CN113192046B - Automatic identification method for radial distribution function graph - Google Patents
Automatic identification method for radial distribution function graph Download PDFInfo
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- CN113192046B CN113192046B CN202110528637.8A CN202110528637A CN113192046B CN 113192046 B CN113192046 B CN 113192046B CN 202110528637 A CN202110528637 A CN 202110528637A CN 113192046 B CN113192046 B CN 113192046B
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- 238000005315 distribution function Methods 0.000 title claims abstract description 24
- 238000000034 method Methods 0.000 title claims abstract description 21
- 229910052739 hydrogen Inorganic materials 0.000 claims abstract description 16
- 239000001257 hydrogen Substances 0.000 claims abstract description 16
- 238000005411 Van der Waals force Methods 0.000 claims abstract description 14
- 238000000354 decomposition reaction Methods 0.000 claims description 13
- 230000007547 defect Effects 0.000 abstract 1
- 230000003993 interaction Effects 0.000 description 6
- 239000002245 particle Substances 0.000 description 5
- GDDNTTHUKVNJRA-UHFFFAOYSA-N 3-bromo-3,3-difluoroprop-1-ene Chemical compound FC(F)(Br)C=C GDDNTTHUKVNJRA-UHFFFAOYSA-N 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 230000009878 intermolecular interaction Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000000329 molecular dynamics simulation Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20064—Wavelet transform [DWT]
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Abstract
The invention discloses an automatic identification method of a radial distribution function graph, which comprises the steps of drawing a radial distribution function curve, denoising the radial distribution function curve, extracting the main characteristics of the radial distribution function curve, determining a peak value, and judging hydrogen bonds and strong van der Waals force based on the position of the peak value. The method disclosed by the invention is reasonable in design and simple in steps, so that a worker can quickly identify whether two key indexes, namely hydrogen bonds and strong van der Waals force, exist in the radial distribution function, and the defects of large error, low efficiency and the like when the two indexes are judged by naked eyes can be effectively overcome.
Description
Technical Field
The invention relates to the field of energetic materials, in particular to an automatic identification method of a radial distribution function graph.
Background
Radial Distribution Function (RDF) describes the variation of the density of a target particle in a particle (atom, molecule, etc.) system as a function of the distance of a reference particle, and this variation is mainly characterized by calculating the ratio of the area density in the vicinity of the target particle to the average density. The radial distribution function, also known as Gr, characterizes the probability of the occurrence of other atoms at any distance r from a given reference atom. The method can be used for researching the structure and special interaction of the condensed state, a radial distribution function graph can be obtained according to the classical molecular dynamics calculation, and the interaction type between corresponding particles can be roughly judged by analyzing the position of the peak value in the graph. The intermolecular interactions are usually hydrogen bonds and van der Waals interactions, and the Gr peak appears at a distance
When, it means that there is a hydrogen bond between the two; when the Gr peak appears at a distance
When, indicates that there is a strong van der Waals interaction between the two; when the Gr peak value appears a distance greater than
When it is weak, it means that there is a weakness between the twoVan der waals interactions, are generally negligible. Thus, the radial distribution function is a fast and efficient means for analyzing the type of interaction between specified groups in a system.
At present, when a radial distribution function graph is analyzed, the position where a peak appears is mainly judged in a visual mode, when the obtained radial distribution function has more noise, the error is larger in the visual judgment mode, and meanwhile, a large amount of time is consumed when the number of radial distribution functions to be processed is large. Therefore, the invention discloses an automatic identification method of a radial distribution function graph.
Disclosure of Invention
The invention provides an automatic identification method of a radial distribution function graph, aiming at the problems of large workload, low efficiency and the like when the key parameters in the radial distribution function graph are manually identified, and the automatic identification method is used for identifying two key parameters of hydrogen bonds and strong van der Waals force in a molecular structure. The method comprises the following steps:
s1, reading the r value of the related molecular structure and the Gr value of the radial distribution function of the related molecular structure, and drawing an original curve 0;
s2, denoising the original curve by adopting a wavelet denoising method to obtain a curve 1;
s3, performing wavelet decomposition on the curve 1, extracting integral features, and abandoning detailed features to obtain a curve 2;
s4, finding all maximum points and minimum points on the curve 2;
s5: judging whether the Gr values of the maximum value point and the left and right nearest minimum value points are greater than 0.05, if so, executing a step S6, otherwise, not having hydrogen bonds and strong van der Waals force, and turning to a step S8;
s6, judging whether the maximum points have the condition that the abscissa is less than 3.1 angstrom meters, if so, then hydrogen bonds exist, and if not, then hydrogen bonds do not exist;
s7, judging whether the maximum points have the condition that the abscissa is more than or equal to 3.1 angstrom and less than 5 angstrom, if so, having strong van der Waals force, and if not, not having strong van der Waals force;
and S8, marking and displaying the obtained result.
Description of the drawings:
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 shows the result of the present invention processing 1 RDF graph.
The specific implementation mode is as follows:
the invention provides an automatic identification method of a radial distribution function graph, which is used for identifying whether hydrogen bonds and strong van der Waals force exist in a radial distribution function. As shown in FIG. 1, the RDF chart of Ammonium Perchlorate (AP) and BAMO-THF copolyether (PBT) in this example was tested, and the method specifically includes the following steps:
s1, obtaining an RDF graph, and concretely comprising the following steps:
s10: obtaining all data point pairs (r, Gr) of the RDF;
and S11, drawing an (r, Gr) curve to obtain a raw RDF curve 0.
S2: denoising an original curve 0 by using a wavelet denoising method to obtain a curve 1, and specifically comprising the following steps:
s20, denoising the original RDF curve 0 by adopting a one-dimensional wavelet denoising function wden of MATLAB;
s21, selecting the threshold value in the wavelet denoising as follows: adjusting the threshold value according to the maximum minimum threshold value or the soft threshold value or the noise level estimation of each layer of wavelet decomposition;
s22, other parameters are set in the following mode when the wavelet is denoised: the wavelet base is selected as 'sym 3', and the decomposition layer number is 3;
s23 Curve 1 can be obtained by S21 and S22.
S3, performing wavelet decomposition on the curve 1, extracting integral characteristics to obtain a curve 2, and concretely comprising the following steps:
s30, decomposing the curve 1 by adopting a wavedec () function of MATLAB;
s31, selecting 'db 4' as the wavelet basis in wavelet decomposition, wherein the decomposition layer number is 3;
s32, abandoning detail features obtained by wavelet decomposition and keeping integral features;
S4, searching all maximum points and minimum points on the curve, which comprises the following steps:
s40: detecting left and right adjacent two points of all points on the curve 2 without considering the end point condition, and if the Gr value of a certain point is larger than the Gr values of the left and right adjacent points of the points at the same time, determining all the points meeting the condition as found maximum value points;
s41: and (3) detecting left and right adjacent two points of all points on the curve 2 without considering the end point condition, and if the Gr value of a certain point is smaller than the Gr value of the left adjacent point and the Gr value of the right adjacent point of the points at the same time, determining all the points meeting the condition as all the found minimum value points.
S5: and judging whether the Gr values of the maximum value point and the left and right nearest minimum value points are greater than 0.05, if so, executing the step S6, otherwise, judging that no hydrogen bond exists and no strong van der Waals force exists, and turning to the step S8.
And S6, judging whether the maximum points have the condition that the abscissa is less than 3.1 angstrom, if so, judging that hydrogen bonds exist, and if not, judging that no hydrogen bonds exist.
And S7, judging whether the abscissa of the maximum value points is more than or equal to 3.1 angstrom and less than 5 angstrom, if so, existing strong van der Waals force, and if not, not existing strong van der Waals force.
And S8, marking and displaying the obtained results, wherein the obtained results in the embodiment are shown in fig. 2.
Claims (6)
1. A method for automatically identifying a radial distribution function graph is characterized by comprising the following steps:
s1, reading the r value of the related molecular structure and the Gr value of the radial distribution function of the related molecular structure, and drawing an original curve 0;
s2, denoising the original curve by adopting a wavelet denoising method to obtain a curve 1;
s3, performing wavelet decomposition on the curve 1, extracting integral features, and abandoning detailed features to obtain a curve 2;
s4, finding all maximum points and minimum points on the curve 2;
s5: judging whether the Gr values of the maximum value point and the left and right nearest minimum value points are greater than 0.05, if so, executing a step S6, otherwise, not having hydrogen bonds and strong van der Waals force, and turning to a step S8;
s6, judging whether the maximum points have the condition that the abscissa is less than 3.1 angstrom meters, if so, then hydrogen bonds exist, and if not, then hydrogen bonds do not exist;
s7, judging whether the maximum points have the condition that the abscissa is more than or equal to 3.1 angstrom and less than 5 angstrom, if so, having strong van der Waals force, and if not, not having strong van der Waals force;
and S8, marking and displaying the obtained result.
2. The method according to claim 1, wherein the step S1 specifically comprises the following steps:
s10: obtaining all data point pairs (r, Gr) of the RDF;
and S11, drawing an (r, Gr) curve to obtain a raw RDF curve 0.
3. The method according to claim 1, wherein the step S2 specifically comprises the following steps:
s20, denoising the original RDF curve 0 by adopting a one-dimensional wavelet denoising function wden of MATLAB;
s21, selecting the threshold value in the wavelet denoising as follows: adjusting the threshold value according to the maximum minimum threshold value or the soft threshold value or the noise level estimation of each layer of wavelet decomposition;
s22, other parameters are set in the following mode when the wavelet is denoised: the wavelet base is selected as 'sym 3', and the decomposition layer number is 3;
s23 Curve 1 can be obtained by S21 and S22.
4. The method according to claim 1, wherein the step S3 specifically comprises the following steps:
s30, decomposing the curve 1 by adopting a wavedec () function of MATLAB;
s31, selecting 'db 4' as the wavelet basis in wavelet decomposition, wherein the decomposition layer number is 3;
s32, abandoning detail features obtained by wavelet decomposition and keeping integral features;
s33 Curve 2 can be obtained by S31 and S32.
5. The method according to claim 1, wherein the step S4 specifically comprises the following steps:
s40: detecting left and right adjacent two points of all points on the curve 2 without considering the end point condition, and if the Gr value of a certain point is larger than the Gr values of the left and right adjacent points of the points at the same time, determining all the points meeting the condition as found maximum value points;
s41: and (3) detecting left and right adjacent two points of all points on the curve 2 without considering the end point condition, and if the Gr value of a certain point is smaller than the Gr value of the left adjacent point and the Gr value of the right adjacent point of the points at the same time, determining all the points meeting the condition as all the found minimum value points.
6. The method for automatically identifying a radial distribution function map as claimed in claim 1, wherein step S8 is used for displaying and marking the obtained result, and the contents of the displaying and marking include curve 0, curve 1, curve 2, hydrogen bond identification, and strong van der waals force identification.
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