlorination catalyst based on random forest algorithm is used to screen out ligands. As a modifier for acetylene hydrochlorination catalyst.

[0029] The storage medium has a computer program, and the computer program can run the method for predicting the quality of acetylene hydrochlorination reaction catalyst ligand based on random forest algorithm as described in claim 1.

[0030] A device for predicting the quality of catalyst ligands for acetylene hydrochlorination reaction based on a random forest algorithm, wherein the device is equipped with the computer-readable storage medium according to claim 7.

[0031] The present invention relates to a method for catalysis of adding unknown ligands and predicting the conversion rate of acetylene hydrochlorination. The specific steps of the method are as follows: (1) constructing a chemical ligand database for training a model; (2) extracting a 64-bit ECFP fingerprint of the ligand and separating it bit by bit and then combining it to construct a tensor matrix; converting the experimental conditions of temperature and HCl ratio into vectors and adding them to a feature matrix; the complete feature matrix not only contains the molecular structure information of the ligand, but also contains different experimental conditions; (3) inputting the complete feature matrix into a random forest as a preliminary feature vector, wherein each column in the matrix is ​​a feature, and setting the importance attribute of each feature to the model performance. The importance parameter of each feature is obtained by preliminarily training the random forest model using database data, and the parameters are arranged in descending order. The features with lower importance are removed in proportion to reduce the dimension and prevent overfitting; (4) inputting the simplified feature matrix into a random forest for model training and evaluating its performance.

[0032] First, we collected a large amount of chemical ligand data by consulting various relevant literature. Each ligand needs to include its molecular structure, properties, and conversion rate in the acetylene hydrochlorination reaction. The ligand information was obtained through literature surveys, laboratory data collection, and the public chemical database PubChem.

[0033] Furthermore, the chemical ligands are feature extracted through ECFP (Extended Connectivity Fingerprint) technology. ECFP can effectively represent the topological information of the molecule and convert the chemical structure of the ligand into a fixed-length vector representation. The 64-bit ECFP fingerprint of each ligand is extracted using the chemical informatics tool RDKit. The neighborhood information of atoms or groups in the molecule is extracted through an iterative algorithm and constructed into a series of binary bit representations to generate a fixed-length fingerprint. The extracted 64-bit ECFP fingerprint is converted into a tensor matrix. The ECFP fingerprint of each ligand is a 64-dimensional binary vector. When constructing the ECFP tensor, the fingerprint of each ligand will become a row vector of a matrix. When there are multiple ligands, these row vectors will form a two-dimensional tensor, in which each row represents the fingerprint of a ligand.

[0034] Furthermore, a random forest model is trained using the tensor matrix obtained in the previous step as feature input and the conversion rate as the target output. As the training progresses, the random forest model will assign an importance score to each input feature, which reflects the contribution of each feature to the model performance. Through these scores, it is possible to identify which features contribute more to predicting the conversion rate and which features contribute less to the model. Sorting the feature importance and proportionally removing the lower importance features will help reduce the negative impact of irrelevant or redundant features on the model, thereby reducing the complexity of the model and reducing the risk of overfitting.

[0035] Furthermore, the temperature and HCL ratio, which are closely related to the acetylene hydrochlorination reaction, are selected. These variables will affect the reaction performance of the catalyst, so it is necessary to consider these factors in the model. The environmental variables are converted into a tensor matrix, and the value of each of them is used as a column in the matrix to construct a two-dimensional tensor of environmental variables. The tensor matrix of environmental variables is merged with the reduced ECFP tensor matrix by column to obtain a complete feature matrix.

[0036] Furthermore, the complete feature matrix obtained in the previous step is used as input, and the conversion rate is used as the target variable to input into the random forest model for training, and the training parameters are further adjusted.

[0037] Beneficial effects:

[0038] The present invention uses a model based on a random forest algorithm (Random Forest). The advantage of random forest is that it is good at handling noise points and can reduce the impact of errors caused by some experiments. This type of model can greatly reduce the workload, and in future development, as the data set increases, the accuracy of prediction will continue to improve. We have used this algorithm to design and predict multiple high-performance mercury-free hydrochlorination catalysts, and they have shown high accuracy and catalytic performance.

[0039] Existing catalyst conversion rate prediction methods usually focus on data features from a single source, such as modeling based only on the molecular structure information of the ligand or a single experimental environment variable. Such a model may ignore the interaction between the chemical ligand and the experimental environment variables and their combined impact on the catalytic reaction performance. The present invention adopts a method of combining the ECFP fingerprint of the ligand with the experimental environment variables. In this way, the model not only takes into account the molecular structure of the ligand during the catalyst preparation process, but also can perform dynamic predictions based on changes in experimental conditions, thereby more comprehensively capturing the factors affecting catalyst performance. This multi-feature fusion greatly improves the prediction accuracy of the model, especially when faced with unknown ligands and changing experimental conditions.

[0040] Many traditional methods use simple molecular descriptors or features to represent ligands. These methods usually fail to effectively capture the complexity of ligand structures, resulting in poor model performance. The present invention uses ECFP fingerprints to extract features of chemical ligands and converts them into 64-bit binary vectors. By converting the ECFP fingerprint of each ligand into a tensor matrix and inputting it into the model, the present invention can efficiently express the complex information of the molecular structure. This tensor matrix form can capture higher-order relationships in molecules, thereby improving the model's ability to represent ligands and prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] Figure 1 It is a schematic diagram of the method flow of the present invention DETAILED DESCRIPTION

[0043] The technical solution of the present invention is further explained below through embodiments in combination with the accompanying drawings.

[0044] Example 1

[0045] Step 1: Create a dataset

[0046] First, collect information about known catalyst ligands. For example, a catalyst prepared with ligand A was found, which hydrochlorinated acetylene at 230°C and HCl ratio of 1.15 with a conversion of 80%. This information (molecular structure, SMILES string, experimental conditions, conversion) was recorded in the database. Considering the influence of different batches of activated carbon, we set the target value as the difference between the conversion rate with and without ligand added, and calculated all the target values.

[0047] Step 2: Convert the molecular structure of the ligand into an ECFP fingerprint

[0048] In order for the model to "understand" the structure of the ligand, first obtain the SMILES string of the ligand from the website https: / / pubchem.ncbi.nlm.nih.gov / . Further, transcode the SMILES string of each ligand into a 64-bit binary vector. Use the chemical tool RDKit to complete this step and generate the ECFP fingerprint of each ligand. For example, the SMILES string of ligand A is C=CCl, and RDKit converts it into the following 64-bit binary fingerprint: [1, 0, 0, 1, 1, 0, 1,...]. Each bit in this string of data represents a detailed information of the molecular structure, such as whether it contains certain specific chemical bonds, groups, etc. For subsequent model training, this fingerprint becomes the "identity identifier" of the ligand.

[0049] Step 3: Construct the feature tensor matrix

[0050] Suppose 100 ligands are collected, each with its own 64-bit fingerprint. The fingerprint data of all ligands are integrated into a tensor matrix. Each row of the matrix represents a ligand, and each column is a feature bit. For example, the first two rows can be like this:

[0051] | Ligand number | Position 1 | Position 2 | Position 3 | Position 4 | Position 5 | ... |

[0052] |----------|-----|-----|-----|-----|-----|-----|

[0053] | A | 1 | 0 | 0 | 1 | 1 | ... |

[0054] | B | 0 | 1 | 1 | 0 | 1 | ... |

[0055] Under this matrix structure, the molecular structural characteristics of all ligands are clearly visible, making it convenient for subsequent input into the model.

[0056] Step 4: Add experimental conditions

[0057] The conversion rate of acetylene hydrochlorination is not only affected by the ligand structure, but also closely related to the experimental conditions (such as temperature and HCl ratio). In order to improve the prediction accuracy, the experimental conditions such as temperature and HCl ratio are converted into vectors and added to the feature matrix. Assuming that the experimental temperature of ligand A is 230°C and the HCl ratio is 1.15, these values ​​can be encoded as additional feature columns, such as:

[0058] | Ligand number | Position 1 | Position 2 | ... | Temperature | HCl ratio |

[0059] |----------|-----|-----|-----|------|---------|

[0060] | A | 1 | 0 | ... | 230 | 1.15 |

[0061] | B | 0 | 1 | ... | 250 | 1.05 |

[0062] In this way, the feature matrix not only contains the molecular structure information of the ligand, but also includes different experimental conditions, so that the model can take into account the differences in reaction performance caused by environmental changes.

[0063] Step 5: Feature Screening

[0064] The complete feature matrix is ​​input into the random forest model for preliminary training. The random forest model automatically calculates the importance of each feature. These scores indicate how much each feature contributes to the prediction.

[0065] By adjusting the number of trees, the optimization adjustment tried the number of trees (5, 10, 15, 20), the minimum number of samples for leaf merging (1, 2, 3) and the minimum gain for splitting (0, 0.05, 0.1).

[0066] After model training, it was found that the contribution of some features was extremely low. Among the 64-bit fingerprints, 7 bits had low contributions. These unimportant features were removed, and only the features with greater influence were retained. This feature screening can effectively reduce noise and prevent model overfitting.

[0067] Step 6: Model training and evaluation

[0068] The simplified feature matrix is ​​input into the random forest model and training continues. The target variable is the conversion rate. Through model training, the model gradually learns the relationship between ligand structure, experimental conditions and conversion rate. After training, the model is evaluated using the validation set. Assume that the conversion rate of ligand A is predicted to be 78%, while the actual conversion rate is 80%, with an error of 2%. Through this evaluation, the model parameters can be adjusted. After trial and error, the optimal parameters are determined to be the number of trees: 10, the minimum number of samples for leaf merging: 1, and the minimum gain for splitting: 0.05, to ensure the accuracy of the model's predictions for different ligands.

[0069] Step 7: Predict the conversion rate of unknown ligands

[0070] After training, the model can be used to predict the conversion rate of new ligands. Suppose there is a new ligand X, whose SMILES string is C#CCl. Use RDKit to generate a 64-bit fingerprint of ligand X, and then merge it with the experimental conditions into the feature matrix. For example, assuming that the experimental conditions of X are temperature 320℃ and HCL ratio 1.10, after inputting its feature vector into the model, the predicted conversion rate output by the model increases by 20%. This result can help determine whether ligand X has a good catalytic effect under the reaction conditions and whether it is worth further verification in the experiment.

[0071] More than 8,000 ligands were collected from https: / / www.chemsrc.com / and converted into SMLILES strings. The HCl ratio was set to 1.15 and the reaction temperature was set to 180 degrees Celsius. About 100 ligands with excellent prediction results were obtained. After removing factors such as toxicity, danger, and price, 2 ligands were selected from the remaining ligands for verification.

[0072] Step 8: Prepare the catalyst

[0073] The ligand predicted to have good effect under the given reaction conditions in step 7 was used for impregnation method to prepare a catalyst with a central metal content of 1% Ru. The catalyst performance was tested experimentally under the same reaction conditions. At the same time, a catalyst with a central metal content of 1% Ru without adding ligand was prepared under the same conditions as a control to determine the improvement rate after modification.

[0074] Step 9: Experimental Testing

[0075] Experimental tests were conducted on the catalyst prepared with the ligand (CAS No.: 3109-63-5) which predicted a 16% increase in conversion rate, and a blank control. The conclusion was that the conversion rate of the catalyst with added ligand was 88%, while that of the catalyst without added ligand was 63%, with an increase of 25%, which is a good effect.

[0076] Experimental tests were conducted on the catalyst prepared with the ligand (CAS No.: 65039-09-0) which predicted a 40% increase in conversion rate, and a blank control. The conclusion was that the conversion rate of the catalyst with added ligand was 95%, while that of the catalyst without ligand was 63%, with an increase of 32%, which is a good effect.

[0077] The present invention is not limited to the specific technical solutions described in the above embodiments, and all technical solutions formed by equivalent replacement are within the protection scope required by the present invention.

Claims

1. A method for predicting the quality of acetylene hydrochlorination catalyst ligands based on random forest algorithm, characterized in that: The method comprises the following steps: (1) collecting and extracting data from literature as a data set for training a model; catalyst ligands, reaction conditions and ligand properties are used to train the model; the reaction conditions are temperature and HCl ratio; (2) Extract the composite SMILES string of the ligand molecule and construct a tensor matrix, that is, extract the 64-bit ECFP fingerprint of the ligand and separate it bit by bit and then reassemble it to construct a tensor matrix; convert the temperature and HCl ratio experimental conditions into vectors and add them to the feature matrix; the complete feature matrix contains not only the molecular structure information of the ligand, but also different experimental conditions; (3) Input the complete feature matrix into the model based on the random forest algorithm for scoring training; each column in the tensor matrix is ​​a feature, and the importance attribute of each feature to the model performance is set. The importance parameter of each feature is obtained by preliminarily training the random forest model using database data, and the parameters are arranged in descending order. The features with lower importance are removed in proportion to reduce the dimension and prevent overfitting. (4) The simplified feature matrix is ​​input into the random forest for model scoring training and the SMILES string of the unknown performance ligand is input to obtain the result of the ligand quality.

2. The method for predicting the quality of acetylene hydrochlorination catalyst ligands based on random forest algorithm according to claim 1, characterized in that: The catalyst ligands, reaction conditions and ligand properties are used to train the model: each atom and chemical bond in the SMILES string is a word, and the corpus is naturally composed by collecting many compounds. The ECFP (Extended Connectivity Fingerprint) technology is used to collect information, combined with the reaction conditions, and the scoring weights are trained.

3. The method for predicting the quality of acetylene hydrochlorination catalyst ligands based on random forest algorithm according to claim 1, characterized in that: The steps are as follows The ligand complex SMILES string and reaction conditions are embedded in the model, a tensor matrix is ​​constructed, and the SMILES string of the ligand is embedded into the tensor using the trained LDT model to output the score.

4. The method for predicting the quality of acetylene hydrochlorination catalyst ligands based on random forest algorithm according to claim 3, characterized in that: The method comprises the following steps: Step 1: Establish a data set and collect information on known catalyst ligands from the literature. The molecular structure of the ligand and the corresponding composite SMILES string, temperature, HCl ratio, and conversion rate are recorded in the database; Step 2: Convert the molecular structure of the ligand into an ECFP fingerprint The composite SMILES string of each ligand is converted into a 64-bit binary vector called "ECFP fingerprint". For the SMILES string of known ligands, a search method is used to map each functional group into a molecular fingerprint composed of 0 and 1 from the RDKit open source library, and then combine them into a 64-bit molecular fingerprint. Different sequences of 0 and 1 represent different functional groups, thus reflecting the influence of different ligand structures. Step 3: Construct the feature tensor matrix Each ligand collected has its own 64-bit molecular fingerprint. The fingerprint data of all ligands are integrated into a tensor matrix. Each row of the matrix represents a ligand and each column is a feature bit. The experimental conditions temperature and HCl ratio were converted into vectors and added to the feature matrix; Step 4: Build and tune the model architecture Input the complete feature matrix into the random forest model for preliminary training. The random forest model automatically calculates the importance of each feature. These scores indicate the contribution of each feature to the prediction. By adjusting the number of trees, we tried the number of trees (5, 10, 15, 20), the minimum number of samples for leaf merging (1, 2, 3), and the minimum gain for splitting (0, 0.05, 0.1). After model training, we found that the contribution of some features was extremely low. Among the 64-bit fingerprints, 7 bits had low contributions. We removed these unimportant features and only retained the features with greater influence. This feature screening can effectively reduce noise and prevent model overfitting. Step 5: Model Evaluation The simplified feature matrix is ​​input into the random forest model and training continues. The target variable is the conversion rate. Through model training, the model gradually learns the relationship between ligand structure, experimental conditions and conversion rate. After training, the model was evaluated using the validation set; assuming that the predicted conversion rate of ligand A was 78%, while the actual conversion rate was 80%, with an error of 2%; Through model training, the parameters were determined as follows: number of trees: 10, minimum number of samples for leaf merging: 1, minimum gain for splitting: 0.

05. After training, the model was evaluated using the validation set. Finally, the probability that the difference between the predicted conversion rate and the actual conversion rate of different ligands was less than 5% reached more than 50%; Step 6: Predict the conversion rate of unknown ligands After training, the model can be used to predict the conversion rate of a new ligand, ligand X; RDKit is used to generate a 64-bit fingerprint of ligand X, which is then merged into the feature matrix with the corresponding experimental conditions; after the temperature and HCl ratio of the experimental conditions of X are input into the model, the model outputs the predicted conversion rate improvement rate; this output result can help determine whether ligand X has a good catalytic effect under the reaction conditions.

5. The method for predicting the quality of acetylene hydrochlorination catalyst ligands based on the random forest algorithm according to claim 1, characterized in that: Screening out ligands As a modifier for acetylene hydrochlorination catalyst.

6. A computer-readable storage medium, characterized in that: The storage medium has a computer program, and the computer program can run the method for predicting the quality of acetylene hydrochlorination reaction catalyst ligand based on random forest algorithm as described in claim 1.

7. A prediction device for the quality of acetylene hydrochlorination catalyst ligands based on random forest algorithm, characterized in that: The device is equipped with the computer-readable storage medium according to claim 6.

Images