Cellular automaton-based porous nanometal molecular dynamics modeling method, system and apparatus
By using a cellular automata model to divide porous nanometal systems into grids and update their states, the computational complexity and flexibility of traditional modeling methods are addressed, enabling efficient and accurate molecular dynamics modeling of porous nanometals, applicable to simulations of various materials.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGDONG UNIV OF TECH
- Filing Date
- 2026-04-03
- Publication Date
- 2026-07-14
AI Technical Summary
Existing techniques suffer from high computational complexity and poor flexibility in molecular dynamics modeling of porous nanometals, making it difficult to accurately describe the dynamic changes of materials.
A cellular automata model is adopted to divide the porous nanometal system into a uniform grid, define the cellular state and update the state through the Mohr's neighbor rule, so as to reflect the microstructure characteristics and achieve efficient and flexible modeling.
It achieves accurate reproduction of pore structure and flexible adjustment of porosity, adapts to highly adaptable modeling of different nanomaterials, and reduces computational costs.
Smart Images
Figure CN122392658A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of nanometals, specifically to a method, system, and device for molecular dynamics modeling of porous nanometals based on cellular automata. Background Technology
[0002] Porous nanomaterials possess numerous fine structures, and molecular dynamics (MD) methods are powerful tools for simulating them, capable of clearly describing the evolution of their microstructures. However, porous nanomaterials commonly exhibit complex pore structures, lattice defects, and local phase transitions. Therefore, accurately modeling such complex materials remains a significant challenge in molecular dynamics modeling.
[0003] Currently, common methods for constructing molecular dynamics models of porous nanomaterials include the geometric parameter method and the phase-field equation method. The geometric parameter method generates porous structures by pre-setting geometric parameters such as the shape, size, and location of pores and directly deleting atoms from corresponding regions in the crystal structure, thus offering simplicity and strong controllability. The phase-field method can simulate the natural evolution of pores, but the resulting microstructures are still idealized continuous-field results, making it difficult to reproduce the real pore geometry in actual sintered materials, which possesses strong irregularities, rough interfaces, and multi-scale characteristics. The geometric parameter method relies on regularized geometric shapes to construct pores, failing to describe the natural variations in pore morphology, and requires reprogramming and modeling for different pore morphologies, resulting in insufficient flexibility.
[0004] Therefore, traditional methods suffer from high computational complexity and poor flexibility in molecular dynamics modeling of porous nanomaterials. Especially when simulating the microscopic pore structure and local evolution of nanomaterials, existing techniques consume significant computational resources and struggle to accurately describe the dynamic changes of the material.
[0005] Therefore, there is an urgent need to develop an efficient, flexible, and tunable method for establishing molecular dynamics modeling of porous nanometals with lower computational cost. Summary of the Invention
[0006] The purpose of this disclosure is to provide a method, system, and apparatus for modeling the molecular dynamics of porous nanometals based on cellular automata, so as to solve at least one technical problem in the prior art.
[0007] The technical solution disclosed herein is: A molecular dynamics modeling method for porous nanometals based on cellular automata includes: The porous nanometal system to be simulated is spatially divided into a uniform grid, which serves as the cell. Define any of the cells as being in a finite state over a discrete step size of time, and define the porosity P; Define a 10*10 grid, which has 10*10=100 minimum cells. Each cell has two states: solid and void. The porosity P refers to the proportion of voids in the total grid size. For example, if there are 40 voids and 60 solid minimum cells, the porosity is 40 / 100=0.4.
[0008] The Moore neighbor rule is adopted so that the state update of the cell depends on its current state and the state of its neighboring cells, so as to reflect the microstructure characteristics of porous nanometals. Based on the surrounding state of any given cell at its current position, the state of the next layer is determined, and the cell state is updated and evolved to obtain the final porous nanometal molecular dynamics model.
[0009] The process of dividing the porous nanometal system to be simulated into a uniform grid as cells includes: The size of the cell is preset according to the microscale of porous nanometals; The microscale includes one or more of the following: atomic spacing, grain size, and void size.
[0010] The finite states include: solid states and void states; The solid state indicates that metal is present in the area, and the void state indicates that voids exist in the area.
[0011] The step of determining the state of the next layer based on the surrounding state of the current position of any of the cells and updating and evolving the cell state includes: If any of the cells has fewer than 8P-1 physical neighbors: Then, a random number Q is generated using a random number generator, and the state of the cell at that position in the next layer is determined based on the random number Q, where 0 <= Q <= 1; P is the porosity.
[0012] The random number generator refers to any method that can generate random numbers, such as using a random number generator in Python to generate a floating-point number between 0 and 1.
[0013] import random # Generate a random floating-point number between 0.0 (inclusive) and 1.0 (exclusive). random_float = random.random() When Q>P / 2, the cell at that position in the next layer is a solid state; When Q < P / 2, the cell at that position in the next layer is in a void state.
[0014] The step of determining the state of the next layer based on the surrounding state of the current position of any of the cells and updating and evolving the cell state includes: If any of the cells has more than 8P+1 physical neighbors: Then, a random number Q is generated using a random number generator, and the state of the cell at that position in the next layer is determined based on the random number Q, where 0 <= Q <= 1; P is the porosity.
[0015] When Q>P / 2, the cell at that position in the next layer is in a void state; When Q < P / 2, the cell at that position in the next layer is an entity state.
[0016] A system for modeling the molecular dynamics of porous nanometals based on cellular automata includes: Control unit; The preprocessing unit interacts with the control unit to divide the porous nanometal system to be simulated into a uniform grid as cells; and defines any cell as being in a finite state in the discrete step size of time, and defines the porosity P. The modeling unit interacts with the preprocessing unit to employ the Mohr's neighbor rule, making the state update of each cell dependent on its current state and the states of its neighboring cells, thereby reflecting the microstructural characteristics of porous nanometals. Based on the surrounding state of any cell's current position, the unit determines the state of the next layer, updates and evolves the cell state, and obtains the final molecular dynamics model of the porous nanometals.
[0017] An electronic device based on cellular automata-based molecular dynamics modeling of porous nanometals includes: Storage media, used to store computer programs The processing unit exchanges data with the storage medium and executes the computer program through the processing unit when modeling cellular automata for porous metals, performing the steps of the molecular dynamics modeling method for porous nanometals based on cellular automata as described above.
[0018] A computer-readable storage medium: The computer-readable storage medium stores a computer program. When the computer program is run, it executes the steps of the porous nanometal molecular dynamics modeling method based on cellular automata as described above.
[0019] The beneficial effects of this disclosure include at least the following: The porous nanometal molecular dynamics modeling method based on cellular automata disclosed herein divides the porous nanometal system to be simulated into a uniform grid as cells. Then, it defines that any given cell exists in a finite-state region over time and defines the porosity P. Next, it employs the Mohr's neighbor rule, making the state update of each cell dependent on its current state and the states of its neighboring cells to reflect the microstructural characteristics of the porous nanometal. Finally, based on the surrounding states of any given cell's current position, it determines the state of the next layer, updating and evolving the cell states to obtain the final porous nanometal molecular dynamics model. This cellular automata-based modeling method not only accurately reproduces the formation, growth, and connectivity of pore structures but also allows for flexible adjustment of evolution parameters to meet porosity requirements, achieving highly adaptable modeling for different nanometal materials. Attached Figure Description
[0020] Figure 1 This is a flowchart of the molecular dynamics modeling method for porous nanometals based on cellular automata described in this disclosure; Figure 2 Image of a porous nanometal; Figure 3 A diagram illustrating the cellular automata model; Figure 4 Slice diagram for modeling cellular automata; Figure 5 Generate an image for a local slice; Figure 6 This is a block diagram of the system described in this disclosure. Detailed Implementation
[0021] The present disclosure will now be further explained with reference to the accompanying drawings.
[0022] Terminology Explanation: Cellular automata: Composed of regular cellular grids, each cell in the regular grid takes a finite number of discrete states, follows the same action rules, and is synchronously updated according to determined local rules.
[0023] Porosity: The proportion of voids in a region relative to all other locations.
[0024] Porous nanometals: Porous nanometals are a class of functional metal materials with metal framework grains or pore sizes at the nanoscale, combining the scale effect of nanomaterials with the lightweight and high specific surface area characteristics of porous materials.
[0025] This disclosure aims to address the problems of high computational complexity and poor flexibility in traditional methods for molecular dynamics modeling of porous nanomaterials. Especially when simulating the microscopic pore structure and local evolution of nanomaterials, existing techniques suffer from significant computational resource consumption and difficulty in accurately describing the dynamic changes of materials. This disclosure provides an efficient, flexible, and adjustable modeling method by introducing a cellular automata (CA) model, enabling the establishment of structural models of porous nanomaterials at a lower computational cost. Specific Implementation
[0026] This disclosure provides an embodiment: like Figure 1 and 2 A molecular dynamics modeling method for porous nanometals based on cellular automata, targeting, for example... Figure 2 Modeling the aforementioned porous nanometal physical image includes: S1: Mesh Space Division The porous nanoscale metal system to be simulated is spatially divided into a uniform grid, with each grid unit called a "cell". The size of the cell is set according to the microscopic scale of the nanoscale metal (such as interatomic spacing, grain size, and void size). The size of the cell can be adjusted according to different types of nanoscale metal materials, thereby accurately simulating nanoscale structural features.
[0027] S2: Definition of Cell State and Finite State Each cell can exist in a finite number of states over a discrete time step, mainly including two states: solid state (1) and void state (0). The solid state indicates that metal is present in the region, and the void state indicates that voids are present in the region. By defining these states, the method described in this embodiment can flexibly describe the distribution characteristics of porous nanometal solids and voids.
[0028] S3: Neighbor Rules and Interactions This embodiment employs the Moore's Neighbor rule, where each cell's neighbors are its eight surrounding cells. A cell's state update depends on its current state and the states of its neighboring cells. Through this neighbor-influence mechanism, such as... Figure 3-5 The method described in this embodiment can accurately simulate the local state of solids and voids, reflecting the microstructural characteristics of nanometals.
[0029] S4: Cell state update evolution rules The state update of a cell follows specific evolutionary rules (the specific numbers can vary), as follows: Define porosity P (0 <= P <= 1) For the cell at the current position: If a cell has fewer than 8P-1 solid neighbors, a random number Q (0 <= Q <= 1) is generated using a random number generator. When Q > P / 2, the cell at that position in the next layer is in a solid state; otherwise, it is converted to a void state.
[0030] If a cell has more than 8P-1 solid neighbors, a random number Q is generated using a random number generator. If Q > P / 2, the cell at that position in the next layer is in an empty state; otherwise, it remains in a solid state.
[0031] It should be clarified that the parameters such as mesh size and porosity (state transition threshold) in this embodiment are adjustable. By adjusting these parameters, a balance can be achieved between simulation accuracy and computational complexity. For example, the cell size can be set according to the microscopic properties of different nanometals, and the number of neighbors and influence weights can also be adjusted according to actual needs.
[0032] The molecular dynamics modeling method for porous nanometals based on cellular automata proposed in this embodiment is not only significant in theoretical modeling but also shows broad prospects in practical applications. Porous nanometals, due to their unique specific surface area, pore structure, and excellent physicochemical properties, are widely used in energy storage (such as lithium-ion batteries and capacitors), catalytic reactions, sensors, thermal insulation materials, and adsorption and separation. However, traditional simulation methods suffer from limitations in accuracy and flexibility. This disclosure utilizes an efficient CA modeling method to rapidly generate nanometal models with different porosities, pore size distributions, and lattice structures, and can further predict their dynamic responses under different temperature, pressure, and chemical environments by combining molecular dynamics simulations. This method not only helps to reveal the structure-property relationship of porous nanometal materials but also provides theoretical guidance and simulation support for the design and optimization of novel materials, thereby accelerating their application and promotion in cutting-edge fields such as energy, environment, and functional materials.
[0033] Meanwhile, the model provided in this embodiment can be widely applied to the design and optimization of porous nanomaterials, especially in the fields of energy storage materials, catalytic materials, and thermal insulation materials. By accurately simulating the metal pore structure and interatomic interactions, material performance can be effectively optimized, improving its efficiency and stability in practical applications.
[0034] In summary, this embodiment provides an efficient, flexible, and accurate modeling framework that addresses the shortcomings of traditional methods in molecular dynamics modeling of porous nanometals, and has high application value and broad engineering prospects. Specific Implementation
[0035] This disclosure also provides an embodiment: like Figure 6A system for modeling the molecular dynamics of porous nanometals based on cellular automata includes: a control unit 100, a preprocessing unit 200, and a modeling unit 300. The preprocessing unit 200 interacts with the control unit 100 to divide the space of the porous nanometal system to be simulated into a uniform grid as cells; and defines that any cell is in a finite state in the discrete step size of time, and defines the porosity P. The modeling unit 300 interacts with the preprocessing unit 200 to adopt the Mohs neighbor rule so that the state update of the cell depends on its current state and the state of its neighboring cells to reflect the microstructural characteristics of the porous nanometal; and determines the state of the next layer according to the surrounding state of the current position of any cell, updates and evolves the cell state, and obtains the final molecular dynamics model of the porous nanometal.
[0036] This disclosure also provides an embodiment: An electronic device for molecular dynamics modeling of porous nanometals based on cellular automata includes: a storage medium and a processing unit; wherein the storage medium is used to store a computer program; the processing unit exchanges data with the storage medium and is used to execute the computer program through the processing unit when modeling cellular automata for porous metals, performing the steps of the molecular dynamics modeling method for porous nanometals based on cellular automata as described in Specific Embodiment 1.
[0037] A computer-readable storage medium: the computer-readable storage medium stores a computer program; When the computer program is running, it executes the steps of the porous nanometal molecular dynamics modeling method based on cellular automata as described in Specific Embodiment 1.
[0038] It should be clarified that, in this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.
[0039] The above disclosures only cover a few specific implementation scenarios. However, this disclosure is not limited to these, and any variations that can be conceived by those skilled in the art should fall within the protection scope of this disclosure. The serial numbers in this disclosure are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenarios.
Claims
1. A method for modeling the molecular dynamics of porous nanometals based on cellular automata, characterized in that, include: The porous nanometal system to be simulated is spatially divided into a uniform grid, which serves as the cell. Define any of the cells as being in a finite state over a discrete step size of time, and define the porosity P; The Moore neighbor rule is adopted so that the state update of the cell depends on its current state and the state of its neighboring cells, so as to reflect the microstructure characteristics of porous nanometals. Based on the surrounding state of any given cell at its current position, the state of the next layer is determined, and the cell state is updated and evolved to obtain the final porous nanometal molecular dynamics model.
2. The method for modeling the molecular dynamics of porous nanometals based on cellular automata according to claim 1, characterized in that, The process of dividing the porous nanometal system to be simulated into a uniform grid as cells includes: The size of the cell is preset according to the microscale of porous nanometals; The microscale includes one or more of the following: atomic spacing, grain size, and void size.
3. The method for modeling the molecular dynamics of porous nanometals based on cellular automata according to claim 1, characterized in that: The finite states include: solid states and void states; The solid state indicates that metal is present in the area, and the void state indicates that voids exist in the area.
4. The method for modeling the molecular dynamics of porous nanometals based on cellular automata according to claim 1, characterized in that, The step of determining the state of the next layer based on the surrounding state of the current position of any of the cells and updating and evolving the cell state includes: If any of the cells has fewer than 8P-1 physical neighbors: Then, a random number Q is generated using a random number generator, and the state of the cell at that position in the next layer is determined based on the random number Q, where 0 <= Q <= 1; P is the porosity.
5. The method for modeling the molecular dynamics of porous nanometals based on cellular automata according to claim 4, characterized in that: When Q>P / 2, the cell at that position in the next layer is a solid state; When Q < P / 2, the cell at that position in the next layer is in a void state.
6. The method for modeling the molecular dynamics of porous nanometals based on cellular automata according to claim 1, characterized in that, The step of determining the state of the next layer based on the surrounding state of the current position of any of the cells and updating and evolving the cell state includes: If any of the cells has more than 8P-1 physical neighbors: Then, a random number Q is generated using a random number generator, and the state of the cell at that position in the next layer is determined based on the random number Q, where 0 <= Q <= 1; P is the porosity.
7. The method for modeling the molecular dynamics of porous nanometals based on cellular automata according to claim 6, characterized in that: When Q>P / 2, the cell at that position in the next layer is in a void state; When Q < P / 2, the cell at that position in the next layer is an entity state.
8. A system based on the cellular automata-based molecular dynamics modeling method for porous nanometals according to any one of claims 1-7, characterized in that, include: Control unit; The preprocessing unit interacts with the control unit to divide the porous nanometal system to be simulated into a uniform grid as cells. Furthermore, define any of the aforementioned cells as being in a finite state over a discrete step length of time, and define the porosity P; The modeling unit interacts with the preprocessing unit to employ the Mohr's neighbor rule, making the state update of each cell dependent on its current state and the states of its neighboring cells, thereby reflecting the microstructural characteristics of porous nanometals. Based on the surrounding state of any cell's current position, the unit determines the state of the next layer, updates and evolves the cell state, and obtains the final molecular dynamics model of the porous nanometals.
9. An electronic device based on cellular automata-based molecular dynamics modeling of porous nanometals, characterized in that, include: Storage media, used to store computer programs The processing unit exchanges data with the storage medium and executes the computer program through the processing unit when modeling cellular automata for porous metals, performing the steps of the molecular dynamics modeling method for porous nanometals based on cellular automata as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program. When the computer program is run, it performs the steps of the porous nanometal molecular dynamics modeling method based on cellular automata as described in any one of claims 1-7.