Method for constructing analysis prediction model of polar monomer regioselectivity in copolymerization of ethylene with polar monomer catalyzed by α-diimine pd(ii) complex
By constructing a multiple linear regression model of the quantum chemical parameters of polar monomers and the energy difference of the transition state, the problem of predicting the regioselectivity of polar monomer insertion in existing technologies is solved, and efficient screening and accurate prediction of copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- DALIAN UNIV OF TECH
- Filing Date
- 2024-03-22
- Publication Date
- 2026-07-07
AI Technical Summary
The existing technology has not studied the quantitative relationship between the substituents of polar monomers and their insertion regioselectivity, which makes it difficult to accurately predict the copolymer structure in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes, increasing the workload and time cost of experimental and theoretical research.
By constructing a model based on DFT calculation and multiple linear regression, the relationship between the quantum chemical parameters of polar monomers and the energy difference of the transition state is determined. A regional selectivity analysis and prediction model for the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complex is established. The stepwise regression method is used to retain parameters with strong correlations and construct a multiple linear regression model.
It enables accurate prediction of the regioselectivity of polar monomers, reduces the workload of experimental and theoretical research, shortens the research and development cycle, and improves the efficiency and accuracy of catalyst screening.
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Abstract
Description
Technical Field
[0001] This invention belongs to the field of polymer chemical synthesis technology, and relates to a method for constructing a predictive model for the regioselectivity of polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes. Background Technology
[0002] In studies of α-diimine Pd(II) complex-catalyzed copolymerization of ethylene with polar monomers, the regioselectivity of polar monomer insertion is crucial to the properties of the resulting product. It regulates subsequent chain walk, thereby controlling the polymerization process and the structure of the copolymer. Therefore, accurate prediction of polar monomer regioselectivity is of great significance for reducing the workload of theoretical and experimental research, shortening the development cycle, and achieving efficient screening of polymerization catalysts.
[0003] Currently, in the field of metal catalyst-catalyzed polymerization, quantitative prediction models mainly focus on the stereoselectivity of catalysts. For example, Luo et al. used Sterimol descriptors and multiple linear regression models to study the stereoselectivity of 2-vinylpyridine (2VP) catalyzed by 21 yttrium catalysts. By screening the obtained descriptors, they effectively evaluated the factors affecting stereoselectivity [see: Zhao Y, Lu H, Luo G, et al. Origin of stereoselectivity and multidimensional quantitative analysis of ligand effects on yttrium-catalyzed polymerization of 2-vinylpyridine[J]. Catal. Sci. Technol. 2019, 9(22): 6227-6233. DOI: 10.1039 / C9CY01670H.]. In 2020, Sukbok et al. designed catalysts using a linear regression model, achieving amination of nitro groups through sp2 helication or sp3 C-H activation, and prepared more than 40 novel catalysts for screening. This enabled the identification and screening of catalysts based on quantitative analysis. [See: Hwang Y, Jung H, Lee E, et al. Quantitative analysis on two-point ligand modulation of iridium catalysts for chemodivergent CH amidation[J]. Journal of the American Chemical Society, 2020, 142: 8880-8889.]
[0004] Currently, there are no research examples investigating the quantitative relationship between polar monomer substituents and their regioselectivity. Therefore, combining DFT calculations with multiple linear regression to establish a stable and efficient structure-activity relationship model can provide a theoretical basis and predictive model for predicting regioselectivity, saving significant experimental and time costs and promoting the rapid development of the field of catalytic polymerization. Based on this, this invention studies an analytical model that can accurately predict the regioselectivity of polar monomers catalyzed by α-diimine Pd(II) complexes. Summary of the Invention
[0005] The purpose of this invention is to provide a technical solution capable of accurately analyzing and predicting the regioselectivity of α-diimine Pd(II) complexes in the copolymerization of ethylene with polar monomers. To achieve the above objective, this invention provides the following technical solution:
[0006] A method for constructing a predictive model for the regioselectivity of polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes includes:
[0007] The reaction energy difference ΔG between the 1,2-insertion mode transition state and the 2,1-insertion mode transition state of each polar monomer was constructed, and the quantum chemical parameter values of each polar monomer were obtained. The relationship between the reaction energy difference ΔG and the quantum chemical parameter values of each polar monomer was established by stepwise regression modeling method. Quantum chemical parameter values with strong correlation to the reaction energy difference ΔG were retained. A multiple linear regression model of the quantum chemical parameter values of each polar monomer with respect to the reaction energy difference ΔG between the 1,2-insertion mode transition state and the 2,1-insertion mode transition state of each polar monomer was constructed, which is the regioselectivity analysis and prediction model in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complex.
[0008] Specifically as follows:
[0009] Transition state models were constructed for catalysts with different insertion modes for each polar monomer, namely 1,2-insertion and 2,1-insertion. The transition state energies under the two insertion modes were determined. Taking the transition state energy of each polar monomer with 1,2-insertion as zero, the energy difference of the transition state energy of each polar monomer with 2,1-insertion relative to zero was determined, namely the reaction energy difference ΔG between the transition state of each polar monomer with 1,2-insertion mode and the transition state of 2,1-insertion mode.
[0010] Among the obtained quantum chemical parameter values for each polar monomer, the quantum chemical parameters include the embedding volume of the polar monomer, %V. bur The highest occupied molecular orbital energy, E HOMO The lowest unoccupied molecular orbital energy, E LUMO The NBO charge of the C1 atom in the polar monomer vinyl group, NBOC1 The NBO charge of the C2 atom in the polar monomer vinyl group, NBO C2 NMR shifts of C1 atoms in polar monomer vinyl groups; NMR C1 NMR shifts of C2 atoms in polar monomer vinyl groups; NMR C2 ;
[0011] Based on the reaction energy difference ΔG of the transition states of different insertion modes of polar monomers under the catalysis of α-diimine Pd(II) complexes, and the quantum chemical parameter values of each polar monomer, a stepwise regression modeling method was adopted. During the modeling process, useless quantum chemical parameter values were eliminated and those with strong correlation to the dependent variable were retained. Finally, the quantum chemical parameters, including the embedding volume of the polar monomer and the NBO charge of the C1 atom of the vinyl group of the polar monomer, were determined to be used to construct a regioselectivity analysis and prediction model for polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes.
[0012] Furthermore, the regioselectivity prediction model for polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes is as follows:
[0013] ΔG pred =a1×%V bur +a2×NMR C1 +a3
[0014] In the formula, ΔG pred The transition state energy difference of each polar monomer inserted into the α-diimine Pd(II) complex using different insertion modes, as predicted by the multiple linear regression model, is expressed in kcal / mol; a1, a2, and a3 are coefficients.
[0015] Furthermore, a regioselectivity prediction model for polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes was employed. The encapsulation volume of the polar monomer and the NBO charge of the vinyl C1 atom were selected as descriptors to predict its regioselectivity. The prediction result ΔG was then used. pred The reaction energy difference ΔG between the 1,2-insertion mode transition state and the 2,1-insertion mode transition state of each polar monomer catalyzed by the α-diimine Pd(II) complex was fitted, and the correlation coefficient and root mean square error were recorded. When the correlation coefficient was greater than 0.8 and the root mean square error was less than 2.0 kcal / mol, we considered the model to have good predictive performance.
[0016] The technical solution provided by this invention enables accurate prediction of the regioselectivity of polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes. This is of great significance for reducing the workload of theoretical and experimental research, shortening the research and development cycle, and achieving efficient screening of polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes. Compared with the prior art, it has the following beneficial effects:
[0017] 1. The technical solution provided by this invention, through stepwise regression analysis, determines the relationship model between the transition state energy difference ΔG of each polar monomer inserting into the α-diimine Pd(II) complex using different insertion modes and the quantum chemical parameters of each polar monomer. This model can quickly and accurately predict the regioselectivity of polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes, which helps to screen polar monomers according to the expected regioselectivity under specific catalysts, saving a lot of manpower and resources.
[0018] Specifically, the technical solution provided by this invention uses quantum chemistry to determine the relationship model between the transition state energy difference ΔG of each polar monomer inserting into the α-diimine Pd(II) complex using different insertion modes and the quantum chemical parameters of each polar monomer. This model can predict the insertion region selectivity of unknown polar monomers, thereby achieving preliminary screening of polar monomers, reducing the workload of artificial synthesis and testing, shortening the research and development cycle, and reducing research and development costs.
[0019] 2. In the preferred embodiment of this invention, the process of constructing a predictive model for the regioselectivity of polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes utilizes comprehensive data. Multiple linear regression analysis helps to clarify the influencing factors of regioselectivity of polar monomers, which is beneficial for the rational design of polar monomer molecules in the next step. The influence of different vinyl substituents on the polar monomers on the model is considered, as are the effects of various types of quantum chemical parameters. Attached Figure Description
[0020] Figure 1 This is a flowchart illustrating a method for constructing a predictive model for the regioselectivity of polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes, as provided in an embodiment of the present invention.
[0021] Figure 2 This is a schematic diagram of the stepwise regression model construction in Embodiment 1 of the present invention.
[0022] Figure 3 This is a correlation curve diagram of the training set (a) and the validation set (b) in Embodiment 1 of the present invention. Detailed Implementation
[0023] The present invention will be further illustrated below with reference to specific embodiments. These embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Simple substitutions or modifications made to the present invention by those skilled in the art are all within the scope of the technical solutions protected by the present invention.
[0024] Example 1: Predictive Model for Regioselectivity Analysis of Polar Monomers in the Copolymerization of Ethylene and Polar Monomers Catalyzed by α-Diimine Pd(II) Complexes
[0025] Step 1: By changing the substituents of vinyl polar monomers, a total of 23 polar monomers, including acrylone monomers, acrylamide monomers, and acrylate monomers, were obtained. These were used to construct a predictive model for the regioselectivity of polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes. The molecular structure was optimized using the B3LYP / 6-31G(d) theoretical method in Gaussian 16 software, and the quantum chemical parameters of the polar monomers, including the embedding volume (%V), were calculated. bur The highest occupied molecular orbital energy (E) HOMO ), lowest unoccupied molecular orbital energy (E) LUMO ), NBO charge of the C1 atom of the polar monomer vinyl group (NBO) C1 The NBO charge of the C2 atom of the polar monomer vinyl group (NBO) C2 ), NMR shifts of C1 atoms in polar monomer vinyl groups (NMR) C1 ), NMR shift of C2 atom of polar monomer vinyl group (NMR) C2 ), detailed data are shown in Table 1;
[0026] Step 2: Geometric structure optimization was performed on the α-diimine Pd(II) complex. In this embodiment, the α-diimine Pd(II) complex is an α-diimine Pd(II) complex with a dibenzo[a]benzo[B] alkyl group backbone and an axial pentanoyl (Ipty) substituent. A propyl group was used as the alkyl terminus to simulate the structure of a complex after the insertion of one molecule of ethylene. Geometric structure optimization and frequency calculations were performed using B3LYP density functional theory. The non-metallic atoms (C, H, O, N, P, S) were represented by the 6-31G(d) basis set, and the Pd metal atom was represented by the LANL2DZ pseudopotential basis set.
[0027] Step 3: Constructing the transition state structure for the polar monomer insertion during the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes. Geometric structure optimization and frequency calculations were performed using B3LYP density functional theory. The non-metallic atoms (C, H, O, N, P, S) were represented by the 6-31G(d) basis set, and the Pd metallic atom was represented by the LANL2DZ pseudopotential basis set. Based on the optimized structure using the B3LYP method, a higher-level basis set was used for single-point energy calculations. Considering the solvation effect, toluene (dielectric constant ε = 2.37) was used as the solvent, and the solvation model was SMD. Larger 6-311G(d,p) basis sets and SDD pseudopotential basis sets were used for non-metallic atoms (C, H, O, N, P, S) and metallic Pd atoms, respectively. The transition state Gibbs free energies shown in this embodiment all include gas-phase free energy corrections.
[0028] Step 4: Based on the transition state energy of polar monomer insertion during the copolymerization of ethylene and polar monomers catalyzed by the α-diimine Pd(II) complex constructed in Step 3, calculate the transition state energy difference ΔG between the two insertion modes as the response variable. Detailed data are shown in Table 1.
[0029] Table 1. Data on the 23 polar monomer structures used in modeling, descriptor data, and corresponding energy differences ΔG (kcal / mol)
[0030]
[0031]
[0032] Step 5: Based on the transition state energy difference ΔG of these 23 polar monomers inserted into the α-diimine Pd(II) complex using two different insertion modes, and the quantum chemical parameter values of these 23 polar monomers, the descriptor is normalized, and a multiple linear stepwise regression model is constructed. Figure 2 The constructed model is as shown in equation (1);
[0033] ΔG Predict =1.58 + 9.32 × 1 (%V) bur –4.49X6 (NBO) C1 (1)
[0034] Step Six: Calculate the reaction energy difference ΔG between the 1,2-insertion mode transition state and the 2,1-insertion mode transition state of each polar monomer under the catalysis of the α-diimine Pd(II) complex, as predicted by the multiple linear regression model. pred Linear fitting was performed on the reaction energy difference ΔG between the 1,2-insertion mode transition state and the 2,1-insertion mode transition state of each polar monomer catalyzed by the α-diimine Pd(II) complex catalyzed by DFT, and the linear fit was obtained. Figure 3The fitting correlation coefficient was 0.89, and the root mean square error was 0.77 kcal / mol.
[0035] Step 7: Collect published literature on the structures of polar monomers catalyzed by α-diimine Pd(II) complexes in the copolymerization of ethylene with polar monomers (Comprehensive Picture of Functionalized Vinyl Monomers in ChainWalking Polymerization. Macromolecules 2020, 53, 8858-8866), a total of 9 polar monomer structures, as a validation set for the model;
[0036] Step 8: Using the methods in Steps 1 to 3, obtain the quantum chemical parameters of the nine polar monomers in Step 7 and the reaction energy difference ΔG between the 1,2-insertion mode transition state and the 2,1-insertion mode transition state of each polar monomer under the catalysis of the α-diimine Pd(II) complex.
[0037] Table 2. Polar monomer descriptor data and corresponding energy differences ΔG (kcal / mol)
[0038]
[0039] Step 9: Substitute the quantum chemical parameters of these 9 polar monomers into the trained multivariate linear model (1) to obtain the predicted energy difference of the transition state of each polar monomer inserting into the α-diimine Pd(II) complex using different insertion modes. Then, use the reaction energy difference ΔG between the 1,2-insertion mode transition state and the 2,1-insertion mode transition state of each polar monomer under the catalysis of the α-diimine Pd(II) complex catalyzed by the multivariate linear regression model to obtain the predicted energy difference ΔG. pred Linear fitting was performed on the reaction energy difference ΔG between the 1,2-insertion mode transition state and the 2,1-insertion mode transition state of each polar monomer catalyzed by the α-diimine Pd(II) complex catalyzed by DFT. The validation set correlation coefficient was 0.88, and the root mean square error was 1.32 kcal / mol. Figure 3 This indicates that the model has high prediction accuracy.
[0040] The descriptions of the exemplary embodiments presented above are merely illustrative of the technical solutions of the present invention and are not intended to be exhaustive or to limit the invention to the precise forms described. Obviously, those skilled in the art can make many changes and variations based on the above teachings. The exemplary embodiments were chosen and described to explain the specific principles of the invention and its practical applications, thereby enabling other those skilled in the art to understand, implement, and utilize the various exemplary embodiments of the invention and their various alternatives and modifications. The scope of protection of the present invention is intended to be defined by the appended claims and their equivalents.
Claims
1. A method for constructing a predictive model for the regioselectivity of polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes, characterized in that, Construct the reaction energy difference Δ between the 1,2-insertion mode transition state and the 2,1-insertion mode transition state of each polar monomer. G The quantum chemical parameters of each polar monomer were obtained, based on the reaction energy difference Δ between the transition states of each polar monomer under different insertion modes catalyzed by the α-diimine Pd(II) complex. G The quantum chemical parameters of each polar monomer were determined using a stepwise regression modeling method. During the modeling process, useless quantum chemical parameter values were eliminated to determine the quantum chemical parameters, including the embedding volume %V of the polar monomer. bur NBO charge of the C1 atom of the polar monomer vinyl group C1 To construct a regioselectivity prediction model for polar monomers in the copolymerization of ethylene with polar monomers catalyzed by α-diimine Pd(II) complexes, and to construct the reaction energy difference Δ between the 1,2-insertion mode transition state and the 2,1-insertion mode transition state of each polar monomer with respect to the determined quantum chemical parameter values of each polar monomer. G The multiple linear regression model is a predictive model for the regioselectivity of polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes.
2. The construction method according to claim 1, characterized in that, Transition state models were constructed for catalysts with both 1,2-insertion and 2,1-insertion modes for each polar monomer. The transition state energies under both insertion modes were determined. Taking the transition state energy of the 1,2-insertion mode as zero, the energy difference between the transition state energy of the 2,1-insertion mode and the zero point was determined; that is, the reaction energy difference Δ between the 1,2-insertion mode transition state and the 2,1-insertion mode transition state of each polar monomer. G .
3. The construction method according to claim 1, characterized in that, Among the obtained quantum chemical parameter values for each polar monomer, the quantum chemical parameters include the embedding volume of the polar monomer, %V. bur The highest occupied molecular orbital energy, E HOMO The lowest unoccupied molecular orbital energy, E LUMO The NBO charge of the C1 atom in the polar monomer vinyl group, NBO C1 The NBO charge of the C2 atom in the polar monomer vinyl group, NBO C2 NMR shifts of C1 atoms in polar monomer vinyl groups; NMR C1 NMR shifts of C2 atoms in polar monomer vinyl groups; NMR C2 .
4. The construction method according to claim 1, characterized in that, The predictive model for the regioselectivity of polar monomers in the copolymerization of ethylene with polar monomers catalyzed by α-diimine Pd(II) complexes is as follows: G pred = a1 × %V bur + a2 × NMR C1 + a3 In the formula, G pred The transition state energy difference of each polar monomer inserted into the α-diimine Pd(II) complex using different insertion modes, as predicted by the multiple linear regression model, is expressed in kcal / mol; a1, a2, and a3 are coefficients.
5. The construction method according to claim 1, characterized in that, A regioselectivity prediction model for polar monomers in the copolymerization of ethylene and polar monomers catalyzed by α-diimine Pd(II) complexes was used. The encapsulation volume of the polar monomer and the NBO charge of the vinyl C1 atom were selected as descriptors to predict its regioselectivity. The prediction results were then analyzed. G pred The reaction energy difference Δ between the 1,2-insertion mode transition state and the 2,1-insertion mode transition state of each polar monomer under the catalysis of α-diimine Pd(II) complex. G Perform a fitting test and record the correlation coefficient and root mean square error. When the correlation coefficient is greater than 0.8 and the root mean square error is less than 2.0 kcal / mol, the model is considered to have a good prediction effect.