A mineral adsorbs heavy metal molecule dynamics algorithm construction and analysis method

Abstract

The application discloses a mineral adsorption heavy metal molecule dynamics algorithm construction and analysis method, comprising the following steps: S1, comprehensively obtaining information of the mineral and the heavy metal from micro to macro; S2, constructing a physical model based on the characterization result of S1; S3, introducing multi-scale physical parameters, and converting the physical model into a mathematical model; S4, integrating the constructed mathematical model into a software framework, and deeply customizing the software; S5, combining environmental factors and coupling multiple physical fields to perform molecular dynamics simulation; S6, comprehensively analyzing the simulation result; the application constructs an accurate model through multi-level characterization, deeply customizes simulation of multiple physical fields and environmental factors, comprehensively analyzes by using multiple principles and machine learning algorithms, optimizes the model and the calculation efficiency, realizes accurate simulation, in-depth analysis and efficient research on the process of mineral adsorption of heavy metals, and provides strong support for practical application and development of related fields.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of environmental science, and particularly relates to a mineral adsorption heavy metal molecular dynamics algorithm construction and analysis method and system and a storage medium. BACKGROUND

[0002] In the field of environmental science and material science, heavy metal pollution treatment has been an important topic of concern. With the acceleration of industrialization, heavy metal pollutants are discharged into the environment in large quantities, posing a serious threat to the ecosystem and human health. Mineral adsorption as an efficient, economical and environmentally friendly heavy metal pollution treatment method has been widely studied and applied.

[0003] In the early research process, researchers mainly focused on exploring the adsorption effect of minerals on heavy metals at the macro level. For example, through conventional chemical analysis methods, the adsorption capacity of different types of minerals on common heavy metal ions was determined under specific experimental conditions. Although this research method can intuitively understand the removal capacity of minerals for heavy metals, it only stays at the observation of surface phenomena and cannot deeply understand how the adsorption process occurs and the internal mechanism involved.

[0004] With the rapid development of various advanced scientific technologies, a series of advanced spectroscopic techniques have been gradually applied to the research in this field. For example, X-ray photoelectron spectroscopy can accurately determine the chemical valence state of the elements on the surface of the mineral and the change of the element composition. By comparing and analyzing the XPS spectra before and after the adsorption of heavy metals by the mineral, it can be clearly known whether a chemical reaction occurs between the functional groups on the surface of the mineral and the heavy metal ions, and the change of the chemical state of the elements before and after the reaction. At the same time, electron microscopy technology has brought new breakthroughs to the research of mineral adsorption of heavy metals, further exploring the internal relationship between the microstructure of the mineral and the adsorption performance.

[0005] In terms of model construction, the early physical models were relatively simple and idealized. Only the most basic electrostatic attraction between the mineral and the heavy metal ions was considered, while the rich variety of active sites on the surface of the mineral was ignored. In fact, the active sites on the surface of the mineral are diverse, and different active sites have significant differences in adsorption capacity and adsorption mode for heavy metal ions. At the same time, the early models also do not fully consider the hydration shell structure of heavy metal ions in aqueous solution, which will have an important influence on the interaction between heavy metal ions and the surface of the mineral. In addition, the early models also fail to comprehensively and accurately describe the chemical adsorption and physical adsorption involved in the adsorption process.

[0006] In the field of simulation technology, traditional molecular dynamics simulation is often limited to relatively simple simulation environment settings when studying the process of heavy metal adsorption on minerals. Only the basic interaction between minerals and heavy metal ions is considered, without fully considering the influence of various environmental factors on the adsorption process in actual environments. In real natural environments or industrial wastewater treatment scenarios, factors such as the pH value of the solution, ionic strength, and the presence of an external electric field can significantly affect the process of heavy metal adsorption on minerals. For example, the pH value of the solution can change the charge properties and charge density of the mineral surface, thereby affecting the strength of the electrostatic interaction between the mineral and the heavy metal ion; changes in ionic strength can affect the hydration shell structure of the heavy metal ion and the migration rate of the ion in the solution; the presence of an external electric field can change the direction of motion and migration rate of the heavy metal ion, thereby promoting or inhibiting the adsorption process. Today, as research into the process of heavy metal adsorption on minerals deepens, researchers recognize the need to consider these complex environmental factors comprehensively. By coupling various physical fields such as electromagnetic fields and temperature fields, a simulation system that is more realistic is established to accurately simulate the process of heavy metal adsorption on minerals under different conditions. However, in actual operation, accurately mapping complex physical models into mathematical models and successfully integrating them into appropriate molecular dynamics software frameworks still faces many technical difficulties and challenges. For example, how to accurately quantify and parameterize various interactions, how to reasonably introduce multi-scale physical parameters into the mathematical model, and how to ensure that the software framework can efficiently and stably run complex simulation calculations. In addition, the analysis of simulation results also requires the use of multidisciplinary analysis methods for comprehensive and in-depth analysis.

[0007] In summary, although statistical mechanics, thermodynamics, and dynamics principles have been used to analyze the energy changes, kinetic characteristics, and thermodynamic equilibrium of the adsorption process, as well as the structural evolution and stability of the system after adsorption, there is still room for improvement in the perfection and accuracy of the analysis methods. SUMMARY

[0008] To overcome the above-mentioned deficiencies in the prior art, the present application provides a mineral adsorption heavy metal molecular dynamics algorithm construction and analysis method to solve the above problems.

[0009] To achieve the above-mentioned purposes, the technical solutions provided by the preferred embodiments of the present application are as follows: a mineral adsorption heavy metal molecular dynamics algorithm construction and analysis method, the method comprising:

[0010] Step S1, multi-level characterization of minerals and heavy metals, covering from the microscopic atomic and molecular level to the macroscopic material property level, using spectroscopic techniques to analyze the chemical state of mineral surface functional groups and heavy metal ions, and using electron microscopy techniques to observe the microstructure and surface morphology of minerals;

[0011] Step S2, according to the characterization results, design and build a physical model of mineral adsorbing heavy metals, the physical model includes active sites on the surface of the mineral, hydration shell structure of heavy metal ions, and interaction mechanism between the two;

[0012] Step S3, based on the constructed physical model, map it to a mathematical model by introducing multi-scale physical parameters, and integrate information of different scales into a unified mathematical framework;

[0013] Step S4, select a molecular dynamics software framework, integrate the mathematical model into the framework, and customize the software framework by adding functional modules to achieve deep customization, accurate simulation of the adsorption process and handling of special physical phenomena;

[0014] Step S5, combine environmental factors and couple multiple physical fields to perform molecular dynamics simulation for simulating the adsorption process under different conditions;

[0015] Step S6, comprehensive analysis of simulation results, specific analysis methods include statistical mechanics, thermodynamics and dynamics principles, analysis of energy change, kinetic characteristics and thermodynamic equilibrium of the adsorption process, and structure evolution and stability of the system after adsorption.

[0016] In the present application, in step S2, the interaction mechanism includes electrostatic interaction, chemical adsorption and physical adsorption;

[0017] In combination with step S2 and step S3, in the step of mapping the physical model to the mathematical model, Poisson-Boltzmann equation is used for electrostatic interaction:

[0018]

[0019] Where, is the electrostatic potential, ρ is the charge density, ∈ is the dielectric constant;

[0020] This equation is used to describe the electrostatic interaction between minerals and heavy metal ions, considering the influence of charge distribution on electrostatic potential.

[0021] In the present application, after selecting the molecular dynamics software framework in step S4, the system is described using Hamiltonian mechanics principle, and the Hamiltonian H of the system can be represented as:

[0022] H = T + V;

[0023] where T is kinetic energy, and V is potential energy;

[0024] where pi is the momentum of particle i, and mi is the mass of particle i;

[0025] The potential energy V includes intermolecular interaction potential energy, electrostatic potential energy and external field potential energy, and its specific form is determined in combination with the physical model.

[0026] In the present application, the step S5 uses the Nose-Hoover chain heat bath algorithm for time evolution of the system when performing molecular dynamics simulation, and the motion equation is:

[0027]

[0028] where pi is the momentum of particle i, qi is the position of particle i, εj is the heat bath variable, Qj is the mass parameter of the heat bath, K B is the Boltzmann constant, T is the temperature, and g is the degree of freedom;

[0029] The above algorithm is used to control the temperature of the simulated system to ensure that the system is in the required thermal equilibrium state.

[0030] In the present application, the step S6 calculates the Gibbs free energy G of the system when comprehensively analyzing the simulation results, and uses the thermodynamic relationship:

[0031] G=H-TS;

[0032] where H is the enthalpy, T is the temperature, and S is the entropy;

[0033] S is calculated according to the classical statistical mechanics formula: S=-k B ∑ i p i Inp i , where p i is the probability of the initial microstate i of the system.

[0034] In the present application, the step S2 considers the topological structure of the mineral surface during the process of designing and constructing the physical model of the mineral adsorbing heavy metals, and by introducing the fractal geometry theory, the mineral surface is regarded as a fractal object to describe the surface roughness and the distribution characteristics of active sites;

[0035] Specifically, the box counting method is used to determine the fractal dimension D, and the formula is:

[0036] N(∈)∝∈ -D

[0037] where N(∈) is the number of boxes with side length ∈ required to cover the mineral surface;

[0038] The step S2 obtains the fractal dimension D by calculating N(ε) under different ε and performing linear fitting, so as to accurately represent the complexity of the mineral surface and the distribution of active sites.

[0039] In the application, the parameters related to surface interaction in the physical model are adjusted in combination with the fractal dimension D, so that the physical model accurately reflects the adsorption characteristics of the actual mineral surface.

[0040] In the application, when selecting the molecular dynamics software framework, the parallel computing module of the software is optimized in a combination of task parallelism and data parallelism.

[0041] For task parallelism, the molecular dynamics simulation process is divided into multiple independent sub-tasks, and the sub-tasks are distributed to different computing cores, and the message passing interface is used for communication and coordination between tasks.

[0042] For data parallelism, the particle data in the simulation system is divided into multiple data blocks, and the data blocks are distributed to different computing units, and the unified computing device architecture is used for parallel computing.

[0043] According to the characteristics of different hardware architectures and computing tasks, the allocation strategies of tasks and data are dynamically adjusted.

[0044] In the application, the plurality of physical fields in the step S5 include but are not limited to electromagnetic field and temperature field.

[0045] The environmental factors include pH value, ionic strength and external electric field.

[0046] When performing molecular dynamics simulation, the coupling of different environmental factors is simulated by using a multi-physical field coupling algorithm to simulate the adsorption process in the actual environment.

[0047] In the application, the step S6 uses a machine learning algorithm to assist in analysis, specifically including the following steps:

[0048] Step S6.1, the characteristic quantity in the simulation result is taken as an input feature, and the target performance is taken as an output label to construct a data set.

[0049] Step S6.2, a neural network model is constructed using the TensorFlow deep learning framework, a multi-layer perception and a convolutional neural network are selected as the network structure, and appropriate number of layers and neurons are selected according to the size and characteristics of the data set.

[0050] Step S6.3, a stochastic gradient descent algorithm is used as an optimizer to train the model by minimizing the loss function, and in the training process, the data set is divided into a training set, a validation set and a test set, the overfitting of the model is monitored through the validation set, and the performance of the model is evaluated through the test set.

[0051] Step S6.4, using the trained model to predict the adsorption performance under different conditions, and by analyzing the weights and activation functions of the neural network, the potential physical mechanism in the adsorption process is excavated.

[0052] Advantages:

[0053] The present application can accurately simulate the process of mineral adsorbing heavy metals by multi-level characterization of minerals and heavy metals, building a physical model containing detailed interaction mechanisms, accurately mapping to a mathematical model, using molecular dynamics software framework for deep customization simulation, and combining various physical fields and environmental factors. At the same time, using statistical mechanics, thermodynamics and dynamics principles and machine learning algorithms for comprehensive analysis, not only can the energy change, kinetic characteristics and thermodynamic equilibrium of the adsorption process be understood in depth, but also the potential physical mechanism can be excavated, providing a comprehensive and in-depth analysis perspective for the research of mineral adsorbing heavy metals.

[0054] In building the physical model, the present application considers the mineral surface topological structure, introduces the fractal geometry theory, accurately characterizes the complexity of the mineral surface and the distribution of active sites, and then adjusts the physical model parameters, so that the model can accurately reflect the adsorption characteristics of the actual mineral surface. In performing molecular dynamics simulation, multi-physics field coupling algorithm is used for different environmental factors, and parallel computing module optimization is performed when selecting molecular dynamics software framework. These measures make the algorithm model have stronger adaptability and accuracy, and can better simulate the process of mineral adsorbing heavy metals under different conditions, providing reliable theoretical support for practical application.

[0055] The present application also selects a suitable molecular dynamics software framework, and uses a combination of task parallelism and data parallelism optimization for the parallel computing module, dynamically adjusts the distribution strategy according to different hardware architectures and computing task characteristics, greatly improves the efficiency of simulation calculation. At the same time, combined with machine learning algorithm auxiliary analysis, using neural network model for adsorption performance prediction and potential mechanism excavation, further improves the performance of the algorithm. This makes it possible to obtain more accurate results in a shorter time when studying mineral adsorbing heavy metals, providing strong technical support for the rapid development of related fields.

[0056] In summary, the present application builds an accurate model through multi-level characterization, combines multi-physics field and environmental factors for deep customization simulation, uses various principles and machine learning algorithms for comprehensive analysis, and optimizes the model and calculation efficiency, realizes accurate simulation, in-depth analysis and efficient research of the process of mineral adsorbing heavy metals, and provides strong support for practical application and development of related fields. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and for those of ordinary skill in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0058] Figure 1 The step flow chart of the mineral adsorbing heavy metal molecular dynamics algorithm construction and analysis method of the present application. DETAILED DESCRIPTION

[0059] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict, and the present application will be further described in detail below in combination with the drawings and specific embodiments.

[0060] As Figure 1 shown, in the present embodiment, a mineral adsorbing heavy metal molecular dynamics algorithm construction and analysis method includes the following steps:

[0061] The method includes:

[0062] Step S1, multi-level characterization of the mineral and heavy metal, covering from the microscopic atomic and molecular level to the macroscopic material property level, using spectral technology to analyze the chemical state of the mineral surface functional group and heavy metal ion, and using electron microscope technology to observe the microstructure and surface morphology of the mineral;

[0063] In the present embodiment, X-ray photoelectron spectroscopy is specifically used to analyze the chemical state of the mineral surface functional group, which can accurately determine the type of chemically active sites on the mineral surface, such as hydroxyl, carboxyl, etc., and their possible interaction forms with heavy metal ions, at the same time, electron microscope technology, including scanning electron microscope and transmission electron microscope, is used to observe the microstructure of the mineral, such as crystal structure defects, crystal face orientation, and surface morphology, such as roughness, pore structure, etc.; at the macroscopic material property level, the overall property information of the mineral can be obtained through specific surface area analysis, density measurement, etc.; these characterization results provide a rich data basis for the subsequent construction of physical models.

[0064] Step S2, according to the characterization results, design and construct a physical model of the mineral adsorbing heavy metal, the physical model includes the active sites on the mineral surface and the hydration shell structure of the heavy metal ion, the active sites not only include different types of functional groups, but also consider their spatial distribution and activity degree;

[0065] For heavy metal ions, the hydration shell structure is considered, which involves the arrangement of water molecules around the ion and the formation of hydrogen bond network, which will affect the activity and diffusion ability of the ion;

[0066] and the interaction mechanism between the above two;

[0067] The interaction mechanism in the embodiment includes electrostatic interaction, chemical adsorption and physical adsorption; the electrostatic interaction is based on the charge property of ions, the chemical adsorption involves the formation of chemical bonds between ions and functional groups on the mineral surface, and the physical adsorption considers weak interactions such as van der Waals force; by comprehensively considering these factors, a physical model that can reflect the actual physical process is constructed;

[0068] In the step S2, in the process of designing and constructing the physical model of heavy metal adsorption on the mineral, the topological structure of the mineral surface is considered, the mineral surface is regarded as a fractal object by introducing the fractal geometry theory, and the surface roughness and the distribution characteristics of active sites are described;

[0069] The box counting method is specifically used to determine the fractal dimension D, and the formula is:

[0070] N(∈)∝∈ -D

[0071] Wherein, N(∈) is the number of boxes with side length ∈ required to cover the mineral surface;

[0072] The step S2 obtains the fractal dimension D by calculating N(∈) under different ∈ and performing linear fitting, and the fractal dimension D is used to accurately characterize the complexity of the mineral surface and the distribution of active sites;

[0073] The fractal dimension D is combined to adjust the parameters related to surface interaction in the physical model, such as the activity coefficient of adsorption sites; when the fractal dimension D is larger, it indicates that the mineral surface is rougher and the distribution of active sites is more complex, and the activity coefficient of adsorption sites is correspondingly increased to reflect the influence of the complex structure of the actual mineral surface on the adsorption characteristics, so that the physical model accurately reflects the adsorption characteristics of the actual mineral surface;

[0074] In step S3, based on the constructed physical model, the multi-scale physical parameters are introduced to map the physical model to the mathematical model, and the information of different scales is integrated into a unified mathematical framework;

[0075] In the step of mapping the physical model to the mathematical model, for electrostatic interaction, the Poisson-Boltzmann equation is used in combination with step S2 and step S3:

[0076]

[0077] Wherein, is the electrostatic potential, ρ is the charge density, and ∈ is the dielectric constant;

[0078] The equation is used to describe the electrostatic interaction between the mineral and the heavy metal ion, can accurately describe the electrostatic potential distribution between the mineral and the heavy metal ion, and considers the influence of the unevenness of the charge distribution on the electrostatic potential; by introducing multi-scale physical parameters such as atomic-level charge parameters, molecular-level bond length and bond angle parameters, and macroscopic dielectric constant, the information of different scales is integrated into a unified mathematical framework; different interactions such as intermolecular interaction, electrostatic interaction and external field interaction are quantified by different mathematical expressions to form a complete mathematical model, which provides an accurate mathematical basis for subsequent molecular dynamics simulation.

[0079] Step S4, selecting a molecular dynamics software framework, specifically using LAMMPS; integrating the mathematical model into the framework, and deeply customizing the software framework by adding functional modules for accurate simulation of the adsorption process and processing of special physical phenomena;

[0080] In step S4 of the embodiment, the Hamiltonian principle is used to describe the system, and the Hamiltonian H of the system can be represented as:

[0081] H=T+V;

[0082] Wherein, T is kinetic energy, and V is potential energy;

[0083] Wherein, pi is the momentum of particle i, and mi is the mass of particle i;

[0084] The potential energy V includes intermolecular interaction potential energy, electrostatic potential energy and external field potential energy, and its specific form is determined in combination with the physical model;

[0085] The intermolecular interaction potential energy can be determined according to the intermolecular force field, such as Lennard-Jones potential energy, the electrostatic potential energy is obtained according to the calculation result of the above Poisson-Boltzmann equation, and the external field potential energy considers the contribution of external factors such as external electric field; the mathematical model is integrated into the software framework, and the software framework is deeply customized; specific functional modules are added, for example, a special potential function module for mineral adsorption heavy metal system is developed, which is used to process the complex interaction between different types of minerals and heavy metal ions; a monitoring module for structural changes in the adsorption process is added, which can record the changes of adsorption sites and the changes of adsorption capacity in real time, etc.

[0086] In the selection of the molecular dynamics software framework in the embodiment, the parallel computing module of the software is optimized by combining task parallelism and data parallelism;

[0087] For parallel processing, the molecular dynamics simulation process is decomposed into multiple independent sub-tasks, such as calculating inter-particle forces, updating particle positions, and calculating the total system energy. For force calculation, different calculation methods are used according to different distance ranges; direct summation is used for short-range forces, while fast multipole moment methods are used for long-range forces. Sub-tasks are assigned to different computing cores, and a message passing interface is used for communication and coordination between tasks to ensure data synchronization and computational coordination between the cores.

[0088] For data parallelism, particle data in the simulation system is divided into multiple data blocks. Based on the distribution of particles in space, these data blocks are allocated to different computing units, such as GPU threads. For GPU parallel computing, a unified computing device architecture or an open computing language is used. The allocation strategy for tasks and data is dynamically adjusted according to different hardware architectures and the characteristics of computing tasks. For example, on GPUs with high parallel computing capabilities, more data blocks can be allocated to their processing to maximize the utilization of computing resources and improve the performance and scalability of simulation computing.

[0089] Step S5: Combining environmental factors and coupling multiple physical fields, including pH value, ionic strength and applied electric field, and multiple physical fields including but not limited to electromagnetic field and temperature field, perform molecular dynamics simulation to simulate the adsorption process under different conditions.

[0090] When performing molecular dynamics simulations, a multiphysics coupling algorithm is used to realistically simulate the adsorption process under actual conditions by coupling different environmental factors.

[0091] For the coupling of electromagnetic fields and molecular dynamics, the Lorentz force F = q(E + v*B) is used, where q is the particle charge, E is the electric field strength, v is the particle velocity, and B is the magnetic induction intensity. The force acting on the particle is updated by adding this force to the Newtonian equations of motion for molecular dynamics. total =F mol +F; where F mol These are intermolecular forces. Simultaneously, Maxwell's equations are employed. and Where ρ is the charge density, μ0 is the vacuum permeability, ε0 ​​is the vacuum permittivity, and J is the current density; these are used to describe the evolution of the electromagnetic field, achieving bidirectional coupling between the electromagnetic field and molecular dynamics. For the coupling of the temperature field and molecular dynamics, the energy balance equation is used. Where C u We consider the evolution of the temperature field by using the isochoric specific heat capacity and q as the heat flux density, and by modifying the energy equation of molecular dynamics, E = E mol +E thermal ; where E mol It is the intermolecular interaction energy, E thermalis thermal energy, to consider thermal effects, and to realize the coupling of temperature field and molecular dynamics; in this way, the adsorption process under different temperature, electric field and magnetic field environments can be simulated, and the influence of environmental factors on the adsorption process can be studied;

[0092] In step S5 of the embodiment, when performing molecular dynamics simulation, the Nose-Hoover chain heat bath algorithm is used for time evolution of the system, and the motion equation is:

[0093]

[0094] where pi is the momentum of particle i, qi is the position of particle i, εj is the heat bath variable, Qj is the mass parameter of the heat bath, K B is the Boltzmann constant, T is the temperature, and g is the degree of freedom.

[0095] The above algorithm adjusts the heat bath variable to accurately control the temperature of the simulated system, so that the system is in the required thermal equilibrium state, and the accuracy and reliability of the simulation results are ensured.

[0096] In step S6, the simulation results are comprehensively analyzed, and the specific analysis methods include statistical mechanics, thermodynamics and dynamics principles, and the energy change, kinetic characteristics and thermodynamic equilibrium of the adsorption process are analyzed, as well as the structural evolution and stability of the system after adsorption.

[0097] In step S6 of the embodiment, the Gibbs free energy G of the system is calculated, and the thermodynamic relationship is used:

[0098] G=H-TS;

[0099] where H is the enthalpy, T is the temperature, and S is the entropy.

[0100] S is calculated according to the classical statistical mechanics formula: S=-k B ∑ i p i Inp i , where p i is the probability of the initial microstate i of the system; by calculating the Gibbs free energy, the spontaneity and thermodynamic feasibility of the adsorption process are judged, and key indicators are provided for energy analysis of the adsorption process.

[0101] The kinetic characteristics of the adsorption process are analyzed, for example, by calculating the mean square displacement (MSD), where: to study the diffusion behavior of particles, and by calculating the velocity autocorrelation function C u (t)=<V (t) *V (0) > to analyze the correlation of particle motion, and then deduce the adsorption kinetics process.

[0102] Step S6 utilizes machine learning algorithms for analysis and specifically includes the following steps:

[0103] Step S6.1: Use the feature quantities in the simulation results as input features, including adsorption energy, adsorption amount, particle position and velocity, etc.; use the target performance (such as adsorption rate, adsorption equilibrium time and adsorption stability, etc.) as output labels to construct a dataset.

[0104] Step S6.2: Use the TensorFlow deep learning framework to build a neural network model. Based on the complexity of the input features and output labels, select multilayer perceptron and convolutional neural network as the network structure, and select an appropriate number of layers and neurons according to the size and features of the dataset.

[0105] Step S6.3: Use stochastic gradient descent as the optimizer to train the model by minimizing the loss function. During the training process, the dataset is divided into a training set (70%), a validation set (20%), and a test set (10%). The dataset is divided into training, validation, and test sets. The overfitting of the model is monitored through the validation set, and the performance of the model is evaluated based on the test set.

[0106] Step S6.4: Using the trained model to predict the adsorption performance under different conditions can not only provide a reference for experimental design, but also, by analyzing the weights and activation functions of the neural network, uncover the potential physical mechanisms in the adsorption process, providing a basis for further optimization of physical and mathematical models.

[0107] Example

[0108] Preparation of experimental materials and equipment:

[0109] For mineral materials, illite was selected as the adsorbent mineral. The illite was ground into powder and sieved to obtain particles with uniform particle size, with an average particle size of about 100 μm.

[0110] Heavy metal ions, use lead ions (Pg) 2+ The target heavy metal ion was dissolved in an aqueous solution of a certain concentration to form a lead ion solution with a concentration of 0.1 mol / L.

[0111] Instruments and equipment:

[0112] Spectroscopic analysis equipment, X-ray photoelectron spectrometer (XPS) and Fourier transform infrared spectrometer (FTIR).

[0113] Electron microscope, scanning electron microscope (SEM), and transmission electron microscope (TEM).

[0114] Molecular dynamics simulation software, Intel Xeon E5-2690 processor, each processor has 10 cores and GPU acceleration card, such as NVIDIA Tesla V100, to meet the high-performance computing needs.

[0115] Data analysis and machine learning software, using Python programming language and TensorFlow algorithm.

[0116] The specific steps are:

[0117] Step S1, use XPS to analyze the surface of illite, find that there are a large number of hydroxyl (-OH) and silicon oxygen tetrahedral structure on its surface, and further confirm the vibration mode of functional groups through FTIR spectrum, which shows that there are Si-O-Si and Al-O chemical bonds;

[0118] Use TEM to observe the microstructure of illite, which shows a typical layered structure, and the interlayer spacing is about Observe its surface morphology by SEM, and find that it has a certain roughness;

[0119] Use specific surface area analyzer to measure the specific surface area of illite, and get its specific surface area about 300m 2 / g, and determine the density of illite about 2.5g / cm 3 ;

[0120] Step S2, according to the characterization results, construct the physical model of illite adsorbing lead ions, consider the layered structure and surface functional groups of illite, put lead ions in the solution environment, consider its hydration shell structure; for electrostatic interaction, the charge of lead ions will produce electrostatic attraction with the negative charge sites on the surface of illite; chemical adsorption may occur between hydroxyl and lead ions, forming coordination bond. Physical adsorption is based on van der Waals force, considering the short-range attraction between lead ions and illite surface;

[0121] Introduce fractal geometry theory to analyze the surface of illite, determine the fractal dimension by box counting method, divide the surface of illite into different grid length ∈, and count the number of boxes N(∈) required to cover its surface, in the case of ∈ value range from to , through multiple measurements of N(∈) under different conditions, and linear fitting, the fractal dimension is about 2.3; according to this fractal dimension, improve the activity coefficient of adsorption site, to reflect the influence of illite surface roughness on adsorption;

[0122] Step S3, according to the charge distribution of mineral surface and the charge distribution of lead ions, determine the charge density as 1.5×10 - 6 C / m 3and the distribution of electrostatic potential is solved according to the dielectric constant 80 of the aqueous solution; the Hamiltonian of the system is calculated according to the mass 200.59 amu of the lead ion and the mass of the illite atom and the initial velocity 80 m / s;

[0123] Step S4, integrating the above mathematical model into LAMMPS, developing a special potential function module for the illite-lead ion system, adding a structure change monitoring module, which can record the distribution of adsorption sites of lead ions on the surface of illite and the change of adsorption amount with time in real time;

[0124] Step S5, setting the initial temperature to 300 K, using the Nose-Hoover chain heat bath algorithm to control the temperature, and setting the heat bath mass parameter to 1.0;

[0125] The initial ion strength is set to 0.1 mol / L, and the lead ion solution and illite particles are placed in a simulation box under periodic boundary conditions, and the size of the simulation box is set to

[0126] The intensity of the applied electric field is set to 100 V / m, and the intensity of the magnetic field is set to 0.1 T, and the coupling of electromagnetic field and molecular dynamics is realized through Lorentz force and Maxwell equations;

[0127] The initial pH value is set to 7, which is realized by adjusting the proton concentration, and the changes of surface charge of illite and chemical form of lead ions under different pH values are considered;

[0128] Start the molecular dynamics simulation, set the time step to 1 fs, and run the simulation for 100 ps; according to the motion equation of the Nose-Hoover chain heat bath algorithm and the control temperature, ensure that the system is in thermal equilibrium state; at the same time, through the coupling of energy balance equation and modified energy equation (assuming C u is 1 J / (mol·K)), considering the coupling of temperature field and molecular dynamics, updating the energy and temperature of the system in real time;

[0129] Step S6, the Gibbs free energy results calculated at different simulation time points show that the G value gradually decreases during the adsorption process, indicating that the adsorption process is spontaneous in thermodynamics;

[0130] The diffusion behavior of lead ions on the surface of illite is analyzed, and it is found that the MSD of lead ions gradually decreases with time, indicating that the movement is limited and tends to be adsorbed; at the same time, the velocity autocorrelation function is calculated, which shows the change of correlation of lead ion movement, reflecting the change of dynamic behavior in the adsorption process.

[0131] The above merely provides the preferred embodiments of the present application, and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the principles and technical scope of the present application shall fall into the scope of the present application.

Claims

1. A mineral adsorbing heavy metal molecule dynamics algorithm construction and analysis method, characterized in that, The method comprises: Step S1, multi-level characterization of minerals and heavy metals, covering from the microscopic atomic and molecular level to the macroscopic material property level, using spectral technology to analyze the chemical state of mineral surface functional groups and heavy metal ions, and using electron microscope technology to observe the microstructure and surface morphology of the minerals; Step S2, according to the characterization results, design and build a physical model of mineral adsorbing heavy metals, the physical model includes active sites on the mineral surface, hydration shell structure of heavy metal ions, and interaction mechanism of active sites on the mineral surface and hydration shell structure of heavy metal ions; in the process of designing and building the physical model of mineral adsorbing heavy metals, the topological structure of the mineral surface is considered, the mineral surface is regarded as a fractal object by introducing fractal geometry theory, which is used to describe the surface roughness and distribution characteristics of active sites; The box-counting method is used to determine the fractal dimension The formula is: ； wherein, is the number of boxes of side length required to cover the surface of the mineral. The step S2 is to calculate the different and perform linear fitting to obtain the fractal dimension for accurately characterizing the complexity of the mineral surface and the distribution of active sites.​ Step S3, the physical model is combined with multi-scale physical parameter mapping into a mathematical model, and information of different scales is integrated into a unified mathematical framework; Step S4, select a molecular dynamics software framework, integrate the mathematical model into the framework, and add a functional module to the software framework for deep customization; Step S5, combined with environmental factors and coupled with multiple physical fields, execute the molecular dynamics simulation process to simulate the adsorption process under different conditions; Step S6, comprehensive analysis of the simulation results, specific analysis methods include statistical mechanics, thermodynamics and dynamics principles, analysis of energy change, kinetic characteristics and thermodynamic equilibrium of the adsorption process, and structure evolution and stability of the system after adsorption; When selecting a molecular dynamics software framework, the parallel computing module of the software is optimized by combining task parallelism and data parallelism; For task parallelism, the molecular dynamics simulation process is divided into multiple independent subtasks, and the subtasks are distributed to different computing cores, and the message passing interface is used for communication and coordination between tasks; For data parallelism, the particle data in the simulation system is divided into multiple data blocks, and the data blocks are distributed to different computing units, and the unified computing device architecture is used for parallel computing; According to the characteristics of different hardware architectures and computing tasks, the allocation strategy of tasks and data is dynamically adjusted.

2. The method according to claim 1, wherein the method is characterized by, In step S2, the interaction mechanism includes electrostatic interaction, chemical adsorption and physical adsorption; In step S2 and step S3, in the step of mapping the physical model to the mathematical model, for electrostatic interaction, Poisson-Boltzmann equation is used: ； wherein is the electrostatic potential, is the charge density, is the dielectric constant; This equation is used to describe the electrostatic interaction between minerals and heavy metal ions, considering the influence of charge distribution on electrostatic potential.

3. The method of claim 1, wherein the mineral adsorbing heavy metal molecules dynamics algorithm is constructed and analyzed. After the step S4 of selecting the molecular dynamics software framework, a Hamiltonian principle is used to describe the system, and a Hamiltonian of the system may be expressed as: ； wherein, is kinetic energy, is potential energy; wherein, is a particle of momentum, is a particle of mass; Potential energy The potential energy includes intermolecular interaction potential energy, electrostatic potential energy, and external field potential energy, the specific forms of which are determined in combination with the physical model.

4. The method of claim 1, wherein the method is characterized by, In step S5, when performing molecular dynamics simulation, for the time evolution of the system, Nose-Hoover chain heat bath algorithm is used, and its motion equation is: ， ； wherein is the momentum of a particle , is the position of a particle , is a thermal bath variable, is a mass parameter of the thermal bath, is the Boltzmann constant, is the temperature, is a degree of freedom; The above algorithm is used to control the temperature of the simulation system to ensure that the system is in the required thermal equilibrium state.

5. The method of claim 1, wherein the mineral adsorbing heavy metal molecules dynamics algorithm construction and analysis method is characterized by, The step S6 synthesizes and analyzes the simulation results, and calculates the Gibbs free energy of the system using the thermodynamic relation: ； wherein, is enthalpy, is temperature, is entropy; According to the classical statistical mechanics formula: where is the probability of the initial microscopic state of the system .

6. The method of claim 1, wherein the mineral adsorbing heavy metal molecules dynamics algorithm construction and analysis method is characterized by, Combining fractal dimension The parameters related to the surface interaction in the physical model are adjusted so that the physical model accurately reflects the adsorption characteristics of the actual mineral surface.

7. The method of claim 1, wherein the mineral adsorbing heavy metal molecules dynamics algorithm construction and analysis method is characterized by, In step S5, the multiple physical fields include but are not limited to electromagnetic field and temperature field; The environmental factors include pH value, ionic strength and external electric field; In the execution of molecular dynamics simulation, the coupling of different environmental factors is simulated by using a multi-physics coupling algorithm to simulate the adsorption process in the actual environment.

8. The method of claim 1, wherein the mineral adsorbing heavy metal molecules dynamics algorithm construction and analysis method is characterized by, The step S6 uses a machine learning algorithm to assist in analysis, specifically including the following steps: Step S6.1, the characteristic quantity in the simulation result is taken as the input feature, and the target performance is taken as the output label to construct a data set; Step S6.2, a neural network model is built using the TensorFlow deep learning framework, a multilayer perceptron and a convolutional neural network are selected as the network structure, and appropriate number of layers and neurons are selected according to the size and characteristics of the data set; Step S6.3, using the stochastic gradient descent algorithm as the optimizer, the model is trained by minimizing the loss function, in the training process, the data set is divided into training set, validation set and test set, the overfitting of the model is monitored through the validation set, and the performance of the model is evaluated according to the test set; Step S6.4, using the trained model to predict the adsorption performance under different conditions, and through the analysis of the weights and activation functions of the neural network, the potential physical mechanism in the adsorption process is excavated.

Images