Comprehensive performance high-throughput screening method of multi-component nitride coating for high-temperature bearing
By constructing and optimizing multi-component nitride coatings using a high-throughput screening method, the problem of screening difficulties in high-temperature environments was solved, achieving efficient and low-cost coating screening.
Patent Information
- Application Number
- CN202511450092.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-11
- Publication Date
- 2026-02-03
AI Technical Summary
Existing technologies make it difficult to effectively screen out multi-component nitride coatings with excellent overall performance in high-temperature environments, resulting in high experimental research costs.
High-throughput screening was performed using Materials Studio software. By constructing a monometallic metal nitride crystal model, ordered doping and structural optimization were carried out, elastic constants were calculated, and medium-entropy nitride coatings that meet specific application requirements were selected.
This method enables the rapid screening of ideal medium-entropy nitride coatings under high-temperature conditions, significantly reducing experimental costs and workload.
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Figure CN121459993A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a high-throughput screening method for the comprehensive performance of multi-component nitride coatings, belonging to the field of bearing technology. Background Technology
[0002] With the development of cutting-edge technologies such as aerospace, nuclear energy equipment, and high-speed gas turbines, mechanical systems under extreme operating conditions place stringent demands on the high-temperature resistance of bearings. Conventional bearing lubrication technologies (such as grease lubrication and polymer-based solid lubrication) face the risk of failure in high-temperature environments exceeding 500°C: organic lubricants undergo carbonization and decomposition, traditional solid lubricating materials (such as molybdenum disulfide and graphite) experience a sharp decline in lubrication performance due to oxidation or phase transformation, while metal-based coatings are prone to high-temperature softening or oxidative embrittlement. Metal nitride ceramics have shown potential above 500°C and are expected to become candidate materials for lubrication coatings for high-temperature bearings. However, the variety of transition metal elements is vast, the composition space for high-entropy ceramics is enormous, experimental research costs are high, and it is difficult to obtain medium-entropy ceramic compositions with excellent comprehensive performance. Summary of the Invention
[0003] To address the challenges of obtaining high-entropy ceramic components with excellent overall performance due to the vast variety of transition metal elements and the high cost of experimental research, this invention proposes a high-throughput screening method for the comprehensive performance of multi-component nitride coatings for high-temperature bearings.
[0004] The technical solution adopted by the present invention to solve the above problems is as follows: The steps of the present invention include: Step 1: Select three to four metal elements and identify their corresponding metal nitrides as the key components of medium-entropy nitrides; Step 2: Use the 3D Atomistic component of Materials Studio to construct a monometallic metal nitride crystal model; Step 3: Using the Disorder function in Materials Studio software, perform ordered doping on the monometallic nitride constructed in Step 2 to generate a multi-metallic nitride crystal model. Step 4: Perform structural optimization on the multi-component metal nitride crystal model generated in Step 3 to obtain the optimized multi-component metal nitride model; Step 5: Calculate the elastic constants of the multi-metal nitride model optimized in Step 4 using the CASTEP module of Materials Studio software; Step 6: Use Materials Studio software and Perl language code to output the bulk modulus of the entropy nitride. B shear modulus G Young's modulus ECompared to Poisson ; Step 7: Comprehensively evaluate material properties through multiple screening mechanisms to screen out medium-entropy nitrides that meet specific application requirements; medium-entropy nitrides are screened based on mechanical stability, elastic failure strain and resistance to plastic deformation, Vickers hardness and Poisson's ratio.
[0005] Furthermore, the metallic element in step 1 is at least three of the following: Ti, Zr, Hf, V, Nb, Ta, Mo, Cr, and W.
[0006] Furthermore, the structural optimization parameters in step 4 include: selecting the BFGS method as the optimization algorithm, using OTFGUltrasoft as the pseudopotential, and selecting the generalized gradient approximation as the functional. k The point sampling rate was set to 0.015, and the cutoff energy was set to 650 eV. The optimized convergence criteria include: setting the energy convergence accuracy to 5 × 10⁻⁶. -6 eV, the force field convergence criterion is set to 0.01 eV / Å, the stress convergence accuracy is set to 0.02 GPa, and the atomic displacement accuracy is set to 5 × 10 eV. -4 Å, the self-consistent field cycle accuracy is set to 5 × 10 -7 eV; During the optimization process, the lattice shape and ion coordinates are allowed to change.
[0007] Furthermore, the calculation of the elastic constants in step 5 includes: Step 501: Apply six small deformations to the crystal structure, with the maximum strain amplitude controlled at 0.003. Step 502: Re-optimize the structure under each strain state; The optimized parameter settings are as follows: the pseudopotential adopts OTFG Ultrasoft, the functional is selected as the PBE form in the generalized gradient approximation GGA, and the cutoff energy is set to 650 eV; The optimized convergence criteria include: setting the energy convergence accuracy to 2 × 10⁻⁶. -6 eV, the force field convergence criterion is set to 0.006 eV / Å, and the atomic displacement accuracy is set to 2 × 10⁻⁶ eV / Å. -4 Å.
[0008] The beneficial effects of this invention are as follows: Based on a defined crystal system and element type, this invention uses the Disorder function of Materials Studio software to orderly dope the mono-metal nitride crystal structure model, obtaining a medium-entropy nitride crystal structure model. Then, the medium-entropy nitride model is structurally optimized to obtain the crystal structure model with the lowest energy. Small strains are applied to the optimized medium-entropy nitride model, and Hooke's law is used to fit the stress-strain relationship to obtain elastic constants. Based on these elastic constants, the elastic failure strain, resistance to plastic deformation, Vickers hardness, and Poisson's ratio of the medium-entropy nitride are calculated. Finally, a high-throughput screening process is used to obtain ideal target materials for the medium-entropy nitride. This invention uses a first-principles high-throughput method to calculate the mechanical properties of medium-entropy nitrides, enabling the simultaneous calculation of the mechanical properties of multiple metal nitride medium-entropy ceramics. This allows for rapid screening of medium-entropy carbide ceramics with ideal mechanical properties, reducing the heavy workload of synthesis and mechanical property testing experiments, and significantly lowering experimental costs. Attached Figure Description
[0009] Figure 1 This is a flowchart of the entropy nitride screening process. Detailed Implementation
[0010] Specific implementation method one: as follows Figure 1 As shown, a high-throughput screening method for the comprehensive performance of multi-component nitride coatings for high-temperature bearings includes the following steps: Step 1: Select metal elements from Ti, Zr, Hf, V, Nb, Ta, Mo, Cr, and W, and determine their corresponding metal nitrides as the key components of entropy ceramics; Step 2: Using the 3D Atomistic component in Materials Studio software, construct a monometallic metal nitride crystal model for the metal element selected in Step 1, based on its crystal system and crystal structure parameters. Step 3: Using the Disorder function in Materials Studio software, perform ordered doping on the monometallic nitride constructed in Step 2 to generate a medium-entropy nitride model with complex composition. Step 4: Perform structural optimization on the medium-entropy nitride model generated in Step 3; During the optimization process, the following parameter settings were adopted: the BFGS (Broyden-Fletcher-Goldfarb-Shanno) method was selected as the optimization algorithm, the OTFG Ultrasoft method was used for the pseudopotential, the PBE functional in the Generalized Gradient Approximation (GGA) was selected as the functional, and the k-point sampling was set to 0.015 Å. -1 The cutoff energy is set to 650 eV; The parameters for structural optimization include: energy convergence accuracy set to 5 × 10⁻⁶. -6 eV, the force field convergence criterion is set to 0.01 eV / Å, the stress convergence accuracy is set to 0.02 GPa, and the atomic displacement accuracy is set to 5 × 10 eV. -4 Å, the self-consistent field cycle accuracy is set to 5 × 10 -7 eV; During the optimization process, changes in lattice shape and ion coordinates are allowed to ensure the energy and structural stability of the model, ultimately resulting in an optimized multi-component metal nitride model. Step 5: Calculate the elastic constants of the optimized medium-entropy nitride model from Step 4; The calculation process is as follows: First, six micro-deformations were applied to the crystal structure, with the maximum strain amplitude strictly controlled at 0.003. Subsequently, the structure under each strain state was re-optimized with the following optimization parameters: the pseudopotential was OTFGUltrasoft, the functional was selected as the generalized gradient approximation (GGA-PBE), and the cutoff energy was set to 650 eV. The optimized convergence criteria include: setting the energy convergence accuracy to 2 × 10⁻⁶. -6 eV, the force field convergence criterion is set to 0.006 eV / Å, and the atomic displacement accuracy is set to 2 × 10⁻⁶ eV / Å. -4 Å; The stress value corresponding to each strain state is calculated through the above optimization process; Finally, based on Hooke's law, the stress-strain relationship was fitted to obtain the elastic constants; Step 6: Use Materials Studio software and Perl language code to output the bulk modulus of the entropy nitride. B shear modulus G Young's modulus E Compared to Poisson v ; Step 7: Based on the calculation of the elastic constant in Step 5, determine the mechanical stability of the intermediate entropy nitride according to formula (1): (1), In formula (1), Step 8: Shear modulus calculated in step 6 and bulk modulus Based on this, the Vickers hardness of intermediate entropy nitrides is calculated. : (2), Step 9: Calculate Young's modulus in step 6. E And step 8 to calculate Vickers hardness HV Based on this, calculate the elastic failure strain. H V / E and the ability to resist plastic deformation H V 3 / E 2 ; Step 10, using Figure 1 The screening process shown is based on the mechanical stability and elastic failure strain H obtained in steps 6 to 9. V / E and the ability to resist plastic deformation H V 3 / E 2 Vickers hardness H V Compared with Poisson v High-throughput screening of medium-entropy nitrides.
[0011] Example Example 1: Step (1): Select seven metal elements, namely Hf, Nb, Ta, Ti, V, Mo and W, and determine their corresponding metal nitrides as the key components of entropy ceramics.
[0012] Step (2): Using the 3D Atomistic component in Materials Studio software, construct a monometallic metal nitride crystal model with a crystal system of Fm3m for the metal element selected in step (1); Step (3): Using the Disorder function in Materials Studio software, the monometallic nitride constructed in step (2) is subjected to ordered doping to generate 35 medium-entropy nitride models. Step (4): Perform structural optimization on the medium-entropy nitride model generated in step (3). During the optimization process, the following parameters are set: the optimization algorithm is the BFGS (Broyden-Fletcher-Goldfarb-Shanno) method, the pseudopotential is OTFG Ultrasoft, the functional is the PBE functional in the generalized gradient approximation (GGA), and the k-point sampling is set to 0.015 Å. -1 The cutoff energy was set to 650 eV. Parameters for structural optimization included: energy convergence accuracy set to 5 × 10⁻⁶ eV. -6 eV, the force field convergence criterion is set to 0.01 eV / Å, the stress convergence accuracy is set to 0.02 GPa, and the atomic displacement accuracy is set to 5 × 10 eV / Å. -4 Å, the self-consistent field cycle accuracy is set to 5 × 10 -7eV. During the optimization process, changes in lattice shape and ion coordinates are allowed to ensure the model's energy and structural stability. The final optimized multi-component metal nitride model is obtained.
[0013] Step (5): Calculate the elastic constants of the optimized medium-entropy nitride model from step (4). The calculation process is as follows: First, apply six small deformations to the crystal structure, with the maximum strain amplitude strictly controlled at 0.003. Then, re-optimize the structure under each strain state, with the following optimization parameters: the pseudopotential uses OTFG Ultrasoft, the functional is selected as the generalized gradient approximation (GGA-PBE), and the cutoff energy is set to 650 eV. The optimization convergence criteria include: energy convergence accuracy set to 2 × 10⁻⁶. -6 eV, the force field convergence criterion is set to 0.006 eV / Å, and the atomic displacement accuracy is set to 2×10 eV / Å. -4 Å. Through the above optimization process, the stress value corresponding to each strain state is calculated. Finally, based on Hooke's law, the stress-strain relationship is fitted to obtain the elastic constants.
[0014] Step (6): According to steps (6) to (11) of the specific implementation method, calculate the mechanical properties of the medium entropy nitrides and screen them to obtain four medium entropy nitrides with excellent performance, as shown in Table 1.
[0015] Table 1 Screening results of Example 1
[0016] Example 2: Step (1): Select 8 metal elements, namely Hf, Nb, Ta, Ti, Zr, Cr, Mo and W, and determine their corresponding metal nitrides as the key components of entropy ceramics.
[0017] Step (2): Using the 3D Atomistic component in Materials Studio software, construct a monometallic metal nitride crystal model with a crystal system of Fm3m for the metal element selected in step (1); Step (3): Using the Disorder function in Materials Studio software, the monometallic nitride constructed in step (2) is subjected to ordered doping to generate 70 medium-entropy nitride models. Step (4): According to the specific implementation steps (4) to (10), the structure of the medium entropy nitride is optimized, the elastic constant is calculated and the mechanical properties are calculated; according to the specific implementation step (11), the medium entropy nitride is screened to obtain 7 medium entropy nitrides with excellent performance, as shown in Table 2.
[0018] Table 2 Screening Results of Example 2
[0019] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.
Claims
1. A high-throughput screening method for the comprehensive performance of multi-component nitride coatings for high-temperature bearings, characterized in that, The steps of the high-throughput screening method for the comprehensive performance of multi-component nitride coatings for high-temperature bearings include: Step 1: Select three to four metal elements and identify their corresponding metal nitrides as the key components of medium-entropy nitrides; Step 2: Use the 3D Atomistic component of Materials Studio to construct a monometallic metal nitride crystal model; Step 3: Using the Disorder function in Materials Studio software, perform ordered doping on the monometallic nitride constructed in Step 2 to generate a multi-metallic nitride crystal model. Step 4: Perform structural optimization on the multi-component metal nitride crystal model generated in Step 3 to obtain the optimized multi-component metal nitride model; Step 5: Calculate the elastic constants of the multi-metal nitride model optimized in Step 4 using the CASTEP module of Materials Studio software; Step 6: Use Materials Studio software and Perl language code to output the bulk modulus of the entropy nitride. B shear modulus G Young's modulus E Compared to Poisson ; Step 7: Comprehensively evaluate material properties through multiple screening mechanisms to screen out medium-entropy nitrides that meet specific application requirements; medium-entropy nitrides are screened based on mechanical stability, elastic failure strain and resistance to plastic deformation, Vickers hardness and Poisson's ratio.
2. The high-throughput screening method for the comprehensive performance of multi-component nitride coatings for high-temperature bearings according to claim 1, characterized in that, The metallic element in step 1 is at least three of the following: Ti, Zr, Hf, V, Nb, Ta, Mo, Cr, and W.
3. The high-throughput screening method for the comprehensive performance of multi-component nitride coatings for high-temperature bearings according to claim 1, characterized in that, The structural optimization parameters in step 4 include: the BFGS method is selected as the optimization algorithm, OTFGUltrasoft is used for the pseudopotential, and the generalized gradient approximation is chosen as the functional. k The point sampling rate was set to 0.015, and the cutoff energy was set to 650 eV. The optimized convergence criteria include: setting the energy convergence accuracy to 5 × 10⁻⁶. -6 eV, the force field convergence criterion is set to 0.01 eV / Å, the stress convergence accuracy is set to 0.02 GPa, and the atomic displacement accuracy is set to 5 × 10 eV / Å. -4 Å, the self-consistent field cycle accuracy is set to 5 × 10 -7 eV; During the optimization process, the lattice shape and ion coordinates are allowed to change.
4. The high-throughput screening method for the comprehensive performance of multi-component nitride coatings for high-temperature bearings according to claim 1, characterized in that, The calculation of elastic constants in step 5 includes: Step 501: Apply six small deformations to the crystal structure, with the maximum strain amplitude controlled at 0.
003. Step 502: Re-optimize the structure under each strain state; The optimized parameter settings are as follows: the pseudopotential adopts OTFG Ultrasoft, the functional is selected as the PBE form in the generalized gradient approximation GGA, and the cutoff energy is set to 650 eV; The optimized convergence criteria include: setting the energy convergence accuracy to 2 × 10⁻⁶. -6 eV, the force field convergence criterion is set to 0.006 eV / Å, and the atomic displacement accuracy is set to 2 × 10⁻⁶ eV / Å. -4 Å.