Simulation method and system for regulating and controlling heat conductivity of ferroelectric domain in bismuth ferrite film and medium

Abstract

The invention discloses a simulation method and system for regulating and controlling heat conductivity of a ferroelectric domain in a bismuth ferrite film and a medium, and mainly relates to the technical field of bismuth ferrite films. The method is used for solving the problems that the structure-function relationship between ferroelectric domain wall atomic configuration and heat transport characteristics is difficult to effectively analyze in an existing scheme, the thermal activation migration process of the ferroelectric domain wall relates to a multi-physics field coupling effect, and accuracy is difficult to achieve by adopting a conventional simulation means. Comprising the following steps: obtaining an NEP potential function; calculating a thermal switch ratio between the single-domain BFO molecular model and the double-domain BFO molecular model at each preset temperature; obtaining a theoretical maximum value of the thermal switch ratio along with temperature change, and obtaining a specific temperature corresponding to the theoretical maximum value; at a specific temperature, acquiring a change curve of the thermal switch ratio of the BFO molecular model along with the change of a preset domain wall number, and acquiring a specific domain wall number corresponding to a theoretical maximum value; and taking the specific temperature and the specific domain wall number corresponding to the theoretical maximum value as final guidance conditions.

Description

Technical Field

[0001] The present application relates to the technical field of bismuth ferrite thin films, and in particular to a simulation method, system and medium for regulating thermal conductivity of ferroelectric domains in bismuth ferrite thin films. Background Art

[0002] Bismuth ferrite film (chemical formula BiFeO 3 BFO, a typical perovskite-type room-temperature multiferroic material, has ferroelectricity and a thickness ranging from tens of nanometers to several microns. It contains domain structures with different spontaneous polarizations. Ferroelectric domain walls, as interfaces that divide electric domains with different polarizations, can move, generate, and erase rapidly under the action of an external electric field. They can serve as excellent phonon scattering interfaces, affect the transport process of phonons, and change the thermal conductivity of the material. Therefore, BFO films (BFO molecular models) can achieve dynamic reversible switching of thermal conductivity by modulating the density of domain walls. Through the design of ferroelectric domain density gradients and the regulation of three-dimensional structure, a new generation of intelligent thermal material systems with wide-range continuous modulation capabilities and breakthrough thermal switching ratios can be constructed.

[0003] In the existing technology, the performance optimization of bismuth ferrite thin film thermal switch materials is mainly limited by two technical bottlenecks: first, the traditional material design method relies on the empirical trial and error mode, which makes it difficult to effectively analyze the structure-activity relationship between the atomic configuration of the ferroelectric domain wall and the thermal transport characteristics; second, the existing molecular dynamics simulation method is constrained by computational complexity. When simulating the thermal conduction behavior of nanoscale multi-domain structures, there are defects such as limited time scale (usually less than 1ns) and insufficient spatial resolution (unable to accurately characterize the 1-10nm domain wall structure). Especially in multiferroic material systems such as bismuth ferrite, the thermal activation migration process of ferroelectric domain walls involves multi-physical field coupling effects, which is difficult to achieve accuracy using conventional simulation methods, which seriously restricts the optimization efficiency of the material's thermal switch performance. Summary of the invention

[0004] The present application provides a simulation method, system and medium for regulating thermal conductivity of ferroelectric domains in bismuth ferrite films, so as to solve the problems that traditional material design methods rely on empirical trial and error mode, which makes it difficult to effectively analyze the structure-activity relationship between the atomic configuration of ferroelectric domain walls and thermal transport properties, and the thermally activated migration process of ferroelectric domain walls involves multi-physical field coupling effects, which makes it difficult to achieve accuracy using conventional simulation methods.

[0005] In a first aspect, the present application provides a simulation method for regulating thermal conductivity of ferroelectric domains in bismuth ferrite films, the method comprising: Establish BFO molecular models with different supercell structures, and then use AIMD technology to simulate the BFO molecular model in canonical ensemble and isothermal and isobaric ensemble, respectively, to obtain the phase space data of BFO at different preset temperatures as the initial training set; Configure the hyperparameters of the GPUMD software, use the NEP tool embedded in the GPUMD software with the initial training set, train the neural evolution potential NEP, until the initial training set can describe all the preset target MD scenarios, and then obtain the NEP potential function; Obtain single-domain and double-domain BFO molecular models, simulate the thermal conductivity of the single-domain and double-domain BFO molecular models in the preset temperature range in GPUMD software based on the NEP potential function, obtain the thermal conductivity of the single-domain state and the thermal conductivity of the double-domain state at each preset temperature, and calculate the thermal switching ratio between the single-domain and double-domain BFO molecular models at each preset temperature; then obtain the change curve of the thermal switching ratio and temperature change, obtain the theoretical maximum value of the thermal switching ratio with temperature change, and obtain the specific temperature corresponding to the theoretical maximum value; A single-domain BFO molecular model with a preset number of domain walls is obtained, and at a specific temperature, an isothermal isobaric ensemble and a canonical ensemble simulation are performed on the BFO molecular model with a preset number of domain walls to obtain a thermal switching ratio between each preset number of domain walls and the single-domain BFO molecular model; Obtain the variation curve of the thermal switching ratio of the BFO molecular model as the number of preset domain walls changes, and obtain the specific number of domain walls corresponding to the theoretical maximum value; The specific temperature and the specific number of domain walls corresponding to the theoretical maximum value are taken as the final guiding conditions.

[0006] In one implementation of the present application, BFO molecular models with different supercell structures are established, and then the BFO molecular models are simulated by canonical ensemble and isothermal and isobaric ensemble using AIMD technology to obtain the phase space data of BFO at different preset temperatures as the initial training set, specifically including: Construct a 1×1×1 BFO unit cell, use phonopy software to obtain supercells with different numbers of atoms, perform AIMD simulations at different preset temperatures in the first-principles calculation program, and obtain an initial training set; wherein the preset temperatures include at least: 100K, 200K, 300K, 400K, 500K and 600K; Use ASE's read function to read the BFO model.xyz file of the 3×3×3 supercell, and store the read atomic structure data in the result file. Use ASE's write function to save the atomic structure data in the result file in a format that can be called by the first-principles program. Use normal distribution to randomly generate perturbations of atomic positions. Call the generate structure file command of the System object to save each perturbation system as a new crystal structure and add it to the initial training set.

[0007] In one implementation of the present application, the neural evolution potential NEP is trained using the NEP tool embedded in the GPUMD software using the initial training set until the initial training set can describe all preset target MD scenarios, thereby obtaining the NEP potential function, specifically including: S0, performing single-point energy calculation on the structures in the initial training set in a first-principles program to obtain energy and potential information, thereby obtaining the first converged training data, and thereby obtaining the first converged training set; S1. Use the first converged training set to train the neural evolution potential NEP to obtain the 1-NEP function; S2. Utilization The supercell atomic BFO molecular model, 1-NEP function and preset condition information are used to obtain the phase space between 100K-700K; S3, using farthest point sampling to select BFO molecular models that meet the preset limit range from the phase space; S4, performing single-point energy calculation on the BFO molecular model that meets the preset limit range in the first principle program to obtain energy and potential information, thereby obtaining converged second training data, and then adding the second training data to the first converged training set to obtain a second converged training set; S5, using the second converged training set to train the neural evolution potential NEP to obtain a 2-NEP function; S6. Repeat S2-S5 until the preset stability condition is met to obtain the final NEP potential function.

[0008] In one implementation of the present application, a single-domain and dual-domain BFO molecular model is obtained, and based on the NEP potential function, the thermal conductivity of the single-domain and dual-domain BFO molecular models is simulated in the GPUMD software within a preset temperature range, the thermal conductivity of the single-domain state and the thermal conductivity of the dual-domain state at each preset temperature are obtained, and the thermal switching ratio between the single-domain and dual-domain BFO molecular models at each preset temperature is calculated, specifically including: Use HNEMD to simulate and obtain several preset temperatures at preset temperature intervals, and then obtain the thermal conductivity of the single-domain and double-domain BFO molecular models corresponding to each preset temperature; By formula: , calculate the thermal switching ratio between the single-domain and double-domain BFO molecular models corresponding to the current preset temperature ; in, represents the thermal conductivity of the single-domain BFO molecular model, represents the thermal conductivity of the dual-domain BFO molecular model.

[0009] In one implementation of the present application, a BFO molecular model with a single domain and a preset number of domain walls is obtained, and at a specific temperature, an isothermal isobaric ensemble and a canonical ensemble simulation are performed on the BFO molecular model with a preset number of domain walls to obtain a thermal switching ratio between each preset number of domain walls and the single domain BFO molecular model, specifically including: Perform HNEMD simulation calculations at specific temperatures; Use isothermal and isobaric ensemble to release stress in BFO molecular model; Use canonical ensemble to perform molecular dynamics simulation, with the driving factor along the y direction, and output the thermal conductivity of the BFO molecular model with a single domain and a preset number of domain walls once per preset step; The thermal switching ratio between the single domain and the BFO molecular models with a preset number of domain walls corresponding to a specific temperature is calculated by comparing the thermal conductivity of the single domain BFO molecular model with the thermal conductivity of the BFO molecular model with a preset number of domain walls.

[0010] In a second aspect, the present application provides a simulation system for regulating thermal conductivity of ferroelectric domains in bismuth ferrite films, the system comprising: The training set acquisition module is used to establish BFO molecular models with different supercell structures, and then use AIMD technology to simulate the BFO molecular model in canonical ensemble and isothermal and isobaric ensemble, respectively, to obtain the phase space data of BFO at different preset temperatures as the initial training set; The function acquisition module is used to configure the hyperparameters of the GPUMD software, and use the NEP tool embedded in the GPUMD software to train the neural evolution potential NEP using the initial training set until the initial training set can describe all preset target MD scenarios, thereby obtaining the NEP potential function; The temperature acquisition module is used to obtain the single-domain and double-domain BFO molecular models. Based on the NEP potential function, the thermal conductivity of the single-domain and double-domain BFO molecular models is simulated in the GPUMD software within a preset temperature range to obtain the thermal conductivity of the single-domain state and the thermal conductivity of the double-domain state at each preset temperature, and calculate the thermal switching ratio between the single-domain and double-domain BFO molecular models at each preset temperature; then, the variation curve of the thermal switching ratio and the temperature change is obtained, the theoretical maximum value of the thermal switching ratio with temperature change is obtained, and the specific temperature corresponding to the theoretical maximum value is obtained; A domain wall number acquisition module is used to acquire a BFO molecular model with a single domain and a preset number of domain walls, and to perform isothermal and isobaric ensemble and canonical ensemble simulations on the BFO molecular model with a preset number of domain walls at a specific temperature to obtain a thermal switching ratio between each preset number of domain walls and the single domain BFO molecular model; to obtain a curve of the thermal switching ratio of the BFO molecular model as the number of domain walls changes, and to obtain a specific number of domain walls corresponding to the theoretical maximum value; The guidance module is used to take the specific temperature and the specific number of domain walls corresponding to the theoretical maximum value as the final guidance conditions.

[0011] In one implementation of the present application, the training set acquisition module includes a training set acquisition unit. To construct a 1×1×1 BFO unit cell, supercells with different numbers of atoms are obtained using phonopy software, and AIMD simulations with different preset temperatures are performed in a first-principles calculation program to obtain an initial training set; wherein the preset temperatures include at least: 100K, 200K, 300K, 400K, 500K and 600K; Use ASE's read function to read the BFO model.xyz file of the 3×3×3 supercell, and store the read atomic structure data in the result file. Use ASE's write function to save the atomic structure data in the result file in a format that can be called by the first-principles program. Use normal distribution to randomly generate perturbations of atomic positions. Call the generate structure file command of the System object to save each perturbation system as a new crystal structure and add it to the initial training set.

[0012] In one implementation of the present application, the temperature acquisition module includes a temperature acquisition unit. Used to simulate using HNEMD, obtain several preset temperatures according to preset temperature intervals, and then obtain the thermal conductivity of the single-domain and double-domain BFO molecular models corresponding to each preset temperature; By formula: , calculate the thermal switching ratio between the single-domain and double-domain BFO molecular models corresponding to the current preset temperature ; in, represents the thermal conductivity of the single-domain BFO molecular model, represents the thermal conductivity of the dual-domain BFO molecular model.

[0013] In one implementation of the present application, the domain wall quantity acquisition module includes a domain wall quantity acquisition unit. Used to perform HNEMD simulation calculations at specific temperatures; Use isothermal and isobaric ensemble to release stress in BFO molecular model; Use canonical ensemble to perform molecular dynamics simulation, with the driving factor along the y direction, and output the thermal conductivity of the BFO molecular model with a single domain and a preset number of domain walls once per preset step; The thermal switching ratio between the single domain and the BFO molecular models with a preset number of domain walls corresponding to a specific temperature is calculated by comparing the thermal conductivity of the single domain BFO molecular model with the thermal conductivity of the BFO molecular model with a preset number of domain walls.

[0014] In a third aspect, the present application provides a non-volatile computer storage medium having computer instructions stored thereon, which when executed implement a simulation method for regulating thermal conductivity of ferroelectric domains in a bismuth ferrite film as described above.

[0015] It can be seen from the above technical solutions that this application has the following advantages: 1. Achieved accurate analysis of the structure-activity relationship between ferroelectric domain wall atomic configuration and heat transport: By combining AIMD (ab initio molecular dynamics) and the fourth-generation NEP (neural evolved potential) technology, high-precision modeling of the atomic-level configuration of the ferroelectric domain wall of the BFO film (BFO molecular model) was achieved, overcoming the problem of insufficient accuracy of traditional empirical potential functions in describing complex polarization-lattice coupling effects, and quantitatively revealing for the first time the nonlinear correlation between the number and distribution of domain walls and thermal conductivity.

[0016] 2. Achieved efficient solution to the multi-physics coupling thermally activated migration simulation problem: By using the NEP tool embedded in GPUMD, multi-field coupled dynamic simulation of ferroelectric domain polarization reversal (electric field response), lattice vibration (heat transport), and stress relaxation (mechanical deformation) is realized under a unified framework. Compared with the traditional finite element method, the computational efficiency is improved, and the transient heat flux fluctuation characteristics at the sub-picosecond level during domain wall migration can be captured.

[0017] 3. Achieved the directional design of high-performance thermal switch materials: By establishing theoretical variation curves of the thermal switching ratio, temperature, and number of domain walls, the optimal operating temperature range (such as the specific temperature corresponding to the theoretical maximum value) and the critical domain wall density threshold (such as the specific number of domain walls) for thermal conductivity regulation of the BFO molecular model are clarified, providing quantifiable design criteria for material doping and domain engineering strategies for low-power thermal management devices.

[0018] 4. Breaking through the computational bottleneck of the traditional trial-and-error model: A method combining adaptive generation of supercell structures with iterative training of NEP potential functions is adopted to reduce the computational resource consumption of all-atom MD simulations, while covering the cross-scale thermal transport behavior prediction from single-domain homogeneous phase to multi-domain heterogeneous phase, laying the foundation for high-throughput virtual screening of complex ferroelectric materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 This is a flow chart of a simulation method for regulating thermal conductivity of ferroelectric domains in a bismuth ferrite film provided in an embodiment of the present application.

[0021] Figure 2 This is a schematic diagram of the internal structure of a simulation system for regulating thermal conductivity of ferroelectric domains in a bismuth ferrite film provided in an embodiment of the present application.

[0022] Figure 3 This is a schematic diagram of the 180° domain structure of a BFO molecular model provided in an embodiment of the present application.

[0023] Figure 4 This is a schematic diagram of the change in the thermal switching ratio of the BFO molecular model provided in an embodiment of the present application with temperature.

[0024] Figure 5 Schematic diagram of the variation of the thermal switching ratio of the BFO molecular model provided in the embodiment of the present application with the number of domain walls at 300K. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] It should be understood by those skilled in the art that the embodiments described below are only preferred embodiments of the present disclosure, and do not mean that the present disclosure can only be implemented through the preferred embodiments. The preferred embodiments are only used to explain the technical principles of the present disclosure, and are not used to limit the protection scope of the present disclosure. Based on the preferred embodiments provided by the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work should still fall within the protection scope of the present disclosure.

[0027] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0028] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0029] The embodiment provides a simulation method for regulating thermal conductivity of ferroelectric domains in bismuth ferrite thin films, such as Figure 1 As shown, the method provided in the embodiment of the present application mainly includes the following steps: Step 110, establish BFO molecular models with different supercell structures, and then use AIMD technology to perform canonical ensemble and isothermal and isobaric ensemble simulations on the BFO molecular models, respectively, to obtain phase space data of BFO at different preset temperatures as an initial training set.

[0030] This step can be specifically as follows: Construct a 1×1×1 BFO unit cell, use phonopy software to obtain supercells with different numbers of atoms, perform AIMD simulations at different preset temperatures in the first-principles calculation program, and obtain an initial training set; wherein the preset temperatures include at least: 100K, 200K, 300K, 400K, 500K and 600K; Use ASE's read function to read the BFO model.xyz file of the 3×3×3 supercell, and store the read atomic structure data in the result file. Use ASE's write function to save the atomic structure data in the result file in a format that can be called by the first-principles program. Use normal distribution to randomly generate perturbations of atomic positions. Call the generate structure file command of the System object to save each perturbation system as a new crystal structure and add it to the initial training set.

[0031] By way of further example, construct BFO unit cell, equivalent pseudo-cubic unit cell lattice constant a = b = c = 3.7893 (obtained by converting the rhombohedral structure into a pseudo-cubic approximation), supercells with different numbers of atoms were obtained using the phonopy software package, and AIMD simulations at different temperatures were performed in the first-principles calculation package to obtain the initial training set. The AIMD simulation process is as follows: to traverse the possible phase space configurations of the BFO model (supercell), the canonical ensemble (NVT) was run at 100K, 200K, 300K, 400K, 500K and 600K, and the Anderson thermostat was used to run for 10ps, and a structure was taken at an interval of 0.2ps, and a total of 300 initial training sets were obtained. Use the read function of ASE (AtomicSimulation Environment) to read the BFO model.xyz file of the 3×3×3 supercell, and store the read atomic structure data in the result file. Use the write function of ASE to save the atomic structure data in the result file in a format that can be called by the first-principles program package, and then call the perturb method of System in the dpdata library to perturb the structure, specifying the perturbation number pert_num=3, the lattice parameter perturbation amplitude cell_pert_fraction=0.01-0.05, and the maximum distance of atomic position perturbation is 0.1-0.5 , atom_pert_style = "normal", that is, normal distribution is used to randomly generate perturbations of atomic positions, and then the generate structure file command of the System object is called to save each perturbation system as a new crystal structure file. A total of 100 structures are obtained and added to the initial training set.

[0032] Step 120, configure the hyperparameters of the GPUMD software, use the initial training set to use the NEP tool embedded in the GPUMD software, train the neural evolution potential NEP, until the initial training set can describe all preset target MD scenarios, and then obtain the NEP potential function.

[0033] It should be noted that the process of configuring the hyperparameters of the GPUMD software can be specifically as follows: Select appropriate hyperparameters in GPUMD (Graphics Processing Units Molecular Dynamics) software, select 8 and 6 for radial and angular cutoff values, select 50 for the number of neurons, and use the default values ​​for others. Use the NEP tool embedded in GPUMD to train the fourth generation NEP (neuroevolution potential), and perform iterations and verifications, that is, perform molecular dynamics simulations in GPUMD, use the farthest point sampling method to compare the GPUMD output results with the training set, select structures in the MD results that have too large errors with the training set data for density functional theory (DFT) single point energy calculations and add them to the training set, and retrain NEP until the training set can effectively describe all target MD scenarios. That is, the training set can cover the thermodynamic / dynamic key areas of the target scenario, and evaluate the reliability of NEP through cross-validation, energy and force error analysis, comparison of thermodynamic properties, etc. In addition, run long-term MD simulations to check its stability and physical rationality. Finally, a NEP potential function that can accurately describe the interaction force between BFO atoms is obtained.

[0034] Among them, the NEP tool embedded in the GPUMD software is used to train the neural evolution potential NEP using the initial training set until the initial training set can describe all preset target MD scenarios, and then the NEP potential function is obtained, which can be specifically: S0, performing single-point energy calculation on the structures in the initial training set in a first-principles program to obtain energy and potential information, thereby obtaining the first converged training data, and thereby obtaining the first converged training set; S1. Use the first converged training set to train the neural evolution potential NEP to obtain the 1-NEP function; S2. Utilization The supercell atomic BFO molecular model, 1-NEP function and preset condition information are used to obtain the phase space between 100K-700K; S3, using farthest point sampling to select BFO molecular models that meet the preset limit range from the phase space; S4, performing single-point energy calculation on the BFO molecular model that meets the preset limit range in the first principle program to obtain energy and potential information, thereby obtaining converged second training data, and then adding the second training data to the first converged training set to obtain a second converged training set; S5, using the second converged training set to train the neural evolution potential NEP to obtain a 2-NEP function; S6. Replace the 2-NEP function with the 1-NEP function, and repeat S2-S5 until the preset stability condition is met to obtain the final NEP potential function.

[0035] It should be noted that in order to ensure that accurate energy and potential information in the training set can be obtained during NEP training, single-point energy calculations are performed on the data in the initial training set in the first-principles program. To ensure that all structural energies and forces converge to a unified value, the cutoff energy ENCUT is set to 600. , K-point selection uses KSPACING=0.2, and the force convergence criterion for each atom is below , the energy convergence criterion is . After the calculation is completed, use the bash script to check whether each structure has converged, that is, use the find command to find all result files, and judge whether the structure has converged based on NSW, NELM, the actual number of iterations and whether "aborting loop because EDIFF is reached" appears. Use wc -l to count the number of converged result files, and N_count records the serial number. Then for each converged result file, syst_numb_atom extracts the number of atoms, latt extracts the lattice vector information, ener extracts the total energy, viri_logi extracts the posit information, ion_numb_arra and ion_symb_arra extract the number and symbol of each atom respectively, and then use the paste command to merge all the information and write it to the output file. The converged structure is extracted to obtain the first training data, and the unconverged ones are discarded.

[0036] S1 can be: select appropriate hyperparameters in the GPUMD software package to train the NEP-4 potential function using the first training data. To ensure the accuracy of the potential function, you can select cut off = 8 6, the number of neurons neuron = 50, and the others are default values. After the training, the 1-NEP function is obtained. The sign that the training tends to converge is that the energy, force and stress information of the system tend to be flat as the number of training steps increases.

[0037] The process of S2-S5 can be: Use 1-NEP function to perform molecular dynamics simulation exploration in GPUMD The possible phase space of BFO with 135 supercell atoms at 100K-700K. First, perform the canonical ensemble (NVT) with a step size of 1fs, a total of 500ps, and output a structure every 1ps; then perform the isothermal isobaric ensemble (NPT) with a step size of 1fs, a total of 0.5ns, and output a structure every 10ps. After obtaining the output file dump.xyz, use the farthest point sampling to select the structure, and limit the range to min_distance=0.02. Use the same parameter file to perform DFT single-point energy calculation on the selected structure, add the calculated structure (second training data) to the first converged training set, obtain the second converged training set, and retrain NEP-4 to obtain the 2-NEP function. The process of S6 can be: repeat the iterative operations of S2-S5 until The BFO of the supercell runs stably for 20ns in the isothermal and isobaric ensemble NPT, and the training set can fully describe the motion trajectory of BFO within 20ns at the same temperature. The 2-NEP function at this time is the final version, denoted as the NEP potential function.

[0038] Step 130, obtain single-domain and dual-domain BFO molecular models, simulate the thermal conductivity of the single-domain and dual-domain BFO molecular models in the GPUMD software within a preset temperature range based on the NEP potential function, obtain the thermal conductivity of the single-domain state and the thermal conductivity of the dual-domain state at each preset temperature, calculate the thermal switching ratio between the single-domain and dual-domain BFO molecular models at each preset temperature; then obtain the change curve of the thermal switching ratio and temperature change, obtain the theoretical maximum value of the thermal switching ratio with temperature change, and obtain the specific temperature corresponding to the theoretical maximum value.

[0039] The process of calculating the thermal switching ratio between the single-domain and double-domain BFO molecular models at each preset temperature can be: Use HNEMD to simulate and obtain several preset temperatures at preset temperature intervals, and then obtain the thermal conductivity of the single-domain and double-domain BFO molecular models corresponding to each preset temperature; By formula: , calculate the thermal switching ratio between the single-domain and double-domain BFO molecular models corresponding to the current preset temperature ;in, represents the thermal conductivity of the single-domain BFO molecular model, represents the thermal conductivity of the dual-domain BFO molecular model.

[0040] More specifically, this step may be as follows: Materials Studio was used to construct BFO molecular models with different domain structures: a 180° single domain structure (0DW) of BFO and a 180° double domain structure (1DW) with a parallel domain wall. Figure 3 As shown. The two structures are expanded along the x, y, and z axes respectively. , a total of 147,000 atoms.

[0041] Use HNEMD to simulate and calculate the thermal conductivity of two domain structures between 200K and 500K, with an interval of 50K. In the editor of GPUMD input file run.in, first initialize the velocity of all atoms in the lattice, for example, at 200K, set velocity to 200, then use isothermal and isobaric ensemble NPT to release the stress in the structure and relax it fully until the structure converges. Then use canonical ensemble NVT to perform molecular dynamics simulation for 20ns, with the driving factor The average thermal conductivity result is output every 1000 steps along the y direction. To ensure the accuracy of the data and reduce the error, each HNEMD simulation is performed 5 times independently to obtain the average.

[0042] The formula for calculating the thermal conductivity k of the BFO molecular model using HNEMD is: ,in In the non-equilibrium state, Heat flux in the direction In simulation time The ensemble average within represents the thermal conductivity tensor, represents the temperature of the system, represents the volume of the system, non-equilibrium heat flux Is driven by external factors The data output by GPUMD was used for fitting in Python, and finally the variation of thermal conductivity of the two BFO molecular model structures with temperature was obtained. The obtained thermal conductivity data was used in Origin to fit the variation curve of thermal switching ratio with temperature. Figure 4 The BFO molecular model thermal switch changes with temperature. Finally, the maximum thermal switch ratio of the 180° domain wall BFO ​​molecular model at 300K is calculated. .

[0043] Step 140, obtain a BFO molecular model with a single domain and a preset number of domain walls, perform isothermal and isobaric ensemble and canonical ensemble simulations on the BFO molecular model with a preset number of domain walls at a specific temperature, and obtain a thermal switching ratio between each preset number of domain walls and the single domain BFO molecular model; obtain a variation curve of the thermal switching ratio of the BFO molecular model as the preset number of domain walls changes, and obtain the specific number of domain walls corresponding to the theoretical maximum value.

[0044] In some embodiments, the calculation process for obtaining the thermal switching ratio between each preset number of domain walls and the single-domain BFO molecular model may be: Perform HNEMD simulation calculations at specific temperatures; Use isothermal and isobaric ensemble to release stress in BFO molecular model; Use canonical ensemble to perform molecular dynamics simulation, with the driving factor along the y direction, and output the thermal conductivity of the BFO molecular model with a single domain and a preset number of domain walls once per preset step; The thermal switching ratio between the single domain and the BFO molecular models with a preset number of domain walls corresponding to a specific temperature is calculated by comparing the thermal conductivity of the single domain BFO molecular model with the thermal conductivity of the BFO molecular model with a preset number of domain walls.

[0045] More specifically, this step may be as follows: In Materials Studio, a BFO molecular model structure with different numbers of domain walls was constructed. The angle was 180°. The five numbers of domain walls were 1, 3, 5, 7, and 9, for a total of five structures.

[0046] Perform HNEMD simulation at 300K (assuming 300K is the specific temperature). In the GPUMD input file run.in, initialize the velocity of all atoms in the lattice to 300. First, use the isothermal and isobaric ensemble NPT to release the stress in the structure. After sufficient relaxation, the structure will converge to a certain size. Then use the canonical ensemble NVT to perform a 20ns molecular dynamics simulation. The driving factor The average thermal conductivity result is output every 1000 steps along the y direction. To ensure the accuracy of the data and reduce the error, each HNEMD simulation is performed 5 times independently to obtain the average.

[0047] The data output by GPUMD is used for fitting in Python to obtain the variation of thermal conductivity of the domain structure of the BFO molecular model with the number of domain walls. Figure 5 The BFO molecular model has the largest thermal switching ratio when the number of 180° domain walls is 9 at 300K. .

[0048] Step 150: taking the specific temperature and the specific number of domain walls corresponding to the theoretical maximum value as the final guiding conditions.

[0049] More specifically, this step may be as follows: The theoretical conditions for the maximum thermal switching ratio of the BFO molecular model are obtained. For example, at 300K (assuming 300K is the specific temperature), when the number of 180° domain walls is 9, the thermal switching ratio of the BFO molecular model reaches its maximum value, λ=1.52.

[0050] In addition, this application Figure 2 A simulation system for regulating thermal conductivity of ferroelectric domains in bismuth ferrite thin films provided in the embodiments of the present application. Figure 2 As shown, the system provided in the embodiment of the present application mainly includes: The training set acquisition module 210 is used to establish BFO molecular models with different supercell structures, and then use AIMD technology to perform canonical ensemble and isothermal and isobaric ensemble simulations on the BFO molecular models to obtain phase space data of BFO at different preset temperatures as an initial training set.

[0051] The training set acquisition module 210 includes a training set acquisition unit, To construct a 1×1×1 BFO unit cell, supercells with different numbers of atoms are obtained using phonopy software, and AIMD simulations with different preset temperatures are performed in a first-principles calculation program to obtain an initial training set; wherein the preset temperatures include at least: 100K, 200K, 300K, 400K, 500K and 600K; Use ASE's read function to read the BFO model.xyz file of the 3×3×3 supercell, and store the read atomic structure data in the result file. Use ASE's write function to save the atomic structure data in the result file in a format that can be called by the first-principles program. Use normal distribution to randomly generate perturbations of atomic positions. Call the generate structure file command of the System object to save each perturbation system as a new crystal structure and add it to the initial training set.

[0052] The function acquisition module 220 is used to configure the hyperparameters of the GPUMD software, and train the neural evolution potential NEP using the NEP tool embedded in the GPUMD software using the initial training set until the initial training set can describe all preset target MD scenarios, thereby obtaining the NEP potential function.

[0053] The temperature acquisition module 230 is used to obtain the single-domain and double-domain BFO molecular models. Based on the NEP potential function, the thermal conductivity of the single-domain and double-domain BFO molecular models is simulated in the GPUMD software within a preset temperature range to obtain the thermal conductivity of the single-domain state and the thermal conductivity of the double-domain state at each preset temperature, and calculate the thermal switching ratio between the single-domain and double-domain BFO molecular models at each preset temperature; then obtain the change curve of the thermal switching ratio and the temperature change, obtain the theoretical maximum value of the thermal switching ratio with temperature change, and obtain the specific temperature corresponding to the theoretical maximum value.

[0054] The temperature acquisition module 230 includes a temperature acquisition unit, Used to simulate using HNEMD, obtain several preset temperatures according to preset temperature intervals, and then obtain the thermal conductivity of the single-domain and double-domain BFO molecular models corresponding to each preset temperature; By formula: , calculate the thermal switching ratio between the single-domain and double-domain BFO molecular models corresponding to the current preset temperature ; in, represents the thermal conductivity of the single-domain BFO molecular model, represents the thermal conductivity of the dual-domain BFO molecular model.

[0055] The domain wall number acquisition module 240 is used to obtain a BFO molecular model with a single domain and a preset number of domain walls. At a specific temperature, the BFO molecular model with a preset number of domain walls is simulated by an isothermal isobaric ensemble and a canonical ensemble to obtain a thermal switching ratio between each preset number of domain walls and the single domain BFO molecular model; a variation curve of the thermal switching ratio of the BFO molecular model with the preset number of domain walls is obtained to obtain the specific number of domain walls corresponding to the theoretical maximum value.

[0056] The domain wall quantity acquisition module 240 includes a domain wall quantity acquisition unit, Used to perform HNEMD simulation calculations at specific temperatures; Use isothermal and isobaric ensemble to release stress in BFO molecular model; Use canonical ensemble to perform molecular dynamics simulation, with the driving factor along the y direction, and output the thermal conductivity of the BFO molecular model with a single domain and a preset number of domain walls once per preset step; The thermal switching ratio between the single domain and the BFO molecular models with a preset number of domain walls corresponding to a specific temperature is calculated by comparing the thermal conductivity of the single domain BFO molecular model with the thermal conductivity of the BFO molecular model with a preset number of domain walls.

[0057] The guidance module 250 is used to use the specific temperature and the specific number of domain walls corresponding to the theoretical maximum value as the final guidance condition.

[0058] In addition, an embodiment of the present application further provides a non-volatile computer storage medium having executable instructions stored thereon, and when the executable instructions are executed, a simulation method for regulating thermal conductivity of ferroelectric domains in a bismuth ferrite film as described above is implemented.

[0059] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A simulation method for regulating thermal conductivity of ferroelectric domains in bismuth ferrite thin films, characterized in that: The method comprises: Establish BFO molecular models with different supercell structures, and then use AIMD technology to simulate the BFO molecular model in canonical ensemble and isothermal and isobaric ensemble, respectively, to obtain the phase space data of BFO at different preset temperatures as the initial training set; Configure the hyperparameters of the GPUMD software, use the NEP tool embedded in the GPUMD software with the initial training set, train the neural evolution potential NEP, until the initial training set can describe all the preset target MD scenarios, and then obtain the NEP potential function; Obtain single-domain and double-domain BFO molecular models, simulate the thermal conductivity of the single-domain and double-domain BFO molecular models in the preset temperature range in GPUMD software based on the NEP potential function, obtain the thermal conductivity of the single-domain state and the thermal conductivity of the double-domain state at each preset temperature, and calculate the thermal switching ratio between the single-domain and double-domain BFO molecular models at each preset temperature; then obtain the change curve of the thermal switching ratio and temperature change, obtain the theoretical maximum value of the thermal switching ratio with temperature change, and obtain the specific temperature corresponding to the theoretical maximum value; Obtain a BFO molecular model with a single domain and a preset number of domain walls, perform isothermal and isobaric ensemble and canonical ensemble simulations on the BFO molecular model with a preset number of domain walls at a specific temperature, and obtain a thermal switching ratio between each preset number of domain walls and the single domain BFO molecular model; obtain a curve of the thermal switching ratio of the BFO molecular model as the preset number of domain walls changes, and obtain the specific number of domain walls corresponding to the theoretical maximum value; The specific temperature and the specific number of domain walls corresponding to the theoretical maximum value are taken as the final guiding conditions.

2. The simulation method for regulating thermal conductivity of ferroelectric domains in bismuth ferrite thin films according to claim 1, characterized in that: Establish BFO molecular models with different supercell structures, and then use AIMD technology to simulate the BFO molecular model in canonical ensemble and isothermal and isobaric ensemble, respectively, to obtain the phase space data of BFO at different preset temperatures as the initial training set, including: Construct a 1×1×1 BFO unit cell, use phonopy software to obtain supercells with different numbers of atoms, perform AIMD simulations at different preset temperatures in the first-principles calculation program, and obtain an initial training set; wherein the preset temperatures include at least: 100K, 200K, 300K, 400K, 500K and 600K; Use ASE's read function to read the BFO model.xyz file of the 3×3×3 supercell, and store the read atomic structure data in the result file. Use ASE's write function to save the atomic structure data in the result file in a format that can be called by the first-principles program. Use normal distribution to randomly generate perturbations of atomic positions. Call the generate structure file command of the System object to save each perturbation system as a new crystal structure and add it to the initial training set.

3. The simulation method for regulating thermal conductivity of ferroelectric domains in bismuth ferrite thin films according to claim 1, characterized in that: Use the NEP tool embedded in the GPUMD software to train the neural evolution potential NEP using the initial training set until the initial training set can describe all the preset target MD scenarios, and then obtain the NEP potential function, including: S0, performing single-point energy calculation on the structures in the initial training set in a first-principles program to obtain energy and potential information, thereby obtaining the first converged training data, and thereby obtaining the first converged training set; S1. Use the first converged training set to train the neural evolution potential NEP to obtain the 1-NEP function; S2. Utilization The supercell atomic BFO molecular model, 1-NEP function and preset condition information are used to obtain the phase space between 100K-700K; S3, using farthest point sampling to select BFO molecular models that meet the preset limit range from the phase space; S4, performing single-point energy calculation on the BFO molecular model that meets the preset limit range in the first principle program to obtain energy and potential information, thereby obtaining converged second training data, and then adding the second training data to the first converged training set to obtain a second converged training set; S5, using the second converged training set to train the neural evolution potential NEP to obtain a 2-NEP function; S6. Replace the 2-NEP function with the 1-NEP function, and repeat S2-S5 until the preset stability condition is met to obtain the final NEP potential function.

4. The method for simulating thermal conductivity of ferroelectric domains in bismuth ferrite thin films according to claim 1, characterized in that: Obtain single-domain and double-domain BFO molecular models. Based on the NEP potential function, simulate the thermal conductivity of the single-domain and double-domain BFO molecular models in the preset temperature range in GPUMD software, obtain the thermal conductivity of the single-domain state and the thermal conductivity of the double-domain state at each preset temperature, and calculate the thermal switching ratio between the single-domain and double-domain BFO molecular models at each preset temperature, including: Use HNEMD to simulate and obtain several preset temperatures at preset temperature intervals, and then obtain the thermal conductivity of the single-domain and double-domain BFO molecular models corresponding to each preset temperature; By formula: , calculate the thermal switching ratio between the single-domain and double-domain BFO molecular models corresponding to the current preset temperature ; in, represents the thermal conductivity of the single-domain BFO molecular model, represents the thermal conductivity of the dual-domain BFO molecular model.

5. The method for simulating thermal conductivity of ferroelectric domains in bismuth ferrite thin films according to claim 1, characterized in that: Obtain a single-domain BFO molecular model with a preset number of domain walls, perform isothermal and isobaric ensemble and canonical ensemble simulations on the BFO molecular model with a preset number of domain walls at a specific temperature, and obtain a thermal switching ratio between each preset number of domain walls and the single-domain BFO molecular model, specifically including: Perform HNEMD simulation calculations at specific temperatures; Use isothermal and isobaric ensemble to release stress in BFO molecular model; Use canonical ensemble to perform molecular dynamics simulation, with the driving factor along the y direction, and output the thermal conductivity of the BFO molecular model with a single domain and a preset number of domain walls once per preset step; The thermal switching ratio between the single domain and the BFO molecular models with a preset number of domain walls corresponding to a specific temperature is calculated by comparing the thermal conductivity of the single domain BFO molecular model with the thermal conductivity of the BFO molecular model with a preset number of domain walls.

6. A simulation system for regulating thermal conductivity of ferroelectric domains in bismuth ferrite films, characterized in that: The system comprises: The training set acquisition module is used to establish BFO molecular models with different supercell structures, and then use AIMD technology to simulate the BFO molecular model in canonical ensemble and isothermal and isobaric ensemble, respectively, to obtain the phase space data of BFO at different preset temperatures as the initial training set; The function acquisition module is used to configure the hyperparameters of the GPUMD software, and use the NEP tool embedded in the GPUMD software to train the neural evolution potential NEP using the initial training set until the initial training set can describe all preset target MD scenarios, thereby obtaining the NEP potential function; The temperature acquisition module is used to obtain the single-domain and double-domain BFO molecular models. Based on the NEP potential function, the thermal conductivity of the single-domain and double-domain BFO molecular models is simulated in the GPUMD software within a preset temperature range to obtain the thermal conductivity of the single-domain state and the thermal conductivity of the double-domain state at each preset temperature, and calculate the thermal switching ratio between the single-domain and double-domain BFO molecular models at each preset temperature; then, the variation curve of the thermal switching ratio and the temperature change is obtained, the theoretical maximum value of the thermal switching ratio with temperature change is obtained, and the specific temperature corresponding to the theoretical maximum value is obtained; A domain wall number acquisition module is used to acquire a BFO molecular model with a single domain and a preset number of domain walls, and to perform isothermal and isobaric ensemble and canonical ensemble simulations on the BFO molecular model with a preset number of domain walls at a specific temperature to obtain a thermal switching ratio between each preset number of domain walls and the single domain BFO molecular model; to obtain a curve of the thermal switching ratio of the BFO molecular model as the number of domain walls changes, and to obtain a specific number of domain walls corresponding to the theoretical maximum value; The guidance module is used to take the specific temperature and the specific number of domain walls corresponding to the theoretical maximum value as the final guidance conditions.

7. The simulation system for regulating thermal conductivity of ferroelectric domains in bismuth ferrite thin films according to claim 6, characterized in that: The training set acquisition module includes a training set acquisition unit, To construct a 1×1×1 BFO unit cell, supercells with different numbers of atoms are obtained using phonopy software, and AIMD simulations with different preset temperatures are performed in a first-principles calculation program to obtain an initial training set; wherein the preset temperatures include at least: 100K, 200K, 300K, 400K, 500K and 600K; Use ASE's read function to read the BFO model.xyz file of the 3×3×3 supercell, and store the read atomic structure data in the result file. Use ASE's write function to save the atomic structure data in the result file in a format that can be called by the first-principles program. Use normal distribution to randomly generate perturbations of atomic positions. Call the generate structure file command of the System object to save each perturbation system as a new crystal structure and add it to the initial training set.

8. The simulation system for regulating thermal conductivity of ferroelectric domains in bismuth ferrite thin films according to claim 6, characterized in that: The temperature acquisition module includes a temperature acquisition unit. Used to simulate using HNEMD, obtain several preset temperatures according to preset temperature intervals, and then obtain the thermal conductivity of the single-domain and double-domain BFO molecular models corresponding to each preset temperature; By formula: , calculate the thermal switching ratio between the single-domain and double-domain BFO molecular models corresponding to the current preset temperature ; in, represents the thermal conductivity of the single-domain BFO molecular model, represents the thermal conductivity of the dual-domain BFO molecular model.

9. The simulation system for regulating thermal conductivity of ferroelectric domains in bismuth ferrite thin films according to claim 6, characterized in that: The domain wall quantity acquisition module includes a domain wall quantity acquisition unit, Used to perform HNEMD simulation calculations at specific temperatures; Use isothermal and isobaric ensemble to release stress in BFO molecular model; Use canonical ensemble to perform molecular dynamics simulation, with the driving factor along the y direction, and output the thermal conductivity of the BFO molecular model with a single domain and a preset number of domain walls once per preset step; The thermal switching ratio between the single domain and the BFO molecular models with a preset number of domain walls corresponding to a specific temperature is calculated by comparing the thermal conductivity of the single domain BFO molecular model with the thermal conductivity of the BFO molecular model with a preset number of domain walls.

10. A non-volatile computer storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, the simulation method of regulating thermal conductivity of ferroelectric domains in a bismuth ferrite film according to any one of claims 1 to 5 is implemented.

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