Material physical property calculation and simulation method and system based on first principle

Abstract

The invention discloses a material physical property calculation and simulation method and system based on a first principle, and relates to the field of material physical property calculation and simulation, and the method comprises the following operation steps: S1, atomic-scale structure modeling and initial parameter setting; s2, structure optimization and energy minimization are carried out; s3, electronic structure calculation and intrinsic physical property extraction; s4, performing multi-scale coupling and mesostructure simulation; s5, simulating a multi-physics coupling process; s6, data-driven intelligent optimization is carried out; s7, carrying out multi-dimensional visual verification and interaction; and S8, carrying out full life cycle model iteration and knowledge base construction. According to the material physical property calculation and simulation method and system based on the first principle, full-process simulation from an atomic scale to a macroscopic material is achieved, atomic-scale structure modeling, multi-scale coupling and macroscopic process simulation are covered, physical properties and behaviors of the material under different scales can be deeply revealed, and the material physical property calculation and simulation method and system based on the first principle are provided for the purpose of improving the material physical property calculation and simulation efficiency. And more complete and accurate information can be provided for material design and process optimization.

Description

Technical Field

[0001] The present invention relates to the field of calculation and simulation of material properties, and in particular to a method and system for calculation and simulation of material properties based on first principles. Background Art

[0002] Material properties refer to the basic physical, chemical, mechanical and other properties inherent in materials, which are the basis of the existence and movement of matter.

[0003] In existing technologies, atomic-scale simulations and macro-scale simulations are often separated, making it difficult to accurately describe the performance changes and interactions of materials at different scales. Traditional simulation methods can usually only consider a single physical field or simple multi-field coupling, and cannot accurately simulate the complex multi-physical field interactions in practical applications. Traditional material research and development methods mainly rely on trial and error, requiring a large number of experiments and tests, with long R&D cycles and high costs.

[0004] Therefore, it is necessary to propose a material property calculation and simulation method and system based on first principles to solve the above problems. Summary of the Invention

[0005] The main purpose of the present invention is to provide a material property calculation and simulation method and system based on first principles, which can effectively solve the problems in the background technology.

[0006] To achieve the above object, the technical solution adopted by the present invention is: A material property calculation and simulation method based on first principles includes the following steps: S1: Atomic-level structural modeling and initial parameter setting, used to establish the atomic-level initial structure of the material; S2: Structural optimization and energy minimization, used to obtain the lowest energy stable atomic structure and eliminate the geometric deviation of the initial modeling; S3: Electronic structure calculation and intrinsic property extraction, used to reveal the nature of the electronic behavior of materials and predict the electrical, optical, and magnetic intrinsic properties; S4: Multiscale coupling and mesoscopic structure simulation, used to bridge the atomic scale and macroscopic properties, revealing the influence of the mesostructure of grain boundaries, defects, and phase boundaries on the macroscopic physical properties of materials; S5: Multi-physics coupled process simulation, used to predict the impact of different process parameters on material growth quality in a virtual environment; S6: Data-driven intelligent optimization, used to achieve an automated closed-loop calculation, simulation, and optimization process. It uses machine learning models to quickly identify the optimal doping concentration and reduce material warpage. S7: Multi-dimensional visual verification and interaction, used to provide engineers with intuitive data analysis tools; S8: Full life cycle model iteration and knowledge base construction to build enterprise-level material design capability barriers.

[0007] Preferably, the S1 specifically includes: using SHINE software to construct a material atomic model, supporting three-dimensional modeling of crystal, molecular, and cluster structures, importing CIF and XYZ format files, and manually defining lattice parameters; The calculation parameters are customized based on the quantum mechanics calculation engine, defining the exchange-correlation functional, cutoff energy, K-point grid, and taking into account spin polarization and van der Waals corrections.

[0008] Preferably, the S2 specifically includes: using the conjugate gradient method to perform geometry optimization, minimizing the interatomic forces and total energy, iterating until the lattice parameters and atomic positions are stable, and introducing stress boundary conditions for thin film materials to simulate the stress state in actual growth.

[0009] Preferably, the S3 specifically includes the following steps: S301: Based on the structure optimized in S2, calculate the material's band structure, state density, partial-wave state density, and extract the band gap width, valence band top, conduction band bottom position, and electron distribution near the Fermi level; S302: Calculate carrier mobility and conductivity using the Boltzmann transport equation, and calculate optical absorption spectrum and refractive index using dielectric function; S303: For magnetic materials, calculate the magnetic moment, exchange coupling constant, and analyze ferromagnetic and antiferromagnetic order.

[0010] Preferably, the S4 specifically includes the following steps: S401: Atomic scale data dimensionality reduction, inputting the material parameters obtained in S3 into the phase field method; S402: Mesoscopic structure, for thin film growth, using kinetic Monte Carlo simulation of the atomic deposition process, considering the effects of temperature and substrate bias on grain size and defect density; For polycrystalline materials, the grain distribution is generated by the Voronoi algorithm, combined with grain boundary energy calculation to simulate grain growth dynamics.

[0011] Preferably, the S5 specifically includes the following steps: S501: Equipment-level process modeling, using FemTCAD software to build a semiconductor production reaction chamber, import the equipment geometry model, and set boundary conditions; S502: Multi-physics coupling simulation, including flow field analysis: simulation of reaction gas flow based on Navier-Stokes equations; Thermal Field and Mass Transfer: Coupling the Fourier heat transfer equation with Fick's diffusion law to calculate the temperature gradient and reactant concentration distribution during thin film growth; Electromagnetic fields and plasma: For etching processes, the PIC-MCC method is used to simulate the plasma sheath potential and ion bombardment energy; S503: Linked with material properties, the material data obtained in S3 is used as input parameters to update the performance changes of the material during the process in real time.

[0012] Preferably, the S6 specifically includes the following steps: S601: Feature engineering: extracting material first-principles calculation data as physical prior features, collecting process simulation data as process features, and constructing a multi-dimensional feature space; S602: Model construction, regression model: Use either gradient boosting tree or graph neural network to establish the mapping relationship between process parameters and material properties; generative model: Use generative adversarial network to design new semiconductor alloys and verify the stability of the generated structure through first principles; S603: Optimization strategy, including Bayesian optimization: automatically searching for the optimal process parameter combination guided by minimizing the objective function; Transfer learning: Migrating training models for silicon-based materials to third-generation semiconductors to reduce repetitive computing costs.

[0013] Preferably, the step S7 specifically includes the following steps: S701: Dynamic process visualization, atomic scale: using SHINE software to render atomic displacement animations and energy band evolution curves in real time during structure optimization; mesoscopic and macroscopic: using FemTCAD's finite element post-processing module to draw temperature contour maps, flow field vector diagrams, and film thickness distribution histograms within the process chamber; S702: Experimental data benchmarking: Import experimental data such as XRD diffraction spectra, TEM images, and Raman spectra of materials, and perform fitting comparisons with simulation results. Parameter sensitivity analysis is supported: Latin hypercube sampling is used to generate parameter combinations and quantify the influence of each factor on the target physical properties.

[0014] Preferably, the step S8 specifically includes the following steps: S801: Building a data center platform, including establishing a materials database: storing the atomic structure, calculation parameters, and physical property results of different material systems; a process knowledge base: recording historical simulation cases and forming a problem and solution mapping library; S802: Continuous optimization of the model. After each application, the actual production data is reversely input into the model to update the intrinsic parameters of the material, forming a closed loop of simulation, production, and feedback. Version control tools are used to manage model iterations and record algorithm improvements for each version.

[0015] A first-principles-based material property calculation and simulation system, including an atomic-level structure modeling and parameter configuration module, a structure optimization and defect simulation module, an electronic structure and intrinsic property calculation module, a multi-scale coupling and mesoscopic evolution simulation module, a multi-physics process simulation and equipment modeling module, a data-driven intelligent optimization and reverse design module, a multi-dimensional visualization and experimental benchmarking module, and a full-lifecycle data management and model iteration module. The atomic-level structure modeling and parameter configuration module is used to realize the digital construction and calculation parameter initialization of crystal, molecular, and cluster structures, and supports three-dimensional modeling of crystals, molecules, and clusters. The structural optimization and defect simulation module is used to eliminate initial structural energy redundancy and simulate defect behavior in real materials. It includes a gradient optimization algorithm library, specifically the conjugate gradient method, BFGS, and DFP. The convergence accuracy supports dynamic adjustment from 0.01 to 0.1 eV / Å. It also includes stress boundary conditions: supporting periodic boundaries, free surfaces, and fixed substrates, and can define biaxial and uniaxial stresses. The electronic structure and intrinsic property calculation module is used to reveal the nature of the electronic behavior of materials and output key physical parameters of electrical, optical, and magnetic properties. It includes multi-scale electronic structure calculations, specifically band engineering: it supports automatic generation of high-symmetry point paths and outputs band diagrams, state density, and partial-wave state density; carrier transport calculations: based on the Boltzmann transport equation, it considers acoustic and optical phonon scattering and calculates electron and hole mobility and conductivity; and also includes multi-field coupled property predictions, specifically predictions of optical and magnetic properties; The multi-scale coupling and mesoscopic evolution simulation module is used to bridge the atomic scale and macroscopic performance, revealing the evolution laws of the mesoscopic structure of grain boundaries, defects, and phase boundaries; The multi-physics process simulation and equipment modeling module is used to build a virtual environment for semiconductor production reaction chambers and simulate the multi-field coupling process of key processes such as CVD, etching, and ion implantation; The data-driven intelligent optimization and reverse design module realizes the automated optimization of material composition and process parameters based on first principles data and process simulation data; The multi-dimensional visualization and experimental benchmarking module is used to provide a full-process visualization tool from atomic motion to production line process, supporting quantitative benchmarking of simulation results and experimental data; The full life cycle data management and model iteration module is used to build an enterprise-level material R&D knowledge base to achieve continuous optimization of simulation models and accumulation of data assets.

[0016] Compared with the existing technology, the present invention provides a material property calculation and simulation method and system based on first principles, which has the following beneficial effects: 1. This first-principles-based material property calculation and simulation method and system realizes the full-process simulation from the atomic scale to the macroscopic material, covering atomic-level structural modeling, multi-scale coupling and macroscopic process simulation. This multi-scale simulation can comprehensively and deeply reveal the physical properties and behaviors of materials at different scales, overcoming the limitation of single-scale simulation that can only focus on local phenomena, and can provide more complete and accurate information for material design and process optimization.

[0017] 2. This material property calculation and simulation method and system based on first principles has the function of multi-physics field coupling simulation, which can simultaneously consider the interaction of multiple physical fields such as mechanical, thermal, electrical, magnetic, and optical fields. In actual materials and material applications, multiple physical fields often exist simultaneously and influence each other. Traditional methods are difficult to accurately simulate this complex coupling effect. However, this system can achieve deep fusion of multiple fields through the independently developed virtual physical scene engine, providing a more realistic simulation environment for solving complex engineering problems.

[0018] 3. This first-principles-based material property calculation and simulation method and system combines first-principles and machine learning technology to use data-driven intelligent optimization. On the one hand, first-principles calculations ensure the accuracy of simulations and the reliability of physical essences; on the other hand, machine learning can process large amounts of computational and experimental data, quickly establish a mapping relationship between process parameters and material properties, and realize automatic optimization of parameters. This greatly shortens the R&D cycle and improves R&D efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0020] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods. Example

[0021] like Figure 1 As shown, a material property calculation and simulation method based on first principles includes the following steps: S1: Atomic-level structural modeling and initial parameter setting, used to establish the atomic-level initial structure of the material; Specifically, it includes: using SHINE software to build material atomic models, supporting 3D modeling of crystal, molecular, and cluster structures, importing CIF and XYZ format files, and manually defining lattice parameters; The calculation parameters are customized based on the quantum mechanics calculation engine, defining the exchange-correlation functional, cutoff energy, K-point grid, and taking into account spin polarization and van der Waals corrections.

[0022] S2: Structural optimization and energy minimization are used to obtain the lowest energy stable atomic structure and eliminate the geometric deviation of the initial modeling. Specifically, it includes: using the conjugate gradient method for geometric optimization, minimizing the interatomic forces and total energy, iterating until the lattice parameters and atomic positions are stable, and for thin film materials, introducing stress boundary conditions to simulate the stress state in actual growth.

[0023] S3: Electronic structure calculation and intrinsic property extraction, used to reveal the nature of the electronic behavior of materials and predict the electrical, optical, and magnetic intrinsic properties; S3 specifically includes the following steps: S301: Based on the structure optimized in S2, calculate the material's band structure, state density, partial-wave state density, and extract the band gap width, valence band top, conduction band bottom position, and electron distribution near the Fermi level; S302: Calculate carrier mobility and conductivity using the Boltzmann transport equation, and calculate optical absorption spectrum and refractive index using dielectric function; S303: For magnetic materials, calculate the magnetic moment, exchange coupling constant, and analyze ferromagnetic and antiferromagnetic order.

[0024] S4: Multiscale coupling and mesostructure simulation, used to bridge the atomic scale and macroscopic properties, revealing the influence of the mesostructure of grain boundaries, defects, and phase boundaries on the macroscopic physical properties of the material. The specific steps include the following: S401: Atomic scale data dimensionality reduction, inputting the material parameters obtained in S3 into the phase field method; S402: Mesoscopic structure, for thin film growth, using kinetic Monte Carlo simulation of the atomic deposition process, considering the effects of temperature and substrate bias on grain size and defect density; For polycrystalline materials, the grain distribution is generated by the Voronoi algorithm, combined with grain boundary energy calculation to simulate grain growth dynamics.

[0025] S5: Multi-physics coupled process simulation is used to predict the impact of different process parameters on material growth quality in a virtual environment. The specific steps include the following: S501: Equipment-level process modeling, using FemTCAD software to build a semiconductor production reaction chamber, import the equipment geometry model, and set boundary conditions; S502: Multi-physics coupling simulation, including flow field analysis: simulation of reaction gas flow based on Navier-Stokes equations; Thermal Field and Mass Transfer: Coupling the Fourier heat transfer equation with Fick's diffusion law to calculate the temperature gradient and reactant concentration distribution during thin film growth; Electromagnetic fields and plasma: For etching processes, the PIC-MCC method is used to simulate the plasma sheath potential and ion bombardment energy; S503: Linked with material properties, the material data obtained in S3 is used as input parameters to update the performance changes of the material during the process in real time.

[0026] S6: Data-driven intelligent optimization is used to achieve an automated closed-loop calculation, simulation, and optimization process. It uses machine learning models to quickly identify the optimal doping concentration and reduce material warpage. The specific steps include: S601: Feature engineering: extracting material first-principles calculation data as physical prior features, collecting process simulation data as process features, and constructing a multi-dimensional feature space; S602: Model construction, regression model: Use either gradient boosting tree or graph neural network to establish the mapping relationship between process parameters and material properties; generative model: Use generative adversarial network to design new semiconductor alloys and verify the stability of the generated structure through first principles; S603: Optimization strategy, including Bayesian optimization: automatically searching for the optimal process parameter combination guided by minimizing the objective function; Transfer learning: Migrating training models for silicon-based materials to third-generation semiconductors to reduce repetitive computing costs.

[0027] S7: Multi-dimensional visual verification and interaction, used to provide engineers with intuitive data analysis tools, including the following steps: S701: Dynamic process visualization, atomic scale: using SHINE software to render atomic displacement animations and energy band evolution curves in real time during structure optimization; mesoscopic and macroscopic: using FemTCAD's finite element post-processing module to draw temperature contour maps, flow field vector diagrams, and film thickness distribution histograms within the process chamber; S702: Experimental data benchmarking: Import experimental data such as XRD diffraction spectra, TEM images, and Raman spectra of materials, and perform fitting comparisons with simulation results. Parameter sensitivity analysis is supported: Latin hypercube sampling is used to generate parameter combinations and quantify the influence of each factor on the target physical properties.

[0028] S8: Full lifecycle model iteration and knowledge base construction, used to build enterprise-level material design capability barriers, specifically including the following steps: S801: Building a data center platform, including establishing a materials database: storing the atomic structure, calculation parameters, and physical property results of different material systems; a process knowledge base: recording historical simulation cases and forming a problem and solution mapping library; S802: Continuous optimization of the model. After each application, the actual production data is reversely input into the model to update the intrinsic parameters of the material, forming a closed loop of simulation, production, and feedback. Version control tools are used to manage model iterations and record algorithm improvements for each version. Example

[0029] A first-principles-based material property calculation and simulation system, including an atomic-level structure modeling and parameter configuration module, a structure optimization and defect simulation module, an electronic structure and intrinsic property calculation module, a multi-scale coupling and mesoscopic evolution simulation module, a multi-physics process simulation and equipment modeling module, a data-driven intelligent optimization and reverse design module, a multi-dimensional visualization and experimental benchmarking module, and a full-lifecycle data management and model iteration module. The atomic-level structure modeling and parameter configuration module is used to digitally construct crystal, molecular, and cluster structures and initialize calculation parameters, and supports three-dimensional modeling of crystals, molecules, and clusters. The Structural Optimization and Defect Simulation module is used to eliminate initial structural energy redundancy and simulate defect behavior in real materials. It includes a gradient optimization algorithm library, specifically the conjugate gradient method, BFGS, and DFP. The convergence accuracy supports dynamic adjustment from 0.01 to 0.1 eV / Å. It also includes stress boundary conditions: supporting periodic boundaries, free surfaces, and fixed substrates, and can define biaxial and uniaxial stresses. The electronic structure and intrinsic property calculation module is used to reveal the nature of the electronic behavior of materials and output key electrical, optical, and magnetic physical property parameters. It includes multi-scale electronic structure calculations, specifically band engineering: it supports automatic generation of high-symmetry point paths and outputs band diagrams, state density, and partial-wave state density; carrier transport calculations: based on the Boltzmann transport equation, it considers acoustic and optical phonon scattering and calculates electron and hole mobility and conductivity; it also includes multi-field coupled physical property predictions, specifically predictions of optical and magnetic properties; The multi-scale coupling and mesoscopic evolution simulation module is used to bridge the atomic scale and macroscopic properties, revealing the evolution of the mesoscopic structure of grain boundaries, defects, and phase boundaries; The multi-physics process simulation and equipment modeling module is used to build a virtual environment for semiconductor production reaction chambers and simulate the multi-field coupling process of key processes such as CVD, etching, and ion implantation. The data-driven intelligent optimization and reverse design module realizes the automated optimization of material composition and process parameters based on first-principles data and process simulation data; The multi-dimensional visualization and experimental benchmarking module provides visualization tools for the entire process, from atomic motion to production line processes, and supports quantitative benchmarking of simulation results with experimental data. The full life cycle data management and model iteration module is used to build an enterprise-level material R&D knowledge base to achieve continuous optimization of simulation models and accumulation of data assets.

[0030] When it comes to nanomaterials, assistance can be provided based on and referring to existing software such as the ab initio calculation software for nanomaterials and the density functional calculation software for nanomaterials.

[0031] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A material property calculation and simulation method based on first principles, characterized by: The following steps are included: S1: Atomic-level structural modeling and initial parameter setting, used to establish the atomic-level initial structure of the material; S2: Structural optimization and energy minimization, used to obtain the lowest energy stable atomic structure and eliminate the geometric deviation of the initial modeling; S3: Electronic structure calculation and intrinsic property extraction, used to reveal the nature of the electronic behavior of materials and predict the electrical, optical, and magnetic intrinsic properties; S4: Multiscale coupling and mesoscopic structure simulation, used to bridge the atomic scale and macroscopic properties, revealing the influence of the mesostructure of grain boundaries, defects, and phase boundaries on the macroscopic physical properties of materials; S5: Multi-physics coupled process simulation, used to predict the impact of different process parameters on material growth quality in a virtual environment; S6: Data-driven intelligent optimization, used to achieve an automated closed-loop calculation, simulation, and optimization process. It uses machine learning models to quickly identify the optimal doping concentration and reduce material warpage. S7: Multi-dimensional visual verification and interaction, used to provide engineers with intuitive data analysis tools; S8: Full life cycle model iteration and knowledge base construction to build enterprise-level material design capability barriers.

2. The material property calculation and simulation method based on first principles according to claim 1, characterized in that: Said S1 specifically includes: using SHINE software to construct a material atomic model, supporting three-dimensional modeling of crystal, molecular and cluster structures, importing CIF and XYZ format files, and manually defining lattice parameters; The calculation parameters are customized based on the quantum mechanics calculation engine, defining the exchange-correlation functional, cutoff energy, K-point grid, and taking into account spin polarization and van der Waals corrections.

3. The material property calculation and simulation method based on first principles according to claim 1, characterized in that: The S2 specifically includes: using the conjugate gradient method to perform geometric optimization, minimizing the interatomic forces and total energy, iterating until the lattice parameters and atomic positions are stable, and introducing stress boundary conditions for thin film materials to simulate the stress state in actual growth.

4. The material property calculation and simulation method based on first principles according to claim 1, characterized in that: The S3 specifically includes the following steps: S301: Based on the structure optimized in S2, calculate the material's band structure, state density, partial-wave state density, and extract the band gap width, valence band top, conduction band bottom position, and electron distribution near the Fermi level; S302: Calculate carrier mobility and conductivity using the Boltzmann transport equation, and calculate optical absorption spectrum and refractive index using dielectric function; S303: For magnetic materials, calculate the magnetic moment, exchange coupling constant, and analyze ferromagnetic and antiferromagnetic order.

5. The material property calculation and simulation method based on first principles according to claim 1, characterized in that: The S4 specifically includes the following steps: S401: Atomic scale data dimensionality reduction, inputting the material parameters obtained in S3 into the phase field method; S402: Mesoscopic structure, for thin film growth, using kinetic Monte Carlo simulation of the atomic deposition process, considering the effects of temperature and substrate bias on grain size and defect density; For polycrystalline materials, the grain distribution is generated by the Voronoi algorithm, combined with grain boundary energy calculation to simulate grain growth dynamics.

6. The material property calculation and simulation method based on first principles according to claim 1, characterized in that: The S5 specifically includes the following steps: S501: Equipment-level process modeling, using FemTCAD software to build a semiconductor production reaction chamber, import the equipment geometry model, and set boundary conditions; S502: Multi-physics coupling simulation, including flow field analysis: simulation of reaction gas flow based on Navier-Stokes equations; Thermal Field and Mass Transfer: Coupling the Fourier heat transfer equation with Fick's diffusion law to calculate the temperature gradient and reactant concentration distribution during thin film growth; Electromagnetic fields and plasma: For etching processes, the PIC-MCC method is used to simulate the plasma sheath potential and ion bombardment energy; S503: Linked with material properties, the material data obtained in S3 is used as input parameters to update the performance changes of the material during the process in real time.

7. The material property calculation and simulation method based on first principles according to claim 1, characterized in that: The S6 specifically includes the following steps: S601: Feature engineering: extracting material first-principles calculation data as physical prior features, collecting process simulation data as process features, and constructing a multi-dimensional feature space; S602: Model construction, regression model: Use either gradient boosting tree or graph neural network to establish the mapping relationship between process parameters and material properties; generative model: Use generative adversarial network to design new semiconductor alloys and verify the stability of the generated structure through first principles; S603: Optimization strategy, including Bayesian optimization: automatically searching for the optimal process parameter combination guided by minimizing the objective function; Transfer learning: Migrating training models for silicon-based materials to third-generation semiconductors to reduce repetitive computing costs.

8. The material property calculation and simulation method based on first principles according to claim 1, characterized in that: The S7 specifically includes the following steps: S701: Dynamic process visualization, atomic scale: using SHINE software to render atomic displacement animations and energy band evolution curves in real time during structure optimization; mesoscopic and macroscopic: using FemTCAD's finite element post-processing module to draw temperature contour maps, flow field vector diagrams, and film thickness distribution histograms within the process chamber; S702: Experimental data benchmarking: Import experimental data such as XRD diffraction spectra, TEM images, and Raman spectra of materials, and perform fitting comparisons with simulation results. Parameter sensitivity analysis is supported: Latin hypercube sampling is used to generate parameter combinations and quantify the influence of each factor on the target physical properties.

9. The material property calculation and simulation method based on first principles according to claim 1, characterized in that: The S8 specifically includes the following steps: S801: Building a data center platform, including establishing a materials database: storing the atomic structure, calculation parameters, and physical property results of different material systems; a process knowledge base: recording historical simulation cases and forming a problem and solution mapping library; S802: Continuous optimization of the model. After each application, the actual production data is reversely input into the model to update the intrinsic parameters of the material, forming a closed loop of simulation, production, and feedback. Version control tools are used to manage model iterations and record algorithm improvements for each version.

10. A first-principles-based material property calculation and simulation system, employing a first-principles-based material property calculation and simulation method according to any one of claims 1 to 9, comprising an atomic-level structure modeling and parameter configuration module, a structure optimization and defect simulation module, an electronic structure and intrinsic property calculation module, a multi-scale coupling and mesoscopic evolution simulation module, a multi-physics process simulation and equipment modeling module, a data-driven intelligent optimization and reverse design module, a multi-dimensional visualization and experimental benchmarking module, and a full-lifecycle data management and model iteration module, characterized in that: The atomic-level structure modeling and parameter configuration module is used to realize the digital construction of crystal, molecule, and cluster structures and the initialization of calculation parameters, and supports three-dimensional modeling of crystals, molecules, and clusters; The structural optimization and defect simulation module is used to eliminate initial structural energy redundancy and simulate defect behavior in real materials. It includes a gradient optimization algorithm library, specifically the conjugate gradient method, BFGS, and DFP. The convergence accuracy supports dynamic adjustment from 0.01 to 0.1 eV / Å. It also includes stress boundary conditions: supporting periodic boundaries, free surfaces, and fixed substrates, and can define biaxial and uniaxial stresses. The electronic structure and intrinsic property calculation module is used to reveal the nature of the electronic behavior of materials and output key physical parameters of electrical, optical, and magnetic properties. It includes multi-scale electronic structure calculations, specifically band engineering: it supports automatic generation of high-symmetry point paths and outputs band diagrams, state density, and partial-wave state density; carrier transport calculations: based on the Boltzmann transport equation, it considers acoustic and optical phonon scattering and calculates electron and hole mobility and conductivity; and also includes multi-field coupled property predictions, specifically predictions of optical and magnetic properties; The multi-scale coupling and mesoscopic evolution simulation module is used to bridge the atomic scale and macroscopic performance, revealing the evolution laws of the mesoscopic structure of grain boundaries, defects, and phase boundaries; The multi-physics process simulation and equipment modeling module is used to build a virtual environment for semiconductor production reaction chambers and simulate the multi-field coupling process of key processes such as CVD, etching, and ion implantation; The data-driven intelligent optimization and reverse design module realizes the automated optimization of material composition and process parameters based on first principles data and process simulation data; The multi-dimensional visualization and experimental benchmarking module is used to provide a full-process visualization tool from atomic motion to production line process, supporting quantitative benchmarking of simulation results and experimental data; The full life cycle data management and model iteration module is used to build an enterprise-level material R&D knowledge base to achieve continuous optimization of simulation models and accumulation of data assets.

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