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CN110222863A - A kind of dielectric dielectric strength prediction technique, device and equipment - Google Patents

A kind of dielectric dielectric strength prediction technique, device and equipment Download PDF

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Publication number
CN110222863A
CN110222863A CN201910280929.7A CN201910280929A CN110222863A CN 110222863 A CN110222863 A CN 110222863A CN 201910280929 A CN201910280929 A CN 201910280929A CN 110222863 A CN110222863 A CN 110222863A
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dielectric strength
molecular
insulating medium
surface area
dielectric
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CN110222863B (en
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周永言
李丽
唐念
樊小鹏
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Guangdong Power Grid Co Ltd
Electric Power Research Institute of Guangdong Power Grid Co Ltd
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Electric Power Research Institute of Guangdong Power Grid Co Ltd
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Abstract

This application discloses a kind of dielectric dielectric strength prediction techniques, device and equipment, using topological surface area VSA instead of surface area square Variable Factors parameter in the prior art, topological surface area VSA is the LabuteASA descriptor that RDKit is calculated, it can be calculated according only to compound 2D structure, have the characteristics that quick and easy, and the surface area square of physical significance than in the prior art is definitely, simultaneously, avoid using electronegativity in the prior art and polarizability product this, but it is substituted using molecule frontier orbit HOMO energy and molecular frontier orbit LOMO energy, acquisition modes are simpler quickly, explicit physical meaning, therefore, it is multiple that dielectric dielectric strength prediction technique provided by the present application solves existing calculating dielectric dielectric strength mode The miscellaneous and indefinite technical problem of Variable Factors physical significance.

Description

Method, device and equipment for predicting dielectric strength of insulating medium
Technical Field
The application relates to the technical field of dielectric strength prediction, in particular to a method, a device and equipment for dielectric strength of an insulating medium.
Background
Electrons of an electrically insulating material are bound to atoms and molecules and therefore have a high resistance to electric current, but when breakdown occurs, the electric field causes the previously bound electrons to be released, and if the strength of the electric field is large enough, the free electrons collide with electrically neutral atoms or molecules and release other electrons, which release is accelerated. In order to measure how strong a voltage an insulating material can withstand before it breaks down, a dielectric strength is introduced. Dielectric strength refers to the highest voltage that a unit thickness of insulating material can withstand before breakdown, i.e., the maximum of the electric field strength. Thus, the dielectric strength is the ratio of the breakdown voltage to the thickness of the material, i.e., E ═ V/d.
In general, dielectric strength is proportional to the pressure of the gas. The study of Wada 2016 et al examined n-C4F8The breakdown voltage (dielectric strength) measured under different pressure conditions confirms that the dielectric strength is in direct proportion to the gas pressure.
The absolute value of the dielectric strength is related to the distance between the electrodes to be tested, in addition to the gas pressure and the shape of the electrodes. For the dielectric strength to be comparable, the relative dielectric strength is generally adopted: the relative dielectric strength of a gas is expressed in terms of the ratio of the dielectric strength of an insulating gas, typically nitrogen or sulfur hexafluoride, to that of a reference gas. Under the same test conditions, this ratio is independent of pressure, electrode distance and can therefore be used to compare different insulation properties. In order to discover and design new insulating gases, quantitative structure-property relationship (QSPR) equations were developed to explore the structure-node strength relationship and to predict the dielectric strength of new compounds. For example, Yu XiaoJuan et al examined QSPR for dielectric strength with a relative dielectric strength of 43 compounds, and the dielectric strength was calculated by the formula:
in the formula,υ、II is a GIPF descriptor based on a density functional calculation, described in Murray 1998, AsFor the molecular surface area, an equivalent plane of an electron density of 0.001 as proposed by Bader et al is used as the molecular surface, and the area of the equivalent plane isThe molecular surface area of (a).
V andare defined as follows:
wherein,andthe mean square deviations of the positive and negative electrostatic potentials on the surface of the molecule, respectively.
Π is the absolute average difference in surface electrostatic potential, defined as:
wherein,is the average value of the electrostatic potential, V (r), of the surface of the moleculei) Is the electrostatic potential at the ith point on the surface, α and χ are the molecular polarizability and electronegativity, respectively, calculated based on the quantum chemical density functional, where electronegativity is defined according to Koopmans theory as follows:
in the Yu XiaoJuan et al study, all GIPF parameters as well as polarizability and electronegativity were calculated using the Quantum chemistry computing software Gaussian 09 at the theoretical level of M06-2X/6-31+ + g (d.p). According to the report of the YuXiaoJuan 2017 research, equationThe predicted linear regression coefficient R2 was 0.985 and the standard deviation σ between the predicted value and the experimental value was 0.08 for 43 data.
Although the linear regression coefficient and standard deviation of the prediction model studied in Yu XiaoJuan 2017 are excellent, the equationBut rather complicated, variable factors have ambiguous physical meanings such as the product of surface area squared, electronegativity, and polarizability. Furthermore, the square surface area, the product of electronegativity and polarizability does not explain classical findings, such as the very strong correlation of dielectric strength with molecular weight, which is found by Vijh et al, and should be proportional.
Disclosure of Invention
The embodiment of the application provides a method, a device and equipment for predicting dielectric strength of an insulating medium, which are used for solving the technical problems of complex mode for calculating the dielectric strength of the insulating medium and ambiguous physical meaning of variable factors in the prior art.
In view of the above, the first aspect of the present application provides a method for predicting dielectric strength of an insulating medium, comprising the following steps:
101. obtaining variable factor parameters of a dielectric strength compound to be predicted, wherein the variable factor parameters comprise: topological molecular surface area VSA, molecular front line orbital HOMO energy EHOMOLOMO energy E of molecular front-line orbitLUMOAbsolute average difference pi of surface electrostatic potential, mean square error of positive electrostatic potential on molecular surfaceAnd the mean square error of the negative potential on the surface of the molecule
102. Substituting the variable factor parameters into a dielectric strength prediction model to calculate the dielectric strength of the dielectric strength compound to be predicted, wherein the dielectric strength prediction model is as follows:
wherein,
preferably, the variable factor parameters further include molecular weight MolWt;
accordingly, the dielectric strength prediction model is:
preferably, the topological molecular surface area VSA and the molecular weight MolWt are calculated according to the RDKit descriptor module.
Preferably, the molecular front-line orbital HOMO energy EHOMOLOMO energy E of front-line orbit of moleculeLUMOCalculated according to the density functional method under APFD/6-311+ g (2d, p).
Preferably, after step 102, the method further comprises:
103. and outputting the calculation result of the dielectric strength and displaying the calculation result according to a preset format.
The second aspect of the present application further provides an insulating medium dielectric strength predicting apparatus, including the following modules:
an obtaining module, configured to obtain variable factor parameters of a dielectric strength compound to be predicted, where the variable factor parameters include: topological molecular surface area VSA, molecular front line orbital HOMO energy EHOMOLOMO energy E of molecular front-line orbitLUMOAbsolute average difference pi of surface electrostatic potential, mean square error of positive electrostatic potential on molecular surfaceAnd the mean square error of the negative potential on the surface of the molecule
A calculating module, configured to substitute the variable factor parameter into a dielectric strength prediction model to calculate the dielectric strength of the dielectric strength compound to be predicted, where the dielectric strength prediction model is:
wherein,
preferably, the obtaining module is further configured to:
obtaining molecular weight MolWt;
accordingly, the dielectric strength prediction model is:
the third aspect of the present application also provides an insulating medium dielectric strength prediction apparatus comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the method of predicting dielectric strength of an insulating medium according to the first aspect according to instructions in the program code.
The fourth aspect of the present application also provides a computer-readable storage medium for storing program code for executing the method for predicting dielectric strength of an insulating medium according to the first aspect.
The fifth aspect of the present application also provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of predicting dielectric strength of an insulating medium according to the first aspect.
According to the technical scheme, the embodiment of the application has the following advantages:
the application provides an insulating medium dielectric strength prediction method, which comprises the following steps: 101. 101, obtaining the dielectric to be predictedVariable factor parameters for the strength compound, the variable factor parameters comprising: topological molecular surface area VSA, molecular front line orbital HOMO energy EHOMOLOMO energy E of molecular front-line orbitLUMOAbsolute average difference pi of surface electrostatic potential, mean square error of positive electrostatic potential on molecular surfaceAnd the mean square error of the negative potential on the surface of the molecule102. Substituting the variable factor parameters into a dielectric strength prediction model to calculate the dielectric strength of the dielectric strength compound to be predicted, wherein the dielectric strength prediction model is as follows:
wherein,according to the method for predicting the dielectric strength of the insulating medium, the topological surface area VSA is adopted to replace surface area square variable factor parameters in the prior art, the topological surface area VSA is a LabuteASA descriptor calculated by RDKit, calculation can be performed only according to a compound 2D structure, the method has the advantages of being fast and simple, the physical significance is more definite than the surface area square in the prior art, meanwhile, the product of electronegativity and the polarizability in the prior art is avoided, the HOMO energy of the molecular front line orbit and the LOMO energy of the molecular front line orbit are adopted to replace the HOMO energy, the obtaining mode is simpler and faster, and the physical significance is definite, so that the technical problems that the existing method for predicting the dielectric strength of the insulating medium is complex in dielectric strength calculating mode and the physical significance of the variable factors is unclear are solved.
Drawings
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a method for predicting dielectric strength of an insulating medium provided herein;
FIG. 2 is a schematic flow chart diagram illustrating another embodiment of a method for predicting dielectric strength of an insulating medium provided herein;
fig. 3 is a schematic structural diagram of an embodiment of an insulating dielectric strength prediction apparatus provided in the present application.
Detailed Description
In order to make the technical solutions of the present application better understood by those skilled in the art, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments that can be derived by a person skilled in the art from the embodiments given in the present application without making any creative effort shall fall within the protection scope of the present application.
For easy understanding, referring to fig. 1, an embodiment of a method for predicting dielectric strength of an insulating medium provided by the present application includes the following steps:
step 101, obtaining variable factor parameters of a dielectric strength compound to be predicted, wherein the variable factor parameters comprise: topological molecular surface area VSA, molecular front line orbital HOMO energy EHOMOLOMO energy E of molecular front-line orbitLUMOAbsolute average difference pi of surface electrostatic potential, mean square error of positive electrostatic potential on molecular surfaceAnd the mean square error of the negative potential on the surface of the molecule
It should be noted that, in the embodiment of the present application, firstly, the topological molecular surface area VSA and the molecular front-line orbital HOMO energy E of the compound to be predicted for dielectric strength need to be obtainedHOMOLOMO energy E of molecular front-line orbitLUMOAbsolute average difference pi of surface electrostatic potential, average variance of positive electrostatic potential on molecular surfaceAnd the mean square error of the negative potential on the surface of the moleculeThe variable factor parameters, the molecular structure of the dielectric strength compound to be predicted can be represented by SMILES codes, the topological molecular surface area of the dielectric strength compound to be predicted can be calculated by a descriptor module of RDkit according to the molecular structure of the dielectric strength compound to be predicted, and the other variable factor parameters can be calculated and obtained under the theoretical level of density functional theory APFD/6-311+ g (2d, p) according to Gaussian 09 software.
Step 102, substituting the variable factor parameters into a dielectric strength prediction model to calculate the dielectric strength of the dielectric strength compound to be predicted, wherein the dielectric strength prediction model is as follows:
wherein,
it should be noted that the dielectric strength prediction model for calculating the dielectric strength compound to be predicted provided in the embodiments of the present application is:
after all the variable factor parameters in the dielectric strength prediction model are acquired in step 101, they are substituted into a multivariate linear equation:
calculating the dielectric strength of the dielectric strength compound to be predicted.
According to the method for predicting the dielectric strength of the insulating medium, the topological surface area VSA is adopted to replace surface area square variable factor parameters in the prior art, the topological surface area VSA is a LabuteASA descriptor calculated by RDKit, the calculation can be carried out only according to a compound 2D structure, the method has the advantages of being fast and simple, the physical significance is more definite than the surface area square in the prior art, meanwhile, the product of electronegativity and the polarizability in the prior art is avoided, the HOMO energy of the molecular front line orbit and the LOMO energy of the molecular front line orbit are adopted to replace the HOMO energy, the obtaining mode is simpler and faster, and the physical significance is definite, so that the method for predicting the dielectric strength of the insulating medium solves the technical problems that the existing method for calculating the dielectric strength of the insulating medium is complex and the physical significance of the variable factors is unclear.
For easy understanding, referring to fig. 2, another embodiment of a method for predicting dielectric strength of an insulating medium provided by the present application includes:
step 201, obtaining variable factor parameters of a dielectric strength compound to be predicted, wherein the variable factor parameters comprise: molecular weight MolWt, topological molecular surface area VSA, and molecular front-line orbital HOMO energy EHOMOLOMO energy E of molecular front-line orbitLUMOAbsolute average difference pi of surface electrostatic potential, mean square error of positive electrostatic potential on molecular surfaceAnd on the surface of the moleculeMean square error of potential
Further, the topological molecular surface area VSA and the molecular weight MolWt were calculated according to the RDKit descriptor module.
Further, the molecular front-line orbital HOMO energy EHOMOLOMO energy E of front-line orbit of moleculeLUMOCalculated according to the density functional method under APFD/6-311+ g (2d, p).
Step 202, substituting the variable factor parameters into a dielectric strength prediction model to calculate the dielectric strength of the dielectric strength compound to be predicted, wherein the dielectric strength prediction model is as follows:
or
It should be noted that, in the embodiment of the present application, first, the molecular weight MolWt, the topological molecular surface area VSA, and the energy E of the front-end orbital HOMO of the dielectric strength compound to be predicted need to be obtainedHOMOLOMO energy E of molecular front-line orbitLUMOAbsolute average difference pi of surface electrostatic potential, mean square error of positive electrostatic potential on molecular surfaceAnd the mean square error of the negative potential on the surface of the moleculeThe molecular structure of the dielectric strength compound to be predicted can be represented by the SMILES code according to these variable factor parametersThe molecular structure of the dielectric strength compound to be predicted can be calculated by a descriptor module of the RDkit according to the topological molecular surface area and the molecular weight of the dielectric strength compound to be predicted, and other variable factor parameters can be calculated and obtained according to Gaussian 09 software under the theoretical level of density functional theory APFD/6-311+ g (2d, p).
Taking hydrogen molecules as an example, the structure of the hydrogen molecules is represented by SMILES as [ H ] [ H ], and the following codes are used for calculation to output the molecular weight MolWt and the topological surface area VSA:
from__future__import print_function
from rdkit import Chem
import rdkit
from rdkit.Chem import Descriptors
from rdkit import DataStructs
mol＝Chem.MolFromSmiles(“[H][H]”)
MW＝Descriptors.MolWt(mol)
VSA＝Descriptors.LabuteASA(mol)
print(round(MW,2),round(VSA,2))
performing structure optimization and frequency calculation based on a density functional theory on a compound with dielectric strength to be predicted, taking hydrogen molecules as an example, and performing the structure optimization and the frequency calculation on the compound with the dielectric strength to be predicted as follows:
％chk＝WBS01.chk
#P APFD/6-311+g(2d,p)Opt freq integral(ultrafinegrid)
Structure:WBS01 APFD/6-311+(2d,p)Opt freq
so that the energy of the molecular front orbitals HOMO and LUMO of the hydrogen molecule can be read.
Molecular surface electrostatic potential analysis was correlated with the GIPF parameter П, υ,the calculation method is as follows:
П is the absolute average difference in surface electrostatic potential, defined as follows:
is the average value of the electrostatic potential, V (r), of the surface of the moleculei) Is the electrostatic potential at the ith point on the surface, α and χ are the molecular polarizability and electronegativity, respectively, calculated based on the quantum chemical density functional, where electronegativity is defined according to Koopmans theory as follows:
is the sum of the variances of the positive and negative electrostatic potentials on the surface, defined as follows:
wherein,andrespectively, the value of positive electrostatic potential (V (r) on the surface of the moleculei) Electrostatic potential > 0) and negative electrostatic potential value (V (r)i) Electrostatic potential of < 0). Is ═ i
V is defined as follows:
according toAnda value v can be calculated that represents the degree of equilibrium between the positive and negative electrostatic potentials at the surface of the molecule.
It should also be noted thatSubstitution into
Another post-deformation dielectric strength prediction model can also be obtained:
will be provided withSubstitution into
Another post-deformation dielectric strength prediction model can also be obtained:
from the above dielectric strength prediction model, it can be known that dielectric strength is proportional to molecular weight or topological molecular surface area, to HOMO energy, and to electronegativity, which is consistent with current knowledge: the insulating gas should not be able to actively donate electrons at high voltages in the first place, and also needs to have the ability to scavenge free electrons generated at high voltages, low HOMO energy means no active donation of electrons, high electronegativity means the ability to adsorb free electrons, helping to prevent the formation of electron bursts and ultimately preventing breakdown from occurring. Therefore, the dielectric strength prediction model provided by the embodiment of the application has good prediction performance, is simpler, has stronger physical significance, and can be used for guiding the design and discovery of insulating gas molecules.
And 203, outputting the calculation result of the dielectric strength and displaying the calculation result according to a preset format.
It should be noted that after the dielectric strength of the compound to be predicted is calculated in step 202, the calculation result is displayed in a preset format.
In order to facilitate understanding of the dielectric strength prediction scheme provided in the embodiments of the present application, a specific application example of the dielectric strength prediction scheme provided in the embodiments of the present application is as follows.
The molecular weight (MolWt) and molecular surface area (LabuteASA) of the compound were first calculated from the plotted molecular structure (indicated by SMILES code) using the descriptor module of RDKit (Version 2017). The WBS01 compound was used as an example to illustrate how to calculate molecular weight and molecular surface area using RDKit. WBS01 is a hydrogen molecule, whose structure is denoted by SMILES as [ H ] [ H ], and whose output molecular weight versus surface area is calculated using the following code:
from_future_import print_function
from rdkit import Chem
import rdkit
from rdkit.Chem import Descriptors
from rdkit import DataStructs
mol＝Chem.MolFromSmiles(“[H][H]”)
MW＝Descriptors.MolWt(mol)
VSA＝Descriptors.LabuteASA(mol)
print(round(MW,2),round(VSA,2))
the molecular weight and surface area of the other compounds were calculated in the same manner, and the results are shown in Table 1.
TABLE 1
Each compound was optimized with the RDKit to generate 3D structures and MMFF94 force field and to generate Gaussian 09 input format, optimized and frequency calculated at APFD/6-311+ g (2D, p) theoretical level and confirmed the optimization results with no virtual frequency by frequency calculation, the input files for each structure are detailed below:
optimization and frequency calculation of compound WBS01
％chk＝WBS01.chk
#P APFD/6-311+g(2d,p)Opt freq integral(ultrafinegrid)
Structure:WBS01APFD/6-311+(2d,p)Opt freq
Optimization and frequency calculation of compound WBS02
％chk＝WBS02.chk
#P APFD/6-311+g(2d,p)Opt freq integral(ultrafinegrid)
Structure:WBS02APFD/6-311+(2d,p)Opt freq
Optimization and frequency calculation of compound WBS03
％chk＝WBS03.chk
#P APFD/6-311+g(2d,p)Opt freq integral(ultrafinegrid)
Structure:WBS03APFD/6-311+(2d,p)Opt freq
The optimization of the compounds WBS04 to WBS043 can be calculated similarly to the frequency calculation, which is not illustrated here.
Energy calculation of molecular front line orbitals HOMO and LUMO
Each calculation experiment of WBS 01-WBS 043 generates two output files: 1) log suffix output file; and 2) check document of chk suffix. The energy of the molecular orbitals HOMO and LUMO (a.u. units) was read from the log file and the results are shown in Table 1 as EHOMO and ELUMO, respectively.
Generating a formatted review document (fchk file)
The chk check document generated by each of the computational experiments WBS 01-WBS 043 was converted to a formatted fchk file using the formchk program of Gaussian 09. Chk file, for example, the following command is entered:
formchk WBS01.chk WBS01.fchk
after the calculation, a new formatted fchk file is generated: wbs01. fchk. Each compound will get a fchk file that is further used to generate electron density and electrostatic potential cube files. Cube file generation
An electron density and electrostatic potential cube file is generated by using the cube program of Gaussian 09. The cube takes fchk as an input file to generate a cube file.
The creation of the cube file was performed with a 24 computer core, and how to create electron density and electrostatic potential cube files from WBS01.fchk using compound WBS01 as an example.
(1) Generating an electronic density cube file, and typing the following commands:
cubegen 24density WBS01.fchk WBS01_dens.cube 80
and after the calculation is finished, generating an electron density cube file of the compound: cube and WBS01_ dens
(2) Generating an electrostatic potential cube file, and typing the following commands:
cubegen 24potential WBS01.fchk WBS01_esp.cube 80
and (3) after the calculation is finished, generating an electrostatic potential cube file of the compound: cube of WBS01_ esp
Two cube files, one electron density cube file, and one electrostatic potential cube file were obtained after each compound.
Molecular surface electrostatic potential analysis was correlated with the GIPF parameter П, υ,is calculated by
The values of the GIPF descriptors П, υ,the surface of the molecule was first defined before the calculation, Bader et al proposed an isosurface with an electron density of 0.001, which was generally accepted as the surface of the molecule, and the upper electrostatic potential of the isosurface was used to calculate П, v,in the GIPF descriptor calculation, the surface of the molecule, if not specified, refers to an isosurface with an electron density of 0.001.
П is the absolute average difference in surface electrostatic potential, defined as follows:
wherein,is the average value of the electrostatic potential, V (r), of the surface of the moleculei) Is the electrostatic potential at the ith point on the surface. The electron density can be read from the electron density cube file generated in example 5, and the electron density is 0.001 grid point electrostatic potential V (r)i) Can be read from the electrostatic potential cube file generated in example 5 and used for calculating the average value of the electrostatic potentialAnd finally the absolute average difference is calculated (П).
Is the sum of the variances of the positive and negative electrostatic potentials on the surface, defined as follows:
v is defined as follows:
according toA value v can be calculated that represents the degree of equilibrium between the positive and negative electrostatic potentials at the surface of the molecule.
The following calculation of the GIPF descriptor was performed according to the above definition using the python 3 language below, taking the WBS03 compound as an example, to illustrate how to perform surface electrostatic potential analysis from the electron density cube and the electrostatic potential cube of the compound and calculate П, ν,
by analogy, 43 molecules from examples 2.1-2.43 were calculated separately, resulting in a GIPF descriptor П,the values for v are shown in Table 1.
Calculating dielectric strength and evaluating statistical indexes:
using topological molecular surface area (VSA), GIPF parameters П,And front line track energy EHOMOAnd ELUMOFor independent variables (descriptors), the experimental dielectric strength Er (expt) was subjected to multiple linear regression for dependent variables to obtain the following dielectric strength prediction model (Er _ EQ 7):
the predicted values are shown in Er _ EQ7 cases of Table 2, and the predicted values are compared with the experimental values, and the linear regression coefficient R20.963, the root mean square difference RMSD is 0.131, and the Pearson correlation coefficient r is 0.982.
Replacing the putative molecular surface area VSA with molecular weight MolWt, the following dielectric strength prediction model (Er _ EQ8) was obtained:
the predicted values are shown in Er _ EQ8 cases of Table 2, and the predicted values are compared with the experimental values, and the linear regression coefficient R20.945, the root mean square difference RMSD is 0.162, and the Pearson correlation coefficient r is 0.972.
According to the linear regression coefficient and the root-mean-square difference obtained according to the predicted values of the two dielectric strength prediction models, the dielectric strength prediction method provided by the application has good prediction performance, is simpler, has stronger physical significance, and can be used for guiding the design and discovery of insulating gas molecules.
For easy understanding, referring to fig. 3, an embodiment of the present application provides an apparatus for predicting dielectric strength of an insulating medium, including the following modules:
an obtaining module 301, configured to obtain a variable factor parameter of a dielectric strength compound to be predicted, where the variable factor parameter includes: topological molecular surface area VSA, molecular front line orbital HOMO energy EHOMOLOMO energy E of molecular front-line orbitLUMOAbsolute average difference pi of surface electrostatic potential, mean square error of positive electrostatic potential on molecular surfaceAnd the mean square error of the negative potential on the surface of the molecule
A calculating module 302, configured to substitute the variable factor parameters into a dielectric strength prediction model to calculate the dielectric strength of the dielectric strength compound to be predicted, where the dielectric strength prediction model is:
wherein,
further, the obtaining module 301 is further configured to:
obtaining molecular weight MolWt;
accordingly, the dielectric strength prediction model is:
further, still include:
and the output module 303 is configured to output the calculation result of the dielectric strength and display the calculation result according to a preset format.
Further, the topological molecular surface area VSA and the molecular weight MolWt were calculated according to the RDKit descriptor module.
Further, the molecular front-line orbital HOMO energy EHOMOLOMO energy E of front-line orbit of moleculeLUMOCalculated according to the density functional method under APFD/6-311+ g (2d, p).
The embodiment of the application also provides an embodiment of insulating medium dielectric strength prediction equipment, and the insulating medium dielectric strength prediction equipment provided by the embodiment of the application comprises a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is used for executing the insulating medium dielectric strength prediction method in the embodiment of the insulating medium dielectric strength prediction method according to the instructions in the program code.
An embodiment of the computer-readable storage medium is further provided in an embodiment of the present application, and the computer-readable storage medium provided in the embodiment of the present application is configured to store a program code, where the program code is configured to execute the insulating medium dielectric strength prediction method in the foregoing insulating medium dielectric strength prediction method embodiment.
An embodiment of the present application further provides a computer program product including instructions, and the computer program product including instructions provided in the embodiment of the present application, when running on a computer, causes the computer to execute the method for predicting dielectric strength of an insulating medium in the embodiment of the method for predicting dielectric strength of an insulating medium.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a hardware form, and can also be realized in a software functional unit form.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (10)

1. A method for predicting dielectric strength of an insulating medium is characterized by comprising the following steps:
101. obtaining variable factor parameters of a dielectric strength compound to be predicted, wherein the variable factor parameters comprise: topological molecular surface area VSA, molecular front line orbital HOMO energy EHOMOLOMO energy E of molecular front-line orbitLUMOAbsolute average difference pi of surface electrostatic potential, mean square error of positive electrostatic potential on molecular surfaceAnd the mean square error of the negative potential on the surface of the molecule
102. Substituting the variable factor parameters into a dielectric strength prediction model to calculate the dielectric strength of the dielectric strength compound to be predicted, wherein the dielectric strength prediction model is as follows:
wherein,
2. the method of claim 1, wherein the step of predicting the dielectric strength of the insulating medium comprises: the variable factor parameters also comprise molecular weight MolWt;
accordingly, the dielectric strength prediction model is:
3. the method of predicting dielectric strength of an insulating medium according to claim 2,
the topological molecular surface area VSA and the molecular weight MolWt are calculated according to the RDKit descriptor module.
4. The method of claim 2, wherein the molecular front rail HOMO energy EHOMOLOMO energy E of front-line orbit of moleculeLUMOAccording to the density functional method, the density is in APFD/6-311+ g (2)d, p) is obtained by calculation.
5. The method of predicting dielectric strength of an insulating medium as set forth in claim 1, further comprising, after step 102:
103. and outputting the calculation result of the dielectric strength and displaying the calculation result according to a preset format.
6. An insulating medium dielectric strength prediction device, characterized by comprising the following modules:
an obtaining module, configured to obtain variable factor parameters of a dielectric strength compound to be predicted, where the variable factor parameters include: topological molecular surface area VSA, molecular front line orbital HOMO energy EHOMOLOMO energy E of molecular front-line orbitLUMOAbsolute average difference pi of surface electrostatic potential, mean square error of positive electrostatic potential on molecular surfaceAnd the mean square error of the negative potential on the surface of the molecule
A calculating module, configured to substitute the variable factor parameter into a dielectric strength prediction model to calculate the dielectric strength of the dielectric strength compound to be predicted, where the dielectric strength prediction model is:
wherein,
7. the insulating medium dielectric strength predicting device according to claim 6, wherein the obtaining module is further configured to:
obtaining molecular weight MolWt;
accordingly, the dielectric strength prediction model is:
8. an insulating medium dielectric strength prediction apparatus, comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the method of predicting dielectric strength of an insulating medium according to any one of claims 1 to 5 according to instructions in the program code.
9. A computer-readable storage medium for storing program code for performing the insulating medium dielectric strength prediction method of any one of claims 1-5.
10. A computer program product comprising instructions which, when run on a computer, cause the computer to perform the insulation medium dielectric strength prediction method of any one of claims 1-5.
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