Method and device for quickly calculating infrared reflection spectrum of crystal

Abstract

The invention discloses a rapid calculation method of a crystal infrared reflection spectrum, which generates a data set through a VASP with less time consumption, is used for training a moment tensor machine learning potential (MTP), further accurately and effectively simulates a high-order force constant, avoids the process of using a first principle to calculate the high-order force constant, consumes a large amount of time and calculation resources, and improves the calculation efficiency. Therefore, phonon vibration parameters are extracted in an accelerated manner, and then the parameters are incorporated into an infrared vibration model to calculate the dielectric function and the infrared reflectivity of the material. Compared with a traditional method, the vibration characteristic can be accurately measured with the precision of the first principle level, empirical parameters are not needed, and the calculation cost is greatly reduced by more than two orders of magnitude; for a low symmetric structure, the calculation cost can even be reduced by three orders of magnitude. The method is helpful for bridging the long-term difference between low calculation speed of a first principle of infrared optical characteristics and low calculation precision of molecular dynamics, and provides a powerful tool for accelerating discovery and design of infrared optical materials.

Description

Technical Field

[0001] The present invention belongs to the technical field of infrared optics, and in particular relates to a method and device for quickly calculating a crystal infrared reflection spectrum. Background Art

[0002] Infrared optical materials play a crucial role in fields such as infrared detection, medical infrared imaging, astronomy, and spectroscopy. The successful and widespread application of infrared optical materials critically depends on their infrared optical properties, which are considered the material's fingerprint. Therefore, in-depth capture of these fingerprints is fundamental to the development of infrared optical materials. While some experimental techniques have recently provided opportunities for identifying microscopic features, rapidly and accurately characterizing a material's intrinsic infrared optical properties remains challenging. Advances in theoretical modeling and computing power have reshaped materials science, making accurate calculations of infrared optical properties possible. Early theoretical studies on the characterization of infrared vibrational features primarily employed numerical calculations or molecular dynamics (MD) simulations. While these methods have improved characterization efficiency and applicability, they also have significant limitations. They are generally only able to calculate the positions of infrared vibrational peaks in non-periodic systems, but cannot compute wavelength-dependent infrared reflectivity. Furthermore, these techniques rely on coupling MD with dipole moment models or numerical schemes, which can reduce the accuracy of calculations for systems with significant quantum effects.

[0003] To overcome these limitations and achieve reliable predictions of infrared spectra for anharmonic periodic systems, quantum-scale first-principles infrared spectroscopy, a recently developed approach, has emerged as a powerful framework. This approach combines density-functional perturbation theory (DFPT) with the phonon Boltzmann transport equation (BTE) and high-order phonon scattering theory, enabling accurate predictions of high-frequency dielectric constants and ground-state infrared phonon mode frequencies. However, despite their exceptionally high prediction accuracy, these approaches come at the cost of significant computational resources and time. In particular, the computational cost of calculating high-order force constants using DFPT rises rapidly with increasing atomic complexity and decreasing crystal symmetry, limiting their application in high-throughput materials screening. Therefore, accelerating the calculation of infrared vibrational spectra without compromising accuracy has become a core challenge in computational infrared spectroscopy. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for rapid calculation of the infrared reflectance spectrum of crystals. Compared with traditional methods, the calculation cost is greatly reduced by more than two orders of magnitude. For low-symmetry structures, the calculation cost can even be reduced by three orders of magnitude.

[0005] In order to solve the above problems, the technical solution of the present invention is: A fast calculation method for crystal infrared reflection spectrum, comprising: AIMD calculations were performed on the crystal structure using VASP to generate a structure-atomic force-energy dataset; Based on the generated dataset, the MTP machine is trained to learn the interatomic potential; The MTP machine learning interatomic potential trained to convergence is used to simulate the corresponding high-order force constants; The generated high-order force constants are input into ShengBTE to solve the Boltzmann equation and obtain the phonon vibration parameters; The obtained phonon vibration parameters are substituted into the Lorentz vibration model to obtain the dielectric function of the crystal material, and the frequency-dependent reflectivity is calculated according to the Fresnel formula to obtain the crystal infrared reflection spectrum.

[0006] According to one embodiment of the present invention, performing AIMD calculation on a crystal structure using VASP to generate a structure-atomic force-energy data set further includes: The first-principles calculation software VASP was used to optimize the structure of the material. The calculation was completed until convergence to obtain the CONTCAR file, which was then converted into a POSCAR file. Use vaspkit tool to expand the POSCAR structure; The expanded structure was subjected to first-principles molecular dynamics calculations using VASP, and NPT simulations were performed using a Langevin thermostat to generate an OUTCAR file. The OUTCAR file was read using MLIP-2 software, and its structure-atomic force-energy data were uniformly sampled to generate a sample data set for MTP training.

[0007] According to one embodiment of the present invention, training the MTP machine learning interatomic potential based on the generated data set further includes: Initialize the MTP training parameters and call the train module of the MLIP-2 software to perform the first training until convergence; The machine learning potential trained for the first time is used as input, and the MLIP-2 software is called to execute the select-add module. Some extreme data are selected and added to the training set. The second training is performed until convergence, and the p.mtp file is obtained.

[0008] According to one embodiment of the present invention, simulating the corresponding high-order force constants from the MTP machine learning interatomic potential trained to convergence further includes: Phonopy software was used to expand POSCAR, and VASP was used to perform density functional perturbation calculations to obtain the BORN charge and high-frequency dielectric constant ε. ∞ , prepare the band.conf file; Put the POSCAR file, p.mtp file, and band.conf into the same folder, execute the interface program mlip_phonopy designed for Phonopy in the MLIP-2 software, obtain the second-order force constant file FORCE_CONSTANTS, and rename it to FORCE_CONSTANTS_2ND; Place the POSCAR file, p.mtp file, and the fake_vasp_calcs.py interface program designed for thirdorder in the same folder. Pass the expansion cell parameters and neighboring atom parameters into the interface program and execute it to obtain the high-order force constant file FORCE_CONSTANTS_3RD.

[0009] According to one embodiment of the present invention, inputting the generated high-order force constants into ShengBTE, solving the Boltzmann equation, and obtaining the phonon vibration parameters further includes: Put the FORCE_CONSTANTS_2ND and FORCE_CONSTANTS_3RD files into the same folder, prepare the CONTROL file, and write the POSCAR file into it; Call ShengBTE software to perform Boltzmann transport calculations and obtain the BTE.w_anharmonic file, from which the degenerate phonon vibration modes near point G and their corresponding phonon damping coefficients are extracted; Append the BORN charge to the CONTROL file, call ShengBTE software again to perform the Boltzmann transport calculation, and obtain the BTE.w_anharmonic file. From it, extract the phonon vibration mode and its corresponding phonon damping coefficient after the BORN correction near the G point. Compare and extract the degenerate phonon vibration mode ω TO and its corresponding phonon damping coefficient γ TO , and the phonon vibration mode ω after splitting LO and its corresponding phonon damping coefficient γ LO , written to the file lorentz.txt.

[0010] According to one embodiment of the present invention, substituting the obtained phonon vibration parameters into the Lorentz vibration model to obtain the dielectric function of the crystal material, and calculating the frequency-dependent reflectivity according to the Fresnel formula further includes: Calculate the frequency-dependent dielectric function according to the Lorentz vibration model: Where ε ∞ is the high frequency dielectric constant, ω j and γ jis the jth infrared active phonon vibration mode and the corresponding phonon damping coefficient, which comes from the file lorentz.txt; After obtaining the dielectric function, the Fresnel equation is used to calculate the reflectance spectrum of the infrared optical material:

[0011] A device for rapidly calculating a crystal infrared reflection spectrum, comprising: a data set acquisition module, configured to perform AIMD calculations on crystal structures using VASP to generate structure-atomic force-energy data sets; An atomic potential training module, configured to train the MTP machine learning interatomic potential based on the generated dataset; A high-order force constant calculation module is configured to simulate the corresponding high-order force constants from the MTP machine learning interatomic potential trained to convergence; The phonon vibration parameter calculation module is configured to input the generated high-order force constants into ShengBTE, solve the Boltzmann equation, and obtain the phonon vibration parameters; The infrared reflection spectrum calculation module is configured to substitute the obtained phonon vibration parameters into the Lorentz vibration model to obtain the dielectric function of the crystal material, and calculate the frequency-dependent reflectivity according to the Fresnel formula to obtain the crystal infrared reflection spectrum.

[0012] According to an embodiment of the present invention, the high-order force constant calculation module is further configured to: Phonopy software was used to expand POSCAR, and VASP was used to perform density functional perturbation calculations to obtain the BORN charge and high-frequency dielectric constant ε. ∞ , prepare the band.conf file; Put the POSCAR file, p.mtp file, and band.conf into the same folder, execute the interface program mlip_phonopy designed for Phonopy in the MLIP-2 software, obtain the second-order force constant file FORCE_CONSTANTS, and rename it to FORCE_CONSTANTS_2ND; Place the POSCAR file, p.mtp file, and the fake_vasp_calcs.py interface program designed for thirdorder in the same folder. Pass the expansion cell parameters and neighboring atom parameters into the interface program and execute it to obtain the high-order force constant file FORCE_CONSTANTS_3RD.

[0013] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art: A fast calculation method for the infrared reflectance spectrum of a crystal, described in one embodiment of the present invention, uses a time-efficient first-principles molecular dynamics method to generate a dataset for training a moment tensor machine learning potential (MTP). This method then accurately and efficiently simulates high-order force constants, avoiding the time-consuming and computationally expensive first-principles calculation of high-order force constants. This accelerates the extraction of phonon vibrational parameters, which are then incorporated into an infrared vibrational model to calculate the material's dielectric function and infrared reflectivity. Compared to traditional methods, our method can accurately determine vibrational properties with first-principles accuracy, without the need for empirical parameters. Conservatively, the computational cost is significantly reduced by more than two orders of magnitude, and even by three orders of magnitude for low-symmetry crystal structures. This method helps bridge the long-standing gap between the slow speed of first-principles calculations of infrared optical properties and the low accuracy of molecular dynamics calculations, providing a powerful tool for accelerating the discovery and design of infrared optical materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Flowchart of a method for rapidly calculating the infrared reflectance spectrum of a crystal in one embodiment of the present invention; Figure 2 Schematic diagram comparing the process of calculating the infrared reflectance spectrum of a crystal using traditional first principles and machine learning-based interatomic interaction potential in one embodiment of the present invention; Figure 3 Schematic diagram of the crystal structure in one embodiment of the present invention; Figure 4 Schematic diagram showing a comparison of the infrared reflectance spectra of a crystal calculated using traditional first principles (DFT) and machine learning-based atomic interaction potential (MTP) in one embodiment of the present invention; Figure 5 Schematic diagram comparing the time consumption of the traditional first principles (DFT) and machine learning-based atomic interaction potential (MTP) calculation methods in one embodiment of the present invention. DETAILED DESCRIPTION

[0015] The following is a detailed description of a method for rapidly calculating a crystal infrared reflectance spectrum proposed by the present invention in conjunction with the accompanying drawings and specific examples. The advantages and features of the present invention will become more apparent from the following description and claims.

[0016] Please see Figure 1 This embodiment provides a method for quickly calculating a crystal infrared reflectance spectrum, comprising: AIMD calculations were performed on the crystal structure using VASP to generate a structure-atomic force-energy dataset; Based on the generated dataset, the MTP machine is trained to learn the interatomic potential; The MTP machine learning interatomic potential trained to convergence is used to simulate the corresponding high-order force constants; The generated high-order force constants are input into ShengBTE to solve the Boltzmann equation and obtain the phonon vibration parameters; The obtained phonon vibration parameters are substituted into the Lorentz vibration model to obtain the dielectric function of the crystal material, and the frequency-dependent reflectivity is calculated according to the Fresnel formula to obtain the crystal infrared reflection spectrum.

[0017] This method generates a dataset using a less time-consuming first-principles molecular dynamics method to train the moment tensor machine learning potential (MTP), thereby accurately and efficiently simulating high-order force constants. Figure 2 , the traditional method uses the first principles to calculate high-order force constants, which is a process that consumes a lot of time and computing resources. As the atomic complexity increases and the crystal symmetry decreases, the computational cost rises rapidly. The method of this embodiment can accelerate the extraction of phonon vibration parameters, which are then incorporated into the infrared vibration model to calculate the dielectric function and infrared reflectivity of the material. Therefore, compared with the traditional method, the method of this embodiment can accurately determine the vibration characteristics with first-principles level accuracy without the need for empirical parameters. Under conservative estimates, the computational cost is greatly reduced by more than two orders of magnitude, and for low-symmetry structures, the computational cost can even be reduced by three orders of magnitude. Moreover, the method has certain versatility in a wide range of binary and ternary compounds. Therefore, the present invention helps to bridge the long-standing gap between the slow speed of first-principles calculations of infrared optical properties and the low accuracy of molecular dynamics calculations, and provides a powerful tool for accelerating the discovery and design of infrared optical materials.

[0018] The following describes the method for quickly calculating the infrared reflectance spectrum of a crystal using a specific crystal structure: Example 1: Step 1: Use VASP to perform AIMD calculations on the 3C-SiC crystal structure to generate a structure-atomic force-energy data set; specifically: Step 1.1: Use the first-principles calculation software VASP to perform structural optimization calculations on the material until convergence, obtain the CONTCAR file, and copy it to the POSCAR file; Step 1.2: Use the vaspkit tool to expand the POSCAR structure after step 1.1 convergence to about 100 atoms; Step 1.3: First-principles molecular dynamics calculations were performed on the expanded structure using the first-principles calculation software VASP developed by the University of Vienna. NPT simulations were performed using a Langevin thermostat at 300 K for 1000 steps to generate the OUTCAR file. Gamma sampling was used for pseudopotentials using the projected augmented plane wave method, with a cutoff energy of 450 eV. The convergence criteria for force and energy were -2×10 -2 eV / Å and 1×10 -5 eV. The MLIP-2 software developed by the Skolkovo Institute of Science and Technology was used to read the OUTCAR file and uniformly sample the structure-atomic force-energy data of 1000 steps to generate a subsample dataset for MTP training; Step 2: Use the dataset generated in step 1 to train the MTP machine learning interatomic potential; specifically: Step 2.1: Prepare an untrained machine learning potential file (included with the MLIP-2 software) and modify the element types, cutoff radius, and other parameters according to the computational system. Place the subsample dataset generated in Step 1 in the same folder as the previously mentioned machine learning potential. Set the energy weight to 1, the force weight to 0.1, and the stress weight to 0.001. Run the first training run in the MLIP-2 software's train module until convergence. Step 2.2: Use the trained machine learning potential as input and call the MLIP-2 software to execute the select-add module. Select some extreme data and add them to the training set. Repeat step 2.1 for the second training until convergence, and obtain the p.mtp file. Step 3: Use the MTP machine learning interatomic potential trained to convergence in step 2 to simulate the corresponding high-order force constants; specifically: Step 3.1: Prepare the POSCAR described in step 1.1 and execute the Phonopy software to expand the cell. The expansion parameters need to be dynamically determined according to the number of atoms in the material system to ensure that the number of atoms after expansion is between 100-1000 atoms. And call VASP to perform density functional perturbation calculations. When calculating, you need to turn on LEPSILON = .TRUE. to obtain the BORN charge after the calculation is completed. Prepare the band.conf file required for Phonopy software post-processing, write the necessary parameters such as the high symmetry point path, expansion parameters, and turn on the output force constant command, and call Phonopy to obtain the BORN charge and high-frequency dielectric constant ε. ∞ ; Step 3.2: Place the POSCAR file described in step 1.1, the p.mtp file described in step 2.2, and the same band.conf file as in step 3.1 into the same folder. Execute the mlip_phonopy interface program designed for Phonopy in the MLIP-2 software to obtain the second-order force constant file FORCE_CONSTANTS and rename it to FORCE_CONSTANTS_2ND. Step 3.3: Place the POSCAR file described in step 1.1, the p.mtp file described in step 2.2, and the fake_vasp_calcs.py interface program designed for thirdorder in the MLIP-2 software into the same folder. Pass the same expansion cell parameters and neighboring atom parameters as in step 3.1 and execute the interface program to obtain FORCE_CONSTANTS_3RD. Step 4: Input the high-order force constants generated in step 3 into ShengBTE, solve the Boltzmann equation, and obtain the phonon vibration parameters. Specifically: Step 4.1: Place the FORCE_CONSTANTS_2ND and FORCE_CONSTANTS_3RD files from step 3.2 and step 3.3 into the same folder. Prepare the CONTROL file and write the POSCAR file from step 1.1 into it. Set all other parameters to default. Run the ShengBTE software to perform the Boltzmann transport calculation and obtain the BTE.w_anharmonic file. Extract the degenerate phonon vibration modes near point G and their corresponding phonon damping coefficients from it. Step 4.2: Similar to step 4.1, except that the BORN file described in step 3.1 is appended to the CONTROL file. The ShengBTE software is called to perform the Boltzmann transport calculation, obtaining the BTE.w_anharmonic file. From this file, the phonon vibration modes near point G, generated after the BORN correction and splitting, and their corresponding phonon damping coefficients are extracted. Step 4.3: Compare and extract the transverse phonon vibration mode (ω TO ) and its corresponding phonon damping coefficient (γ TO ) and the corresponding longitudinal phonon vibration mode (ω LO ) and its corresponding phonon damping coefficient (γ LO ), write to lorentz.txt; Step 5: Substitute the obtained phonon vibration parameters into the Lorentz vibration model to obtain the dielectric function of the material, and calculate the frequency-dependent reflectivity according to the Fresnel formula. Specifically: Step 5.1: The frequency-dependent dielectric function can be obtained from the Lorentz vibration model: the calculation formula is as follows: Where ε ∞ is the high-frequency dielectric constant, extracted from step 3.1, ω j and γ j is the jth infrared active phonon vibration mode and the corresponding phonon damping coefficient, which comes from lorentz.txt described in step 4.3; After obtaining the dielectric function, the reflectance spectrum of the material can be obtained using the Fresnel equation. The calculation formula is as follows:

[0019] The processes of Example 2 and Example 3 are the same as those of Example 1, except that the crystal structures are different from those of Example 1 (3C-SiC), namely SiO2 and Mg2SiO4 respectively. Figures 3 to 5 ,With the increase of atomic complexity and the decrease of crystal symmetry, the ,advantages of the method (MTP) adopted in this embodiment become more ,obvious compared to the traditional method (DFT).

[0020] Based on the same concept, this embodiment also provides a device for quickly calculating a crystal infrared reflectance spectrum, comprising: a data set acquisition module, configured to perform AIMD calculations on crystal structures using VASP to generate structure-atomic force-energy data sets; An atomic potential training module, configured to train the MTP machine learning interatomic potential based on the generated dataset; A high-order force constant calculation module is configured to simulate the corresponding high-order force constants from the MTP machine learning interatomic potential trained to convergence; The phonon vibration parameter calculation module is configured to input the generated high-order force constants into ShengBTE, solve the Boltzmann equation, and obtain the phonon vibration parameters; The infrared reflection spectrum calculation module is configured to substitute the obtained phonon vibration parameters into the Lorentz vibration model to obtain the dielectric function of the crystal material, and calculate the frequency-dependent reflectivity according to the Fresnel formula to obtain the crystal infrared reflection spectrum.

[0021] The device is used to implement the steps of the above-mentioned method for quickly calculating the infrared reflection spectrum of the crystal. The implementation method is similar and will not be repeated here.

[0022] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments. Even if various changes are made to the present invention, if these changes fall within the scope of the claims of the present invention and their equivalents, they still fall within the scope of protection of the present invention.

Claims

1. A method for rapid calculation of crystal infrared reflection spectrum, characterized in that: include: AIMD calculations were performed on the crystal structure using VASP to generate a structure-atomic force-energy dataset; Based on the generated dataset, the MTP machine is trained to learn the interatomic potential; The MTP machine learning interatomic potential trained to convergence is used to simulate the corresponding high-order force constants; The generated high-order force constants are input into ShengBTE to solve the Boltzmann equation and obtain the phonon vibration parameters; The obtained phonon vibration parameters are substituted into the Lorentz vibration model to obtain the dielectric function of the crystal material, and the frequency-dependent reflectivity is calculated according to the Fresnel formula to obtain the crystal infrared reflection spectrum.

2. The method for rapidly calculating the infrared reflectance spectrum of a crystal as claimed in claim 1, wherein: AIMD calculations of crystal structures using VASP to generate structure-atomic force-energy datasets further include: The first-principles calculation software VASP was used to optimize the structure of the material. The calculation was completed until convergence to obtain the CONTCAR file, which was then converted into a POSCAR file. Use vaspkit tool to expand the POSCAR structure; The expanded structure was subjected to first-principles molecular dynamics calculations using VASP, and NPT simulations were performed using a Langevin thermostat to generate an OUTCAR file. The OUTCAR file was read using MLIP-2 software, and its structure-atomic force-energy data were uniformly sampled to generate a sample data set for MTP training.

3. The rapid calculation method of the crystal infrared reflection spectrum according to claim 1, wherein: Based on the generated dataset, training the MTP machine learning interatomic potential further includes: Initialize the MTP training parameters and call the train module of the MLIP-2 software to perform the first training until convergence; The machine learning potential trained for the first time is used as input, and the MLIP-2 software is called to execute the select-add module. Some extreme data are selected and added to the training set. The second training is performed until convergence, and the p.mtp file is obtained.

4. The method for rapidly calculating the infrared reflectance spectrum of a crystal according to claim 1, wherein: The MTP machine learning interatomic potential trained to convergence is further simulated to obtain the corresponding high-order force constants including: Phonopy software was used to expand POSCAR, and VASP was used to perform density functional perturbation calculations to obtain the BORN charge and high-frequency dielectric constant ε. ∞ , prepare the band.conf file; Put the POSCAR file, p.mtp file, and band.conf into the same folder, execute the interface program mlip_phonopy designed for Phonopy in the MLIP-2 software, obtain the second-order force constant file FORCE_CONSTANTS, and rename it to FORCE_CONSTANTS_2ND; Place the POSCAR file, p.mtp file, and the fake_vasp_calcs.py interface program designed for thirdorder in the same folder. Pass the expansion cell parameters and neighboring atom parameters into the interface program and execute it to obtain the high-order force constant file FORCE_CONSTANTS_3RD.

5. The method for rapidly calculating the infrared reflectance spectrum of a crystal as claimed in claim 4, wherein: The generated high-order force constants are input into ShengBTE to solve the Boltzmann equation, and the phonon vibration parameters are further obtained, including: Put the FORCE_CONSTANTS_2ND and FORCE_CONSTANTS_3RD files into the same folder, prepare the CONTROL file, and write the POSCAR file into it; Call ShengBTE software to perform Boltzmann transport calculations and obtain the BTE.w_anharmonic file, from which the degenerate phonon vibration modes near point G and their corresponding phonon damping coefficients are extracted; Append the BORN charge to the CONTROL file, call ShengBTE software again to perform the Boltzmann transport calculation, and obtain the BTE.w_anharmonic file. From it, extract the phonon vibration mode and its corresponding phonon damping coefficient after the BORN correction near the G point. Compare and extract the degenerate phonon vibration mode ω TO and its corresponding phonon damping coefficient γ TO , and the phonon vibration mode ω after splitting LO and its corresponding phonon damping coefficient γ LO , written to the file lorentz.txt.

6. The method for rapidly calculating the infrared reflectance spectrum of a crystal according to claim 5, wherein: Substituting the obtained phonon vibration parameters into the Lorentz vibration model to obtain the dielectric function of the crystal material, and calculating the frequency-dependent reflectivity according to the Fresnel formula further includes: Calculate the frequency-dependent dielectric function according to the Lorentz vibration model: Where ε ∞ is the high frequency dielectric constant, ω j and γ j is the jth infrared active phonon vibration mode and the corresponding phonon damping coefficient, which comes from the file lorentz.txt; After obtaining the dielectric function, the Fresnel equation is used to calculate the reflectance spectrum of the infrared optical material: 。 7. A device for rapidly calculating infrared reflectance spectra of crystals, characterized in that: include: a data set acquisition module, configured to perform AIMD calculations on crystal structures using VASP to generate structure-atomic force-energy data sets; An atomic potential training module, configured to train the MTP machine learning interatomic potential based on the generated dataset; A high-order force constant calculation module is configured to simulate the corresponding high-order force constants from the MTP machine learning interatomic potential trained to convergence; The phonon vibration parameter calculation module is configured to input the generated high-order force constants into ShengBTE, solve the Boltzmann equation, and obtain the phonon vibration parameters; The infrared reflection spectrum calculation module is configured to substitute the obtained phonon vibration parameters into the Lorentz vibration model to obtain the dielectric function of the crystal material, and calculate the frequency-dependent reflectivity according to the Fresnel formula to obtain the crystal infrared reflection spectrum.

8. The device for rapidly calculating the infrared reflection spectrum of a crystal according to claim 7, wherein: The high-order force constant calculation module is further configured to: Phonopy software was used to expand POSCAR, and VASP was used to perform density functional perturbation calculations to obtain the BORN charge and high-frequency dielectric constant ε. ∞ , prepare the band.conf file; Put the POSCAR file, p.mtp file, and band.conf into the same folder, execute the interface program mlip_phonopy designed for Phonopy in the MLIP-2 software, obtain the second-order force constant file FORCE_CONSTANTS, and rename it to FORCE_CONSTANTS_2ND; Place the POSCAR file, p.mtp file, and the fake_vasp_calcs.py interface program designed for thirdorder in the same folder. Pass the expansion cell parameters and neighboring atom parameters into the interface program and execute it to obtain the high-order force constant file FORCE_CONSTANTS_3RD.

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