Method for evaluating heat resistance of energetic material based on molecular dynamics simulation

Abstract

The invention discloses an energetic material heat resistance evaluation method based on molecular dynamics simulation. The method comprises the following steps: (1) establishing a molecular dynamics model of an energetic material, simulating a thermal decomposition process of the energetic material through LAMMPS software, and obtaining data of changes of reactants, products and system potential energy along with time; (2) based on the obtained data, obtaining the initial decomposition time ti of the energetic material, and calculating to obtain the thermal decomposition induction time tPE and the cluster mass ratio Rc; and (3) comprehensively evaluating the heat resistance of the energetic material based on the three key parameters ti, tPE and Rc. According to the method, the problems of potential safety hazards and high cost existing in a traditional experiment mode are solved, and a new research idea is provided for quantitatively evaluating the heat resistance of the energetic material from the microscale.

Description

Technical Field

[0001] This invention belongs to the field of thermal safety in chemical processes and relates to a method for evaluating the heat resistance of energetic materials based on molecular dynamics simulation. Background Technology

[0002] The high energy density of energetic materials primarily originates from energetic groups such as nitro groups in their molecules. However, while these groups endow materials with high energy properties, they also often lead to increased sensitivity to external stimuli (such as heat, impact, and friction), exhibiting a contradictory characteristic of "high energy, high sensitivity." Under thermal stimulation, such materials are prone to violent decomposition reactions, which may lead to heat accumulation and temperature runaway due to the exothermic reaction, potentially causing combustion or even explosions, resulting in serious casualties, property damage, and environmental pollution. Therefore, the scientific assessment and control of the thermal safety of energetic materials is of significant practical importance.

[0003] To systematically understand the thermal hazard characteristics of energetic materials, researchers have gradually constructed a traditional experimental-based thermal risk assessment system. This system typically includes the following key steps: First, using thermal analysis techniques such as thermogravimetric analysis (TG), differential scanning calorimetry (DSC), simultaneous thermal analysis (STA), and adiabatic accelerated calorimetry (ARC) to obtain the material's heat flow curves and analyze its thermal decomposition mechanism; second, using model fitting or model-free kinetic methods to process experimental data and obtain kinetic parameters such as apparent activation energy; finally, by integrating key parameters such as initial decomposition temperature, heat of reaction, adiabatic temperature rise, time to reach maximum reaction rate under adiabatic conditions, and auto-accelerating decomposition temperature, a thermal risk index is calculated and a thermal risk matrix is ​​constructed, thereby achieving a quantitative assessment of the material's thermal hazard level. Although this system is relatively mature, it relies on experimental measurements and has limitations such as high cost, long cycle time, and certain safety risks. With the development of computational chemistry, assessment methods based on quantitative structure-activity relationship (QSPR) have, to some extent, compensated for the shortcomings of experimental methods, but its essence is a data-driven mapping model, which is highly dependent on the training dataset and has difficulty revealing the microscopic mechanism of material thermal decomposition.

[0004] In recent years, advancements in molecular dynamics simulation techniques have provided new avenues for studying the heat resistance of energetic materials. This method, without relying on large experimental samples, can directly simulate the structural evolution and chemical reaction processes of materials under thermal stimulation at the atomic scale, thereby revealing their thermal decomposition mechanisms and heat resistance properties at the microscopic level. Molecular dynamics simulations based on reactive force fields are particularly suitable for describing the breaking and formation of chemical bonds, providing important theoretical guidance for fundamentally understanding the thermal stability of energetic materials and further guiding the molecular design of high-performance, low-sensitivity energetic materials. Summary of the Invention

[0005] The purpose of this invention is to provide a method for evaluating the heat resistance of energetic materials based on molecular dynamics simulations. This method overcomes the limitations of traditional experimental methods, such as safety risks and high operating costs, and avoids the dependence of machine learning methods on large-scale datasets. It only requires obtaining the crystal structure information of the material to systematically evaluate its heat resistance through simulation, realizing the correlation and quantitative prediction from microscopic mechanisms to macroscopic thermal risks, and providing a new theoretical approach for the safety design and evaluation of energetic materials.

[0006] The technical solution to achieve the purpose of this invention is as follows:

[0007] A method for evaluating the heat resistance of energetic materials based on molecular dynamics simulations includes the following steps:

[0008] (1) Establish a molecular dynamics model of energetic materials, simulate their thermal decomposition process using LAMMPS software, and obtain data on the changes of reactants, products and system potential energy over time.

[0009] (2) Based on the data obtained in step (1), the initial decomposition time t of the energetic material is obtained. i The thermal decomposition induction time t was calculated. PE and the percentage of cluster quality R c ;

[0010] (3) Based on t i t PE and R c Three key parameters are used to comprehensively evaluate the heat resistance of energetic materials, and the evaluation criteria are as follows:

[0011] .

[0012] Furthermore, in step (1), the energetic material is a common energetic material, such as 1,3,5-triamino-2,4,6-trinitrobenzene (TATB), cyclotrimethylenetrinitramine (RDX), etc.

[0013] Further, step (1) specifically involves: establishing a molecular dynamics model of the energetic material and performing simulation calculations using LAMMPS software; firstly, setting the simulation conditions and adopting a programmed heating model, wherein the programmed heating mode simulates the material at a set heating rate within a specified temperature range; the simulation uses the ReaxFF / lg reaction force field, which can describe the formation and breaking of chemical bonds, and the conjugate gradient algorithm is used to optimize the atomic coordinates of the supercell system to minimize the total energy of the system; the energy convergence threshold is 10. -10 kcal·mol -1 The convergence criterion for interatomic forces is set to 10. -10 kcal·mol -1 ·Å -1Initially, all atoms were assigned an initial velocity of 300 K according to the Maxwell-Boltzmann distribution. Subsequently, a 5 ps relaxation was performed using an isothermal-isobaric ensemble (NPT) to release residual stress in the system. During relaxation, a Nose-Hoover thermostat and a barometric thermostat were used to control the system temperature and pressure, respectively. The temperature was programmed to rise at 12 K·ps. -1 The heating rate was programmed to increase from 300 K to 2700 K, with a total simulation time of 200 ps, ​​to obtain data on the changes in reactants, products, and system potential energy over time.

[0014] Furthermore, in step (2), the methods for obtaining each parameter are as follows:

[0015] (2.1) Initial decomposition time t i Based on molecular dynamics simulations, the simulation time from the start of heating to the first chemical bond breaking (i.e., the initial decomposition reaction) in the energetic material system can be directly read from the LAMMPS output file. Among them, t i A longer t value indicates that the material retains its chemical structure intact for a longer period under thermal stimulation, and its initial heat resistance is generally higher. Under the same simulation conditions, t i It can indirectly reflect the activation energy of the initial decomposition step. A higher activation energy usually leads to a longer t0. i .

[0016] (2.2) Thermal decomposition induction time t PE This is calculated based on the potential energy output file of LAMMPS, representing the time difference between the start of the thermal decomposition simulation and the point at which the system's potential energy reaches its maximum. The maximum potential energy marks the critical turning point where the system transitions from the "induction phase," where it needs to continuously absorb energy to overcome the reaction energy barrier, to the "accelerated decomposition phase," where potential energy is released and the reaction proceeds spontaneously and rapidly. PE The shorter the time, the less time the system needs to accumulate to a critical unstable state under thermal action, the faster the overall thermal decomposition process starts, and the worse the thermal stability of the material.

[0017] (2.3) Cluster mass percentage R c The calculation formula is as follows: The species evolution data is derived from the LAMMPS data file.

[0018] (1)

[0019] In the formula, The total mass of the system clusters is defined as the total mass of the system clusters. Products with a relative molecular weight smaller than that of the reactants are defined as small molecule products, and vice versa. The total mass of the system.

[0020] If the highly reactive free radicals and fragments generated in the initial stage of decomposition are prone to secondary polymerization, addition or condensation reactions, or dimerization, forming clusters with larger molecular weights, then R c It will rise rapidly. The formation of a large number of clusters effectively inhibits atomic migration and heat transfer, thereby significantly improving the heat resistance of energetic materials.

[0021] Compared with the prior art, the present invention has the following advantages:

[0022] (1) The method for evaluating the heat resistance of energetic materials in this invention does not rely on experimental testing and external data, which overcomes the limitations of traditional thermal analysis techniques such as safety risks, high costs and complex operation, while avoiding the dependence of data-driven models on large-scale samples.

[0023] (2) The heat resistance assessment method of energetic materials of the present invention can directly track the microstructure evolution and chemical reaction process of materials under thermal stimulation at the atomic scale, thereby revealing their heat resistance at the mechanism level and providing a direct and reliable basis for quantifying the thermal risk level in essence. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the process for evaluating the heat resistance of energetic materials based on molecular dynamics simulations.

[0025] Figure 2 The cell structures are for two energetic material models.

[0026] Figure 3 This represents the ratio of the remaining reactants in the energetic material to the original reactants during the programmed heating process.

[0027] Figure 4 This refers to the potential energy evolution of energetic materials during programmed heating.

[0028] Figure 5 This represents the evolution of the mass percentage of different components during the programmed heating process. Detailed Implementation

[0029] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0030] The method for evaluating the heat resistance of energetic materials based on molecular dynamics simulation of the present invention includes the following steps:

[0031] (1) Establish a molecular dynamics model of the energetic material, simulate its thermal decomposition process using LAMMPS software, and obtain data on the changes of reactants, products, and system potential energy over time, specifically including:

[0032] First, simulation conditions were set using a programmed temperature rise model. The programmed temperature rise mode simulates the process within a specified temperature range at a set heating rate. The simulation employs the ReaxFF / lg reactive force field, which describes the formation and breaking of chemical bonds. The conjugate gradient algorithm is used to optimize the atomic coordinates of the supercell system to minimize the total system energy. The energy convergence threshold is 10. -10 kcal·mol -1 The convergence criterion for interatomic forces is set to 10. -10 kcal·mol -1 ·Å -1 Initially, all atoms were assigned an initial velocity of 300 K according to the Maxwell-Boltzmann distribution. Subsequently, a 5 ps relaxation was performed using an isothermal-isobaric ensemble to release residual stress in the system. During relaxation, a Nose-Hoover thermostat and a barometric thermostat were used to control the system temperature and pressure, respectively. The temperature program was initiated at 12 K∙ps. -1 The heating rate was programmed to increase from 300 K to 2700 K, with a total simulation time of 200 ps, ​​in order to obtain data files on the changes in reactants, products, and system potential energy over time.

[0033] (2) Based on the data obtained above, the initial decomposition time t of the energetic material is obtained. i The thermal decomposition induction time t was calculated. PE and the percentage of cluster quality R c ;

[0034] (2.1) Initial decomposition time t i Based on molecular dynamics simulations, the simulation time from the start of heating to the first chemical bond breaking (i.e., the initial decomposition reaction) in the energetic material system can be directly read from the LAMMPS output file. Among them, t i A longer t value indicates that the material retains its chemical structure intact for a longer period under thermal stimulation, and its initial heat resistance is generally higher. Under the same simulation conditions, t i It can indirectly reflect the activation energy of the initial decomposition step. A higher activation energy usually leads to a longer t0. i .

[0035] (2.2) Thermal decomposition induction time t PE This is calculated based on the potential energy output file of LAMMPS, representing the time difference between the start of the thermal decomposition simulation and the point at which the system's potential energy reaches its maximum. The maximum potential energy marks the critical turning point where the system transitions from the "induction phase," where it needs to continuously absorb energy to overcome the reaction energy barrier, to the "accelerated decomposition phase," where potential energy is released and the reaction proceeds spontaneously and rapidly. PEThe shorter the time, the less time the system needs to accumulate to a critical unstable state under thermal action, the faster the overall thermal decomposition process starts, and the worse the thermal stability of the material.

[0036] (2.3) Cluster mass percentage R c The calculation formula is as follows: The species evolution data is derived from the LAMMPS data file.

[0037] (1)

[0038] In the formula, The total mass of the system clusters is defined as the total mass of the system clusters. Products with a relative molecular weight smaller than that of the reactants are defined as small molecule products, and vice versa. The total mass of the system.

[0039] If the highly reactive free radicals and fragments generated in the initial stage of decomposition are prone to secondary polymerization, addition or condensation reactions, or dimerization, forming clusters with larger molecular weights, then R c It will rise rapidly. The formation of a large number of clusters effectively inhibits atomic migration and heat transfer, thereby significantly improving the heat resistance of energetic materials.

[0040] (3) Based on t i t PE and R c Three key parameters are used to comprehensively evaluate the heat resistance of energetic materials. The evaluation criteria are shown in Table 1.

[0041] Table 1

[0042]

[0043] Example 1

[0044] A method for evaluating the heat resistance of energetic materials based on molecular dynamics simulations, using TATB and RDX as research objects, includes the following steps:

[0045] (1) Two energetic materials were selected as research objects, namely TATB and RDX. The unit cell structures of each material are shown in the figure. Figure 2 Based on periodic boundary conditions, cell expansion was performed along the a, b, and c axes for each monomer unit cell (TATB: 4×4×3; RDX: 2×2×3) to construct simulated systems containing 2000-3000 atoms. The similar number of atoms in each system effectively avoids the influence of intermolecular interaction deviations caused by differences in system size.

[0046] The thermal decomposition of the target energetic material was simulated using LAMMPS software. Prior to the simulation, the atomic coordinates of all supercell systems were optimized using the conjugate gradient algorithm to minimize the total system energy. The energy convergence threshold was 10.-10 kcal·mol -1 The convergence criterion for interatomic forces is set to 10. -10 kcal·mol -1 ·Å -1 Initially, all atoms were assigned an initial velocity of 300 K according to the Maxwell-Boltzmann distribution. Subsequently, at 300 K and 1 atm, a 5 ps relaxation was performed using an isothermal-isobaric ensemble to release residual stress in the system. During the relaxation process, a Nose-Hoover thermostat and a barometric thermostat were used to control the system temperature and pressure, respectively, with a time step of 0.1 fs and a temperature damping coefficient of 10 fs. Atomic trajectories, temperature, potential energy, and chemical composition were recorded every 100 steps. Based on the equilibrium model, an NVT ensemble was used with a velocity of 12 K∙ps. -1 The heating rate was programmed to increase from 300 K to 2700 K, with a simulation time of 200 ps, ​​to obtain data files on the changes in reactants, products, and system potential energy over time.

[0047] (2) Based on the data obtained in step (1), the simulation data is analyzed and processed using software such as Origin to obtain the initial decomposition time t. i And calculate the thermal decomposition induction time t PE Cluster quality percentage R c Three key parameters. The specific calculation process is as follows:

[0048] (2.1) First at 12 K∙ps -1 Under programmed temperature rise, the amount of reactants is greater than that of R. m The decay curves and the potential energy evolution curves of the entire system are shown in the figure. Figure 3 and Figure 4 Initial decomposition time, t i A longer value indicates a longer time for the material to maintain its chemical structure under thermal stimulation, and its heat resistance is generally higher. Thermal decomposition induction time, t PE The shorter the time, the less time the system needs to accumulate to a critical unstable state under thermal effects, the faster the overall thermal decomposition process starts, and the relatively worse the thermal stability of the material. Cluster mass percentage R c A higher value indicates that the formation of numerous clusters effectively suppresses atomic migration and heat transfer, thereby significantly improving the heat resistance of energetic materials. The values ​​of the three key parameters are shown in Table 2:

[0049] Table 2

[0050]

[0051] (3) Based on t i t PE and R cThree key parameters were used to comprehensively evaluate the heat resistance of energetic materials. As shown in Table 1, the evaluation criteria revealed significant differences in heat resistance between the two materials. TATB exhibited the longest initial decomposition time and thermal decomposition induction time, along with the highest cluster mass percentage; while RDX had the shortest initial decomposition time and thermal decomposition induction time, and the lowest cluster mass percentage, at only 3%. Based on the aforementioned heat resistance evaluation criteria, TATB is classified as an energetic material with good heat resistance, while RDX is classified as an energetic material with poor heat resistance. Therefore, in practical storage and applications, RDX-type materials require close attention and appropriate protective measures to reduce their risk of thermal runaway.

[0052] (4) The cluster evolution behavior of the two energetic materials during the heating process was comprehensively analyzed, and the results are shown in the figure. Figure 5 The high-frequency decomposition modes of energetic materials were statistically analyzed, and the results are shown in Table 3:

[0053] Table 3

[0054]

[0055] (5) Based on the analysis results of cluster evolution and initial reaction pathways, the microscopic mechanism by which energetic materials possess excellent heat resistance is systematically elucidated. According to the above cluster mass ratio R... c Analysis revealed that TATB, with its superior heat resistance, exhibited a significant growth trend during heating, consistently accounting for the largest proportion across all mass ranges. In contrast, RDX tended to generate more small molecules during thermal decomposition. High-frequency reaction analysis showed that the initial decomposition of TATB was always dimerization. Conversely, the initial decomposition of RDX, a less heat-resistant energetic material, primarily involved NO2 elimination, directly breaking down into small molecules during the initial decomposition process. This indicates that RDX, with its low heat resistance, tends to rapidly decompose into stable small-molecule products during pyrolysis, while TATB, with its high heat resistance, tends to form clusters, thus improving its heat resistance.

Claims

1. A method for evaluating heat resistance of energetic materials based on molecular dynamics simulation, characterized by, The method comprises the following steps: (1) establishing a molecular dynamics model of the energetic material, simulating a thermal decomposition process of the energetic material by using LAMMPS software, and obtaining data of reactants, products and potential energy of the system changing with time; (2) based on the data obtained in step (1), obtaining the initial decomposition time t of the energetic material i , calculating the thermal decomposition induction time t PE and the cluster mass proportion R c ; (3) Based on t i , t PE and R c , three key parameters, the heat resistance of energetic materials is comprehensively evaluated, and the evaluation criteria are as follows: 。 2. The energetic material heat resistance evaluation method according to claim 1, characterized by, In step (1), the energetic material is TATB or RDX.

3. The energetic material heat resistance evaluation method according to claim 1, characterized by, Step (1) is specifically: establishing a molecular dynamics model of energetic materials, using LAMMPS software for simulation calculation: first, set the simulation conditions, adopt the program heating to heat the model, wherein the program heating mode is simulated at a specified temperature range with a set heating rate; the simulation adopts the ReaxFF / lg reaction force field which can describe the formation and rupture of chemical bonds, and the conjugate gradient algorithm is used to optimize the atomic coordinates of the supercell system to minimize the total energy of the system; the energy convergence threshold is 10 -10 kcal·mol -1 , the convergence standard of interatomic force is set to 10 -10 kcal·mol -1 ·Å -1 ; at the initial moment, all atoms are given initial velocities at 300 K according to the Maxwell-Boltzmann distribution, then an isothermal-isobaric ensemble (NPT) is used for 5 ps relaxation to release the residual stress of the system, and a Nose-Hoover thermostat and a gas pressure thermostat are used to control the temperature and pressure of the system during the relaxation process; the program heating process is programmed to heat from 300 K to 2700 K at a heating rate of 12 K·ps -1 , a total of 200 ps simulation, and the data of reactants, products and system potential energy change with time are obtained.

4. The energetic material heat resistance evaluation method according to claim 1, characterized by, In step (2), each parameter is obtained by the following method: (2.1) Initial decomposition time t i : Based on molecular dynamics simulation, the simulation time corresponding to the first chemical bond rupture of the energetic material system from the start of heating is directly read through the output file of LAMMPS; (2.2) Thermal decomposition induction time t PE : Calculated from the potential energy output file of LAMMPS, i.e. the time difference between the start of thermal decomposition simulation and the time when the system potential energy reaches the highest point. (2.3) Cluster mass ratio R c : According to the species evolution file of LAMMPS, the calculation formula is as follows: （1） wherein Mtot is the total mass of the system cluster, and products with a relative molecular mass less than that of the reactants are defined as small molecule products, and otherwise as clusters; Mtot is the total mass of the system.

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