A DMol3 electron density difference data processing system and file generation method

Abstract

This invention relates to the field of computational chemistry data processing technology, and in particular to a DMol3 electron density difference data processing system and file generation method. The system includes: an input module for acquiring electron density files in specific formats for the overall system and each component generated by DMol3 calculations; a processing module for automatically reading the files and performing numerical calculations on the electron density differences; and an output module for simultaneously generating the calculation results into a first format file and a second format file. This invention, through a multi-format parallel output architecture, solves the technical problems of cumbersome post-processing workflows, single result file formats, and incompatibility with visualization software in existing DMol3 electron density difference data, achieving automation of the data processing workflow and cross-platform compatibility for result display.

Description

Technical Field

[0001] This invention belongs to the field of data post-processing technology for computational chemistry and materials simulation software, and particularly relates to a DMol3 electron density difference data processing system and file generation method for automatically processing specific format data files generated by the DMol3 module of Materials Studio software and generating cross-platform compatible visualization files. Background Technology

[0002] Materials Studio, developed by Accelrys, is a multi-scale materials computation software. Its core modules include the 3D modeling tool Visualizer, the quantum mechanics program DMol3 / CASTEP, the molecular dynamics module Forcite, and the crystal diffraction analysis module Reflex. It can perform tasks such as electronic structure calculations, dynamic simulations, and spectral analysis. As an integrated simulation platform for materials science, it provides multi-scale simulation solutions for materials research and development, chemical engineering, and nanotechnology. Utilizing molecular dynamics, quantum chemistry, and Monte Carlo methods, it helps users predict material properties, optimize structural designs, and accelerate the development of new materials. The quantum mechanics program DMol3 / CASTEP, after optimizing the model's structure, allows for further exploration of the model's electronic properties. Electron density difference (EDD) is a commonly used technique in quantum theory calculations to analyze chemical bond formation and electron transfer. The electron density map in the calculation model needs to calculate the electron density of different parts. Assuming that the three electron densities are simply set as AB, A, and B, the electron density map is calculated based on the electron density data obtained from the Dmol3 module. The problems are as follows: (1) The electron density of the three parts AB, A, and B is subtracted using Origin software. The disadvantage of this scheme is that it requires the use of text software such as Notepad to open the file. However, when the file data is too large, it is easy to cause lag. At the same time, a large amount of data needs to be copied frequently. After the difference calculation, the data needs to be copied back to the .grd format file. The operation is cumbersome and prone to human error. (2) The Multiwfn software developed by Lu Tian is used for operation. Multiwfn can also directly load the grd file. Then, the sub-function 11 of the main function 13 can be used to perform various mathematical operations on the currently loaded and another grd file. The grid data after calculation can be viewed directly in the main function 13 using option-2 to view the isosurface. It can also be exported as a cub file for other programs to draw. The drawbacks of this approach are that the generated cub files are inconvenient to view in Multiwfn, the exported files do not contain atoms and their coordinates, only equipotential surface plots, which do not meet the requirements for plotting differential electron density maps; the exported format is only .cub files, which cannot be viewed in Materials Studio software, and the plot styles exported in other software are different from those in Materials Studio, causing plotting discrepancies.

[0003] Therefore, the main problem with existing technologies is that the post-processing of DMol3 electron density difference is cumbersome and error-prone, and there is a lack of a technical solution that can automatically generate an electron density difference file containing complete structural information that is compatible with both commercial software (Materials Studio) and open-source software (VESTA). Summary of the Invention

[0004] The present invention aims to overcome the shortcomings of the prior art and provide an automated, high-precision, multi-format output DMol3 electronic density difference data processing system and file generation method.

[0005] To solve the above-mentioned technical problems, the present invention is implemented as follows:

[0006] A DMol3 electron density difference data processing system, comprising:

[0007] The input module is configured to receive the overall system electron density file, the first component electron density file, the second component electron density file, and the overall system structure file generated by DMol3;

[0008] The processing module, communicatively connected to the input module, is configured to automatically parse and read the numerical data in the electron density file, and based on the formula... Perform numerical calculations of electron density difference to generate an electron density difference dataset;

[0009] The output module, which is communicatively connected to the processing module, is configured to simultaneously convert and output the electron density difference data set in the processing module into two different software-recognizable format files: a first format file suitable for Materials Studio software and a second format file suitable for VESTA software.

[0010] Furthermore, the processing module is implemented using Matlab or Python programming language, forming a Matlab processing unit or a Python processing unit.

[0011] Furthermore, when the structure file received by the input module is a structure data file conforming to the VASP interface standard, and the second format file generated by the output module contains complete atomic structure information, it can be directly and correctly read and visualized by the VESTA software.

[0012] Furthermore, the structure data file is in .VASP or .POSCAR format.

[0013] Furthermore, the system is deployed on a Linux operating system, and the processing module is written in Python and configured to perform batch data processing tasks on a massive number of multiple sets of electron density files.

[0014] A method for generating electron density difference files, applied to the aforementioned DMol3 electron density difference data processing system, the method comprising:

[0015] (1) Receive multiple specific format electron density files and structure files generated by DMol3 calculation through the system's input module;

[0016] (2) The electron density difference is automatically calculated through the system's processing module, generating an electron density difference dataset;

[0017] (3) The electron density difference dataset is merged and generated and output as a specific format file that can be recognized by two different software through the system's output module.

[0018] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described above.

[0019] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, controls the processor to perform the steps in the above-described method, or causes the computing device to operate as the above-described system.

[0020] This invention provides a DMol3 electron density difference data processing system. The core of this system lies in its modular architecture, which solves the end-to-end technical problems from data reading and calculation to multi-format file generation through the collaborative work of various modules. Using this invention, simply place the three electron density files (AB, A, and B) into a folder, and running the system will directly generate files in the required format. This eliminates the need to open the text files for numerical copying and reduces human error by minimizing manual operations. The generated file formats are divided into two types, allowing for visualization without software limitations. It can be displayed in commercial software like Materials Studio or in free and open-source software such as VESTA, with good display results. This invention is the first to achieve DMol3 data processing that leverages the complementary advantages of two languages, featuring a multi-language collaborative overall architecture. It breaks the limitations of processing DMol3 data in Materials Studio, enabling efficient processing of the data in third-party programs. The calculation process is simple, efficient, and has a low learning curve. A Python-Matlab hybrid verification module verifies the reliability of the results. Furthermore, relying on the Linux system, programming with Python enables efficient processing of massive amounts of data.

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

[0022] (1) The present invention only requires executing one calculation command to obtain the target data;

[0023] (2) This invention fully considers the platform copyright owned by the user, and can use either the copyrighted commercial software Matlab or the free and open source Python program;

[0024] (3) This invention directly addresses the functional defects of the DMol3 module and the output defects of existing post-processing tools (such as Multiwfn), providing an end-to-end automated solution.

[0025] (4) This invention eliminates human error through automated processing, and improves the accuracy and efficiency of data processing; through multi-format parallel output, it realizes seamless technical integration of data processing results with different visualization software (Materials Studio, VESTA), and improves the usability and cross-platform sharing capability of the results.

[0026] (5) This invention supports both Matlab and Python dual-language engines, which can utilize the stability and efficiency of commercial software, as well as the advantages of open source software in batch processing of massive data in the Linux environment. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating the framework and data processing of the system described in an embodiment of the present invention.

[0028] Figure 2 This is a visualization of the electronic density difference file (CHGCAR format) generated by the system of this invention in VESTA software.

[0029] Figure 3 This is a visualization of the electronic density difference file (.grd format) generated by the system of this invention in Materials Studio 2020 software.

[0030] Figure 4 This is a visualization of the .cub format file generated using the existing Multiwfn software in VESTA for comparison.

[0031] Figure 5 This is a flowchart of the core computational logic of the Matlab processing unit in this embodiment of the invention.

[0032] Figure 6 A Python-based interface for calculating the electron density difference of DMol3. Detailed Implementation

[0033] The present invention will now be described in detail through specific embodiments. These embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art. As used throughout the specification and claims, the terms "comprising" or "including" are open-ended and are interpreted as "comprising but not limited to". The following description is a preferred embodiment for carrying out the invention; however, this description is intended to illustrate the general principles of the specification and is not intended to limit the scope of the invention. The scope of protection of the present invention is determined by the appended claims.

[0034] The electron density difference (EDD) in the calculation model can be obtained using different software, depending on the software's parameter settings. Commonly used quantization calculation software can be simply categorized as follows:

[0035] Commonly used commercial software: VASP, Materials Studio (CASTEP / DMol3), Gaussian

[0036] Commonly used open-source and free software: CP2K, Quantum ESPRESSO, ORCA;

[0037] Different software can be chosen to suit different model requirements. For calculating periodic models, such as molecular sieves and oxide surface models, VASP, Materials Studio (CASTEP / DMol3), CP2K, and QuantumESPRESSO are preferred. For calculating aperiodic models, such as biological proteins and metal clusters, Gaussian, Materials Studio (DMol3), and ORCA are more commonly used. Generally, commercial software has accompanying visualization programs that can visualize the calculated EDD. For example, the electron density files (CHG and CHGCAR files) calculated by VASP can be visualized in the free VESTA software after data processing. Materials Studio has a very simple visualization interface; simply click to select a model to visualize the EDD calculated by CASTEP. The electron density files (cube files) calculated by Gaussian are visualized using the accompanying GaussView software. Free and open-source software, however, requires software such as VESTA, VMD, and Multiwfn for data processing and EDD visualization after calculating the electron density files.

[0038] Among them, the DMol3 module in Materials Studio, with its unique numerical function quantum mechanics program, can quickly and efficiently calculate periodic and aperiodic models, making it a widely used computational chemistry program. While this module can calculate electron density, Materials Studio itself does not provide electron density processing operations for the DMol3 module; post-processing of the calculated electron density data is required to obtain the desired electron density difference map.

[0039] This invention provides a method for calculating the electron density difference of DMol3. Its core lies in automating the entire calculation and output process of the electron density difference using a Matlab or Python program. The method of this invention includes:

[0040] Input steps: The program receives four key input files, namely the overall system (AB), the DMol3 electron density files of component A and component B (usually *_density.grd), and the structure file (POSCAR) describing the atomic arrangement of the system.

[0041] Processing steps: The program kernel is based on the fundamental principle formula of electron density difference. It automatically reads the values ​​from three electron density files and performs matrix subtraction. Calculation and verification steps: Before executing the calculation, the processing module first executes the grid consistency verification subroutine to compare the values ​​with M. AB M A M B Check if the dimension parameters of the three matrices are strictly equal. If the check passes, perform element-wise linear subtraction using a matrix operation library (such as NumPy): M diff (i,j,k) = M AB (i,j,k) - M A (i,j,k) - M B (i,j,k). For the calculation results, the system will perform outlier detection (such as removing infinity values) to ensure the validity of the dataset.

[0042] Output steps: The program outputs the calculated electron density difference data in parallel into two standard file formats:

[0043] DMol3 format file (.grd): This file can be directly imported into the Analysis module of Materials Studio software for isosurface plotting and visualization analysis.

[0044] VASP format file (CHGCAR): This file can be directly read and visualized by free and open-source software such as VESTA. The CHGCAR file generated by the Python version of the program is reconstructed by the output module according to the standard data stream format defined by the VASP software. The specific process includes:

[0045] (1) Write header information: The input module first parses the structure file (such as POSCAR) that conforms to the VASP interface standard and extracts the key metadata in the first 7-8 lines, including scaling factor, lattice vector matrix and atom position information.

[0046] (2) Mesh parameter writing: Immediately following, write the mesh generation parameters (N). x , N y , N z );

[0047] (3) Data serialization: The calculated difference density matrix M diff Flatten the data according to Z-axis priority (or the specific axis order required by the software) and append it to the end of the file in scientific notation format (such as %16.11E).

[0048] Through this "structure head + density volume" encapsulation technology, the generated output file is self-descriptive, allowing users to simultaneously display the atomic skeleton and electron cloud isosurfaces in VESTA without needing to load additional structure files.

[0049] Furthermore, this invention provides programs implemented in both Matlab and Python, allowing users to flexibly choose based on their software license and computing environment (such as Windows or Linux). Particularly on the Linux platform, the Python program enables batch processing of large numbers of computational tasks, significantly improving research efficiency. The processing module incorporates batch task scheduling logic: the system first traverses the target folder, matching all files with the *_density.grd extension using regular expressions; then, it automatically creates file index groups based on filename prefixes (such as System_AB, System_A); subsequently, it initiates a loop iteration, sequentially passing each group of file paths to the computation core, automatically completing all data calculations and format conversions through multi-process or serial methods, and generating corresponding log files.

[0050] To address the demands of massive data processing in Linux environments, this invention utilizes the Python processing unit based on the NumPy high-performance numerical computing library. By leveraging NumPy's ndarray structure to read the binary stream of .grd files, large-scale matrix subtraction operations can be performed extremely quickly in a vectorized manner. Furthermore, in batch mode, the system utilizes Python's multiprocessing module to construct a parallel process pool. The system automatically traverses the target directory, matches file groups using regular expressions, and distributes each group of computational tasks to different CPU cores for parallel execution. This architecture significantly reduces I / O blocking and dramatically improves the efficiency of processing hundreds or thousands of computational models on server clusters.

[0051] Example: Operation flow of the DMol3 electron density difference data processing system

[0052] See Figure 1 The operational flow of this system in practical applications is as follows:

[0053] 1. Data Input Stage

[0054] Users complete DMol3 calculations in Materials Studio to obtain raw data files such as AB_density.grd, A_density.grd, and B_density.grd.

[0055] The user prepares the structure file POSCAR. The system's input module requires this structure file to conform to the VASP standard interface definition (including lattice vectors and atomic coordinates) so that the system can directly read it and use it for subsequent heterogeneous file stream merging.

[0056] 2. Data Processing and Calculation Stage

[0057] For researchers or scenarios requiring integration into automated workflows, this system provides a scripting interface based on the Matlab language. Users do not need to launch a graphical interface; they only need to specify the input file path (density_AB, density_A, etc.) and output parameters in the script's configuration area to directly call the main control function ElectronDensityDifference4DMol3. The advantages of this mode are its lightweight nature and ease of batch processing. Users can write loop statements to call the function at once to process hundreds or thousands of structural models.

[0058] The user invokes the system. Taking the Matlab version as an example, execute the following command in the command line:

[0059] MATLAB

[0060] ElectronDensityDifference4DMol3('AB_density.grd', 'A_density.grd', 'B_density.grd', 'POSCAR', 'Output_EDD');

[0061] ```

[0062] After the user enters a command in the Matlab terminal to trigger a calculation, the system executes the following in the background: Figure 5 The logical flow is shown below. First, the input interface verifies the validity of the five parameter paths. Next, the memory allocator pre-allocates memory space for the three upcoming double-precision floating-point matrices based on the pre-read file header information (Header Info), to avoid performance loss due to dynamic expansion. Finally, the system transfers control to the core computing module. This program automatically reads the numerical contents of the three .grd files and executes the formulas. The defined numerical operations.

[0063] When a processing instruction is triggered, the system calls the main control function ElectronDensityDifference4DMol3. This function internally executes the following strict timing logic:

[0064] Parameter validation and initialization: The program first checks the number of input parameters (nargin). If there are insufficient parameters (less than 4), the system throws an exception message; if no output file name is specified, the system automatically assigns a default identifier to ensure the program's fault tolerance.

[0065] Structured data parsing: The main control function calls the import_grd subroutine to parse the input files of the overall system (AB) and the subsystems (A, B) respectively. Crucially, this step not only extracts the electron density matrix but also simultaneously extracts the grid division parameters (nGrids) and lattice constant, storing this geometric information in memory as a reference for subsequent coordinate alignment.

[0066] Difference calculation: Perform matrix subtraction in memory: density = density_AB - densityA - densityB to obtain the electron density difference data in the original unit.

[0067] Cross-software unit normalization (core technical feature): Because DMol3 and VASP / VESTA software use different physical unit systems (usually involving the difference between atomic units Bohr and Angstrom), to ensure that the generated CHGCAR file can be correctly rendered by VESTA, the system executes a unit mapping algorithm: first, the cell volume (Volumet) is calculated, and then the density matrix is ​​volume-weighted and unit-scaled based on the Bohr radius constant (approximately 0.529177) (corresponding code logic: density*Volumet / 0.52917721067^3). This step eliminates the physical dimensional barriers between different quantum chemistry software.

[0068] Split Output: Finally, the system calls two independent encapsulation subroutines, export_grd and export_CHGCAR. The former directly outputs the original mesh data, while the latter combines the input POSCAR structure information with the normalized density data to generate a composite file.

[0069] 3. File output and application stage:

[0070] The system's output module begins working, simultaneously creating two independent data files conforming to different software specifications in the user-specified directory: Output_EDD_deformation_density.grd and Output_EDD.CHGCAR.

[0071] Users can drag and drop .grd files directly into the Analysis module of Materials Studio for visualization; see the results below. Figure 3 .

[0072] Users can directly drag and drop CHGCAR files into VESTA software, which can immediately and correctly parse and render a 3D image superimposed with the electron density difference isosurface and atomic structure. See the results below. Figure 2 .

[0073] Comparative example:

[0074] In comparison, the same DMol3 dataset was processed using the existing Multiwfn technology. Current issues include the absence of a structure file in the generated computation file, indicating a functional deficiency. Furthermore, the exported file format (cub file) requires tedious additional structural information addition, and data copying can easily lead to formatting issues. See the computation results below. Figure 4While it can perform mathematical calculations, its output .cub file is an "incomplete" data file, missing crucial atomic structure information. If developers modify Multiwfn to import detailed atomic coordinates into the calculation file, a similar effect might be achieved. However, since the exported file is in .cub format, the visualization in Multiwfn differs significantly from conventional methods. Opening this file in VESTA only displays isolated, unstructured electron cloud isosurfaces. Figure 4 Such results have limited value for in-depth chemical analysis. However, this invention, through a systematic technical solution, particularly the design of the output module, ensures that the generated file is not only a numerical representation of the calculation results, but also a "finished" data file containing all the information needed for visualization, directly usable by professional software. This solves the core technical obstacle of cross-software platform data compatibility, producing a significantly superior technical effect compared to existing technologies. The formula used by the program to calculate the electron density difference is:

[0075]

[0076] Matlab program description:

[0077] ElectronDensityDifference4DMol3.m is the main function, which requires five parameters to execute the above calculation formula:

[0078] The required file format is: ElectronDensityDifference4DMol3(density_AB, density_A, density_B, POSCAR, output_filename).

[0079] Parameter description:

[0080] density_AB, density_A, and density_B represent the total electron density of the system and the electron density of each component, respectively. density_AB, density_A, and density_B are input parameters representing the file path strings to be processed. The system reads the corresponding binary or text files from these paths and converts them into a 3D floating-point matrix for subsequent algebraic operations. The input module has a built-in file path parsing algorithm that can automatically identify and read hidden files or files with specific extensions (.grd) in a specified directory without requiring the user to change the operating system's file display settings.

[0081] output_filename: The filename of the output file (default setting is: 'ElectronDensityDifference').

[0082] Introduction to using Matlab programs:

[0083] Using Materials Studio (DMol3 module), the electron densities of components AB, A, and B are calculated. After processing, the user only needs to pass the storage path (Path String) of the three result files to the Matlab main control function, and the system can automatically mount and read the data without moving or copying the original files.

[0084] You can directly execute the following command in the Matlab command line: ElectronDensityDifference4DMol3('AB_density','A_density', 'B_density', 'POSCAR', 'AB')

[0085] Introduction to Python Programming

[0086] The Python program parameters are as shown above.

[0087] To reduce the learning curve for users, this system further encapsulates a graphical user interface (GUI). The system frontend receives user commands through a visual file selector and buttons, while the backend automatically converts user clicks into a list of parameters required by the computational kernel. This model decouples human-computer interaction from underlying computation, making it suitable for novice users to quickly process single tasks. Furthermore, for massive data processing scenarios, the Python processing unit of this invention also provides a headless mode. In a Linux environment, the system automatically traverses a specified directory tree using the os.walk or glob module, and distributes each matched .grd and .POSCAR file to the computational kernel using a multiprocessing pool. This mode does not load GUI components, thus significantly reducing memory consumption and enabling automated batch processing of hundreds or thousands of systems.

[0088] See Figure 6As shown, the operation steps are as described above. To ensure accurate mapping of structural information, the input module of this system presets a standard VASP structural data interface. The input module is configured to recognize and parse structural files conforming to the VASP.POSCAR / CONTCAR standard format. This format contains rigorously defined lattice vectors and atom position data. For user-provided initial models (such as .cif or .xsd formats), the system requires them to be input as a standardized ASCII encoded data stream to ensure the execution benchmark of the heterogeneous file merging algorithm. (See...) Figure 6 (As shown).

[0089] Calculation results: will be generated in the current program file.

[0090] Matlab calculation result file processing:

[0091] ***AB_deformation_density.grd: A DMol3 format file that can be imported and viewed in Analysis.

[0092] ***.CHGCAR: VASP's CHGCAR format file, which can be viewed using VESTA. The specific steps are as follows:

[0093] The automated splicing logic in Matlab addresses the issue that Materials Studio cannot directly export VASP-compatible formats. The Matlab processing unit of this invention incorporates a heterogeneous file stream merging algorithm. The program first reads the externally input structural auxiliary file (such as a user-exported AB.VASP or AB.POSCAR) using the textscan or fopen function, parsing it into independent character stream objects. Then, the program locates the atomic coordinate terminator of this character stream in memory. Finally, the program calculates the electron density difference matrix M... diff The string is converted to VASP-compliant scientific notation and appended to the end of the character stream, automatically generating the complete AB.CHGCAR file. This process is entirely executed in the background by the code, requiring no manual text editing by the user. The file can then be correctly opened with VESTA, allowing for appropriate adjustments.

[0094] Python calculation result file processing:

[0095] All computational files are generated directly without any modification. For the AB.CHGCAR file generated by the Python processing unit, the output module automatically maps the lattice vectors and atom positions of the structure file (POSCAR) to the header of the target file using the Linecache or Pandas library before writing the data. Therefore, the generated file is self-describing. Users do not need to mount any additional structure files; they can directly render the superimposed image of the atomic framework and electron cloud isosurfaces in VESTA software (e.g., ...). Figure 2 (As shown), the operation steps are the same.

[0096] Display of computational results: The computational results from Matlab and Python are the same; this effect is a display of different visualization software.

[0097] Terminology Explanation:

[0098] Electron density: the number of electrons per unit volume, is a key parameter characterizing the microstructure of matter.

[0099] Electron density difference: also known as differential charge or charge density difference, is a method unique to density functional theory used to analyze electron transfer. It can intuitively obtain the direction of electron flow after the interaction of various segments, or the change of electron density during the formation of molecules from atoms, and explore the nature of chemical bonds.

[0100] Periodic systems: Macroscopic systems are approximated by a model box called a cell, the smallest basic structural unit.

[0101] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A DMol3 electron density difference data processing system, characterized in that, include: The input module is configured to receive the overall system electron density file, the first component electron density file, the second component electron density file, and the overall system structure file generated by DMol3; The processing module, communicatively connected to the input module, is configured to automatically parse and read the numerical data in the overall system electron density file, and based on the formula... Perform numerical calculations of electron density difference to generate an electron density difference dataset; The output module, which is communicatively connected to the processing module, is configured to simultaneously convert and output the electron density difference data set in the processing module into two different software-recognizable format files; the format files include a first format file suitable for MaterialsStudio software and a second format file suitable for VESTA software.

2. The DMol3 electron density difference data processing system according to claim 1, characterized in that, The processing modules are implemented using Matlab or Python, forming Matlab or Python processing units.

3. The DMol3 electron density difference data processing system according to claim 2, characterized in that, When the structure file received by the input module is a structure data file that conforms to the VASP interface standard, and the second format file generated by the output module contains complete atomic structure information, it can be directly and correctly read and visualized by the VESTA software.

4. The DMol3 electron density difference data processing system according to claim 3, characterized in that, The structure data file is in .VASP or .POSCAR format.

5. The DMol3 electron density difference data processing system according to claim 4, characterized in that, The system is deployed on the Linux operating system; the processing module is written in Python and configured to perform batch data processing tasks on a large number of multiple sets of electron density files.

6. A method for generating an electronic density difference file, characterized in that, The method, applied to the DMol3 electron density difference data processing system as described in any one of claims 1 to 5, comprises: (1) Receive multiple formats of electron density files and structure files generated by DMol3 calculation through the system's input module; (2) The electron density difference is automatically calculated through the system's processing module, generating an electron density difference dataset; (3) The electron density difference dataset is merged and generated and output as two different software-recognizable format files through the system's output module.

7. A computing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method as described in claim 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it controls the processor to perform the steps of the method as described in claim 6, or causes the computing device to operate as a system as described in any one of claims 1 to 5.

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