Method for calculating and evaluating safety of explosive through neural network potential

Abstract

The invention provides a method for calculating and evaluating the safety of an explosive through neural network potential. The method comprises the following steps: S1, obtaining a microstructure image of a binder explosive through an in-situ transmission electron microscope (TEM); s2, establishing a molecular model of an energetic material and a molecular model of a binder according to the obtained microstructure image, and modifying the microstructure image; s3, performing neural network training on data obtained by analogue simulation, and fitting potential energy surfaces of the molecular model of the energetic material and the molecular model of the binder; s4, simulating the evolution of the explosive along with time under the load effect; and S5, analyzing a simulation result. According to the method for calculating and evaluating the safety of the explosive through the neural network potential, all explosives which have been applied and have application potential at present can be evaluated by adopting the established method for calculating the safety of the real microstructure of the binder explosive based on the neural network potential, so that the application of novel explosives is greatly promoted; and great convenience is brought to ammunition safety engineering personnel.

Description

Technical Field

[0001] The invention relates to the field of explosive safety, and in particular to a method for evaluating explosive safety through neural network potential calculation. Background Art

[0002] In order to solve the contradiction between the power and safety of ammunition, both China and foreign countries are committed to developing new explosives. Among them, polymer bonded explosives, which are made of main explosives (such as RDX, HMX and triaminotrinitrobenzene (TATB)) or their mixtures mixed with polymer binders, have good safety and high energy characteristics and are an important direction of current research on explosive formulations. The impact initiation performance and detonation dynamics of explosives directly affect the performance indicators of weapon warheads. Studying the process and mechanism of detonation growth of impact initiation of explosives is the core of understanding the impact initiation performance, detonation performance and safety performance of explosives, and is the basis for the detonation power and safety design of ammunition. When hot spots are formed inside explosives due to impact, it is extremely important to study the mechanism and principle of hot spot formation.

[0003] At present, the macroscopic phenomenological reaction rate model is the most commonly used research method in China for the safety design of explosives, but this method cannot well describe the mechanism of hot spot formation under impact. Establishing a detonation reaction flow model with a certain universality to accurately describe the entire process of impact initiation and detonation growth of high-energy insensitive mixed explosives is the theoretical basis for the power and safety design of explosives, and is a hot issue in the current research of detonation physics at home and abroad. However, how to establish a model that can accurately describe the internal mesoscopic and microscopic structure of explosives, and accurately evaluate the safety of explosives based on the complex atomic-scale physicochemical changes inside the explosives. Existing methods are still unable to accurately evaluate the safety of explosives in extreme environments through atomic-scale models, which remains to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide a method for evaluating the safety of explosives through neural network potential calculation. The method for calculating the safety of the real microstructure of binder explosives based on neural network potential can be used to evaluate all explosives that are currently used and have application potential, which greatly promotes the application of new explosives and will bring great convenience to ammunition safety engineers.

[0005] To achieve the above object, the present invention provides a method for evaluating the safety of explosives by neural network potential calculation, comprising the following steps:

[0006] S1. Microstructure images of the binder explosive were obtained by in-situ transmission electron microscopy (TEM);

[0007] S2. According to the obtained microstructure image, a molecular model of the energetic material and a molecular model of the binder are established and the microstructure image is modified;

[0008] S3. Using a quantum mechanics calculation method based on density functional theory to simulate the molecular model of the energetic material and the molecular model of the binder, and performing neural network training on the data obtained from the simulation to fit the potential energy surface of the molecular model of the energetic material and the molecular model of the binder;

[0009] S4, applying external shock or high temperature, using the established neural network potential to perform calculations to simulate the evolution of explosives over time under load;

[0010] S5. Analyze the simulation results, evaluate the temperature, stress, chemical and physical phenomena of the explosives under external loads, and then evaluate the safety of the explosives.

[0011] Preferably, in step S1, the microstructure image includes the size of energetic material crystals, internal cracks, pores, dislocations, and the size and distribution position of the binder.

[0012] Preferably, in step S2, atoms are deleted and modified at corresponding defect positions to conform to the microstructure image of the real binder explosive, thereby obtaining a binder explosive model including the microstructure.

[0013] Preferably, in step S3, the basic idea of ​​density functional theory is to use electron density to describe the ground state energy of the system, and the energy function is expressed as:

[0014] E[ρ]=T s [ρ]+E ext [ρ]+E H [ρ]+E XC [ρ]+E elec [ρ];

[0015] Among them, T s [ρ] is the non-relativistic kinetic energy term; E ext [ρ] is the interaction energy between the electron and the external potential; E H [ρ] is the classical Coulomb interaction energy between electrons; E XC [ρ] is the exchange-correlation energy; E elec [ρ] is the energy of interaction between electrons;

[0016] To solve for the electron density ρ(r), we need to solve the Kohn-Sham equation:

[0017]

[0018] Among them, ψ i (r) is a Kohn-Sham orbital; ∈ i is the energy of the orbit; V eff (r) is the effective potential, including external potential, Hartree potential and exchange-correlation potential;

[0019] V eff (r) = V ext (r)+V H (r)+V XC (r);

[0020] Exchange-correlation energy E XC The calculation of [ρ] is the key to density functional theory, the exchange-correlation functional E XC [ρ] is approximated as follows:

[0021] E XC [ρ]=∫ρ(r)∈ XC (ρ(r))dr;

[0022] Among them, ∈ XC is the local density functional of the exchange-correlation energy;

[0023] The expression of the single electron wave function is:

[0024]

[0025] in, is the kinetic energy term; is the external potential; is the Coulomb interaction between electrons; It is an exchange effect;

[0026] The total energy of the system is the sum of the energies of the single-particle orbitals:

[0027]

[0028] Among them, ∈ i is the energy of the electron orbital;

[0029] The energy calculation formula in quantum mechanics can be expressed by the following total energy term:

[0030] E total =E kinetic +E electrostatic +E exchange +E correlation ;

[0031] Among them, E kinetic is the kinetic energy term; E electrostatic is the Coulomb interaction energy; E exchange is the exchange energy; E correlation It is related energy.

[0032] Preferably, in step S3, the theoretical method of the neural network potential is as follows:

[0033] The two-body embedding of the DP descriptor is a multi-body representation of the smoothed version of the atomic local environment. The descriptor of the complete information is given by:

[0034]

[0035] The number of neighboring atoms is less than N c , then the dimension is N c The matrix will be filled; is a coordinate matrix, each row Can be constructed as:

[0036]

[0037] Among them, r ij is the relative coordinate, s(r ij ) is defined as:

[0038]

[0039] in, From 1 r s Switches to 0 at the cutoff radius; s(r ij ) function is smooth because the second-order derivative is continuous and each row of the embedding matrix consists of nodes from the output layer of the NN function:

[0040]

[0041] Preferably, in step S5, the molecular temperature is calculated by the following formula:

[0042]

[0043] Among them, k B is the Boltzmann constant, which is 1.380649×10 -23 m 2 kg / s 2 K, E kin is the sum of the kinetic energy of all atoms in the system, N DOF is the degree of freedom of the system as a whole;

[0044] The molecular stress is obtained by adding the molecular kinetic energy and the Virial contribution, and is calculated as follows:

[0045] S ab =-mv a v b -W ab ;

[0046]

[0047] Among them, the first Virial contribution is the sum of the energy contributions of two atoms in pairs; Np is the number of atoms in the pair; r1 and r2 are the positions of the two atoms in the pairwise interaction; F1 and F2 are the forces generated by the pairwise interaction; the second term is a bonding contribution of similar form, N b is the number of bonds; the following three items are similar contribution items, N a is the bond angle contribution; N d Contribution to dihedral angle; N i is the anomalous interaction term; the last term represents the KSpace contribution of the long-range Coulomb interaction.

[0048] Therefore, the present invention adopts the above-mentioned method for evaluating the safety of explosives through neural network potential calculation. The real microstructure safety calculation method of binder explosives based on neural network potential can evaluate all explosives that are currently used and have application potential, which greatly promotes the application of new explosives and will bring great convenience to ammunition safety engineers. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of a method for evaluating the safety of explosives by neural network potential calculation according to the present invention;

[0050] Figure 2 A structural diagram of a floor suction head of a method for evaluating the safety of explosives by neural network potential calculation according to the present invention;

[0051] Figure 3 The present invention is a connection diagram of a controller for a method of evaluating the safety of explosives through neural network potential calculation. DETAILED DESCRIPTION

[0052] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0053] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0054] Embodiment 1

[0055] like Figure 1-Figure 3 As shown, the present invention provides a method for evaluating the safety of explosives by neural network potential calculation, comprising the following steps:

[0056] S1. Microstructure images of the binder explosive were obtained by in-situ transmission electron microscopy (TEM);

[0057] Microstructure images include the size of energetic material crystals, internal cracks, pores, dislocations, and the size and distribution of binders;

[0058] S2. According to the obtained microstructure image, a molecular model of the energetic material and a molecular model of the binder are established and the microstructure image is modified;

[0059] In step S2, atoms are deleted and modified at corresponding defect positions to conform to the microstructure image of the real binder explosive, thereby obtaining a binder explosive model including the microstructure.

[0060] S3. Using a quantum mechanics calculation method based on density functional theory to simulate the molecular model of the energetic material and the molecular model of the binder, and performing neural network training on the data obtained from the simulation to fit the potential energy surface of the molecular model of the energetic material and the molecular model of the binder;

[0061] In step S3, the basic idea of ​​density functional theory is to use electron density to describe the ground state energy of the system. The energy function is expressed as:

[0062] E[ρ]=T s [ρ]+E ext [ρ]+E H [ρ]+E XC [ρ]+E elec [ρ];

[0063] Among them, T s [ρ] is the non-relativistic kinetic energy term (i.e., Kohn-Sham kinetic energy); E ext [ρ] is the interaction energy between the electron and the external potential; E H [ρ] is the classical Coulomb interaction energy (Hartree energy) between electrons; E XC [ρ] is the exchange-correlation energy (exchange-correlation functional); E elec [ρ] is the energy of interaction between electrons;

[0064] To solve for the electron density ρ(r), we need to solve the Kohn-Sham equation:

[0065]

[0066] Among them, ψ i (r) is a Kohn-Sham orbital; ∈ i is the energy of the orbit; V eff (r) is the effective potential, including external potential, Hartree potential and exchange-correlation potential;

[0067] V eff (r) = V ext (r)+V H (r)+V XC (r);

[0068] Exchange-correlation energy EXC The calculation of [ρ] is the key to density functional theory, the exchange-correlation functional E XC [ρ] is approximated as follows:

[0069] E XC [ρ]=∫ρ(r)∈ XC (ρ(r))dr;

[0070] Among them, ∈ XC is the local density functional of the exchange-correlation energy;

[0071] The expression of the single electron wave function is:

[0072]

[0073] in, is the kinetic energy term; is the external potential; is the Coulomb interaction between electrons; It is an exchange effect;

[0074] The total energy of the system is the sum of the energies of the single-particle orbitals:

[0075]

[0076] Among them, ∈ i is the energy of the electron orbital;

[0077] The energy calculation formula in quantum mechanics can be expressed by the following total energy term:

[0078] E total =E kinetic +E electrostatic +E exchange +E correlation ;

[0079] Among them, E kinetic is the kinetic energy term; E electrostatic is the Coulomb interaction energy; E exchange is the exchange energy; E correlation It is related energy.

[0080] In step S3, the theoretical method of the neural network potential is as follows:

[0081] The two-body embedding of the DP descriptor is a multi-body representation of the smoothed version of the atomic local environment. The descriptor of the complete information is given by:

[0082]

[0083] The number of neighboring atoms is less than N c , then the dimension is N c The matrix will be filled; is a coordinate matrix, each row Can be constructed as:

[0084]

[0085] Among them, r ij is the relative coordinate, s(r ij ) is defined as:

[0086]

[0087] in, From 1 r s Switches to 0 at the cutoff radius; s(r ij ) function is smooth because the second-order derivative is continuous and each row of the embedding matrix consists of nodes from the output layer of the NN function:

[0088]

[0089] S4, applying external shock or high temperature, using the established neural network potential to perform calculations to simulate the evolution of explosives over time under load;

[0090] S5. Analyze the simulation results, evaluate the temperature, stress, chemical and physical phenomena of the explosives under external loads, and then evaluate the safety of the explosives.

[0091] In step S5, the molecular temperature is calculated by the following formula:

[0092]

[0093] Among them, k B is the Boltzmann constant, which is 1.380649×10 -23 m 2 kg / s 2 K, E kin is the sum of the kinetic energy of all atoms in the system, N DOF is the degree of freedom of the system as a whole.

[0094] The molecular stress is obtained by adding the molecular kinetic energy and the Virial contribution, and is calculated as follows:

[0095] S ab =-mv a v b -W ab ;

[0096]

[0097] Among them, the first Virial contribution is the sum of the energy contributions of two atoms in pairs; N pis the number of atoms in the pair; r1 and r2 are the positions of the two atoms in the pairwise interaction; F1 and F2 are the forces generated by the pairwise interaction; the second term is a bonding contribution of similar form, N b is the number of bonds; the following three items are similar contribution items, N a is the bond angle contribution; N d Contribution to dihedral angle; N i is the anomalous interaction term; the last term represents the KSpace contribution of the long-range Coulomb interaction.

[0098] Therefore, the present invention adopts the above-mentioned method for evaluating the safety of explosives through neural network potential calculation. The real microstructure safety calculation method of binder explosives based on neural network potential can evaluate all explosives that are currently used and have application potential, which greatly promotes the application of new explosives and will bring great convenience to ammunition safety engineers.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for evaluating the safety of explosives by neural network potential calculation, characterized in that: The following steps are involved: S1. Microstructure images of the binder explosive were obtained by in-situ transmission electron microscopy (TEM); S2. According to the obtained microstructure image, a molecular model of the energetic material and a molecular model of the binder are established and the microstructure image is modified; S3. Using a quantum mechanics calculation method based on density functional theory to simulate the molecular model of the energetic material and the molecular model of the binder, and performing neural network training on the data obtained from the simulation to fit the potential energy surface of the molecular model of the energetic material and the molecular model of the binder; S4, applying external shock or high temperature, using the established neural network potential to perform calculations to simulate the evolution of explosives over time under load; S5. Analyze the simulation results, evaluate the temperature, stress, chemical and physical phenomena of the explosives under external loads, and then evaluate the safety of the explosives.

2. A method for evaluating the safety of explosives by neural network potential calculation according to claim 1, characterized in that: In step S1 , the microstructure image includes the size of energetic material crystals, internal cracks, pores, dislocations, and the size and distribution position of the binder.

3. A method for evaluating the safety of explosives by neural network potential calculation according to claim 1, characterized in that: In step S2, atoms are deleted and modified at corresponding defect positions to conform to the microstructure image of the real binder explosive, thereby obtaining a binder explosive model including the microstructure.

4. The method for evaluating the safety of explosives by neural network potential calculation according to claim 1, characterized in that: In step S3, the basic idea of ​​density functional theory is to use electron density to describe the ground state energy of the system. The energy function is expressed as: E[ρ]=T s [p]+E ext [p]+E H [p]+E XC [p]+E elec [r]; Among them, T s [ρ] is the non-relativistic kinetic energy term; E ext [ρ] is the interaction energy between the electron and the external potential; E H [ρ] is the classical Coulomb interaction energy (Hartree energy) between electrons; E XC [ρ] is the exchange-correlation energy; E elec [ρ] is the energy of interaction between electrons; To solve for the electron density ρ(r), we need to solve the Kohn-Sham equation: Among them, ψ i (r) is a Kohn-Sham orbital; ∈ i is the energy of the orbit; V eff (r) is the effective potential, including external potential, Hartree potential and exchange-correlation potential; V eff (r)=V ext (r)+V H (r)+V XC (r); Exchange-correlation energy E XC The calculation of [ρ] is the key to density functional theory, the exchange-correlation functional E XC [ρ] is approximated as follows: E XC [ρ]=∫ρ(r)∈ XC (ρ(r))dr; Among them, ∈ XC is the local density functional of the exchange-correlation energy; The expression of the single electron wave function is: in, is the kinetic energy term; is the external potential; is the Coulomb interaction between electrons; It is an exchange effect; The total energy of the system is the sum of the energies of the single-particle orbitals: Among them, ∈ i is the energy of the electron orbital; The energy calculation formula in quantum mechanics can be expressed by the following total energy term: AND total =And kinetic +E electrostatic +E exchange +E correlation ; Among them, E kinetic is the kinetic energy term; E electrostatic is the Coulomb interaction energy; E exchange is the exchange energy; E correlation It is related energy.

5. The method for evaluating the safety of explosives by neural network potential calculation according to claim 1, characterized in that: In step S3, the theoretical method of the neural network potential is as follows: The two-body embedding of the DP descriptor is a multi-body representation of the smoothed version of the atomic local environment. The descriptor of the complete information is given by: The number of neighboring atoms is less than N c , then the dimension is N c The matrix will be filled; is a coordinate matrix, each row Can be constructed as: Among them, r ij is a relative coordinate; s(r ij ) is defined as: in, From 1 r s Switches to 0 at the cutoff radius; s(r ij ) function is smooth because the second-order derivative is continuous and each row of the embedding matrix consists of nodes from the output layer of the NN function:

6. A method for evaluating the safety of explosives by neural network potential calculation according to claim 1, characterized in that: In step S5, the molecular temperature is calculated by the following formula: N DOF =n dim N atoms -n dim -N fixDOFs Among them, k B is the Boltzmann constant, which is 1.380649×10 -23 m 2 kg / s 2 K; E kin is the sum of the kinetic energy of all atoms in the system, N DOF is the degree of freedom of the system as a whole; The molecular stress is obtained by adding the molecular kinetic energy and the Virial contribution, and is calculated as follows: S ab =-mv a v b -W ab ; Among them, the first Virial contribution is the sum of the energy contributions of two atoms in pairs; N p is the number of atoms in the pair; r1 and r2 are the positions of the two atoms in the pairwise interaction; F1 and F2 are the forces generated by the pairwise interaction; the second term is a bonding contribution of similar form, N b is the number of bonds; the following three items are similar contribution items, N a is the bond angle contribution; N d Contribution to dihedral angle; N i is the anomalous interaction term; the last term represents the KSpace contribution of the long-range Coulomb interaction.

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