CN113311020A - Method for calculating and predicting thermoelectric performance of material based on high-throughput first principle - Google Patents
Method for calculating and predicting thermoelectric performance of material based on high-throughput first principle Download PDFInfo
- Publication number
- CN113311020A CN113311020A CN202110597707.5A CN202110597707A CN113311020A CN 113311020 A CN113311020 A CN 113311020A CN 202110597707 A CN202110597707 A CN 202110597707A CN 113311020 A CN113311020 A CN 113311020A
- Authority
- CN
- China
- Prior art keywords
- calculation
- optimization
- energy
- vasp
- calculating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N25/00—Investigating or analyzing materials by the use of thermal means
- G01N25/20—Investigating or analyzing materials by the use of thermal means by investigating the development of heat, i.e. calorimetry, e.g. by measuring specific heat, by measuring thermal conductivity
Landscapes
- Chemical & Material Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明提供一种基于高通量第一性原理计算预测材料热电性质的方法，包括以下步骤：步骤1，对材料的初始晶体结构进行一次磁性测试计算，以便于获得材料的磁矩信息；步骤2，用共轭梯度算法或者拟牛顿算法对化合物进行结构优化计算，获得优化后的晶胞结构，接下来进行材料体积模量的计算、拟合，获得对应材料的体积模量信息，进而推导出材料的声速信息；步骤3，结构优化计算收敛之后进行材料的自洽计算，从而获得材料的电荷密度、总能量和磁矩。步骤4，基于步骤3算得的电荷密度，进行热电性质的计算。
The present invention provides a method for predicting the thermoelectric properties of materials based on high-flux first-principles calculations, comprising the following steps: Step 1, perform a magnetic test calculation on the initial crystal structure of the material, so as to obtain the magnetic moment information of the material; step 2. Use the conjugate gradient algorithm or the quasi-Newton algorithm to optimize the structure of the compound to obtain the optimized unit cell structure, and then calculate and fit the bulk modulus of the material to obtain the bulk modulus information of the corresponding material, and then deduce The sound velocity information of the material is obtained; in step 3, the self-consistent calculation of the material is performed after the structural optimization calculation converges, so as to obtain the charge density, total energy and magnetic moment of the material. Step 4, based on the charge density calculated in Step 3, calculate the thermoelectric properties.
Description
Technical Field
The invention relates to the field of electrical properties of thermoelectric materials, in particular to a method for calculating and predicting thermoelectric properties of a material based on a high-throughput first-nature principle.
Background
Materials play an important role in our lives, and today innovation in material technology should also become key to urgent social challenges such as global climate change and increasingly serious pollution. Therefore, the development of environment-friendly materials such as thermoelectric materials is accelerated. The thermoelectric material is a functional material capable of converting heat energy and electric energy into each other, can generate electricity by using temperature difference and exchange heat energy by using electric energy to realize refrigeration, thereby being made into various thermoelectric devices and being a novel functional environment-friendly material.
We commonly use the dimensionless formula ZT ═ S2The thermoelectric property of the material is expressed by sigma T/kappa, wherein S is the Zeebeck coefficient of the material, sigma represents the electric conductivity of the material, kappa is the thermal conductivity of the material, and S2The product of σ is referred to as the power factor of the material. Since higher ZT values indicate better thermoelectric properties of the material, higher power factors and lower thermal conductivities are required to obtain materials with better thermoelectric properties. Therefore, in the field of thermoelectric materials, the research on the power factor is of great significance. Further according to the formula c ═ (k/ρ)1/2(c represents the sound velocity of the material, ρ is the density of the material, and k is the bulk modulus), the bulk modulus of the material can be used to deduce the sound velocity of the material. A smaller bulk modulus results in a material with lower acoustic speed, while a material with lower acoustic speed always has lower thermal conductivity, so that the acoustic speed can also be used as a high-throughput screening condition of thermoelectric materials, thereby finding a material with a higher ZT value.
In general we can optimize the ZT value of a material from two aspects: from the aspect of electric transportation performance, the power factor S of the material can be improved2σ, the thermal conductivity κ of the material is to be reduced from the thermal transport aspect. In order to increase the power factor of the material, the zeebeck coefficient S and the electrical conductivity σ need to be increased, and the thermal conductivity κ of the material needs to be reduced. However, these parameters are not isolated, and on the one hand, as the carrier concentration increases, the zeebeck coefficient of the material decreases and the conductivity increases. On the other hand, the thermal conductivity of the material is influenced by the carrier concentration and the lattice phonons, the carrier concentration is increased, the thermal conductivity is also increased, and the effects of the electrical conductivity and the thermal conductivity on the ZT value are opposite. In view of the difficulties in obtaining new thermoelectric materials by means of tuning, we have created this method with the aim of finding new materials with good electrical properties by means of a rapid prediction of the power factor and the speed of sound of the materials, and by means of a direct high-throughput screening.
The existing calculation method for acquiring the thermoelectric property of the material mostly utilizes a BoltzTrap process based on the approximation of constant relaxation timeProcedures, e.g. work on thermoelectric property calculations based on Materials Project of the university of Massachusetts1. However, the constant relaxation time approximation only calculates the electronic structure part and chooses to ignore complex scattering term calculation, so that the power factor obtained by the calculation method is greatly deviated from the actual result. The method is compatible with a TransOpt electric transport calculation program2The TransOpt program processes the relaxation time based on constant electroacoustic coupling approximation, treats the electroacoustic coupling matrix element on the full-space energy band as a constant, does not increase extra calculation amount compared with the constant relaxation time approximation, but considers the scattering correlation of the electroacoustic coupling, ensures the calculation speed and improves the prediction precision in algorithm.
Disclosure of Invention
The invention aims to overcome the blindness of a trial-and-error method in an experiment and provides a method for calculating and obtaining the material electric transport property based on a high-throughput first principle. By giving an initial unit cell structure, an automatic flow of calculating the electric transport property of the material can be realized, and the operation is simple and quick.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a method for obtaining material electric transport properties based on high-throughput first principle calculation comprises the following steps:
step 1, performing magnetic test calculation on an initial crystal structure of a material so as to obtain magnetic moment information of the material;
step 2, performing structure optimization calculation on the compound by using a conjugate gradient algorithm or a quasi-Newton algorithm to obtain an optimized unit cell structure, and then performing calculation and fitting on the bulk modulus of the material to obtain bulk modulus information of the corresponding material, so as to deduce sound velocity information of the material;
and 3, performing self-consistent calculation on the material after the structure optimization calculation is converged, thereby obtaining the charge density, the total energy and the magnetic moment of the material.
And 4, calculating the thermoelectric property based on the charge density calculated in the step 3.
Wherein, the step 1 comprises:
step 1.1, magnetic test calculation is carried out, and the high symmetry K point of the step is set to be 30/a +1, 30/b +1, 30/c +1, and a, b and c are lattice parameters of the compound.
Step 1.2, if the absolute value of the magnetic moment of any atom calculated in this step is greater than 0.02 μ B, the parameter "ISPIN ═ 2" is added to the INCAR file in the following calculation step, otherwise, no modification is made to the INCAR file.
The step 2 comprises the following steps:
and 2.1, performing optimized calculation on the atom position, the shape and the volume of the compound. Here, the K point is set to 30/a +1, 30/b +1, 30/c +1 as above, and the convergence criterion of the force is set to be lower per atom than
Energy convergence criterion is 10-4eV。
And 2.2, optimizing the atom position and the unit cell volume for five times at most, setting parameters to be 3 for ISIF and 2 for IBRION, and performing a conjugate gradient algorithm. The result of any one of the five optimizations reaches the convergence criterion, and the next calculation is performed.
And 2.3, if the convergence standard is not met after the five times of optimization of the conjugate gradient algorithm, performing five times of optimization calculation of only the atom position, setting parameters to be ISIF (inverse discrete interface) 0 and IBRION (inverse discrete interface) 1, and simulating a Newton algorithm. And (4) when the result of any step reaches the convergence standard, carrying out the next calculation.
Step 2.4, if the tenth optimization still does not reach the convergence criterion, we consider the compound to be unable to converge, and mark it as "structural optimization is not converged", and stop calculating it. If the structure optimization reaches the convergence standard, the structure after optimization is subjected to 9 times of simulated stretching and compression of material volume, 9 pairs of energy-volume data are obtained through calculation, and the Vinet is utilized3Fitting a state equation to obtain the bulk modulus of the corresponding material, and finally obtaining the bulk modulus of the corresponding material based on the formula c ═ k/rho1/2The sound velocity of the corresponding material can be obtained.
The step 3 comprises the following steps:
after the relaxation calculation converges, the self-consistent calculation of step 3 is started, which is mainly to obtain the charge density, total energy and magnetic moment of the material. The K point grids used for self-consistent calculations are 60/a +1, 60/b +1, 60/c + 1. At the same time, the electron density of states of the material is obtained here on the basis of self-consistent calculations.
The step 4 comprises the following steps:
and (3) calculating the electric transport property based on the charge density calculated in the step (3) and based on a TransOpt program, wherein denser Brillouin zone high symmetry K points are set as 240/a +1, 240/b +1 and 240/c + 1. In the process of calculating the electric transport, the deformation potential and the Young modulus are respectively 3eV and 100GPa, and the calculation temperature is 700K.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
1. the method overcomes the defects of the traditional trial-and-error method, saves resources and time, is calculated based on a high-throughput first principle, and can automatically realize the calculation of the material electric transport property by giving an initial crystal structure.
2. The method does not relate to experiments and use chemical products in the whole process, does not generate chemical pollution, and accords with the concept of green environmental protection; and the method is simple to operate, low in cost, easy to realize and suitable for popularization and application.
3. According to the method, the thermoelectric performance of the material can be predicted in advance through the calculation result, samples meeting the requirements are selected for experimental verification, the experimental time and resources can be saved, the experimental efficiency is improved, the guiding effect is achieved, and blindness is avoided.
4. The electron state densities of four different smoothness degrees are obtained, and the orbit contributions of different elements can be more intuitively displayed.
5. The method is compatible with a TransOpt electric transport calculation program, ensures the calculation speed and increases the accuracy of the calculation result for the first-principle high-flux electric transport calculation.
6. Different structure optimization strategies are adopted, and the calculation time is reduced.
Drawings
FIG. 1 is a flow chart of the calculation of the method.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
The first embodiment is as follows:
the method for obtaining the material electric transport property based on the high-flux first principle comprises the following steps:
step 1, performing simple magnetic test calculation on Fe1Nb1Sb1 with the initial cubic system and the space group number of 216.
Step 1.1, the lattice constants of Fe1Nb1Sb1 are respectively
The high symmetry K point of this step is set to 8 x 8.
Step 1.2, magnetic test calculation shows that the magnetic moment of Fe1Nb1Sb1 is 0.0015 μ B, which is a nonmagnetic compound, and no parameter "ISPIN ═ 2" is added to the next calculated INCAR input file.
Step 2, performing structure optimization calculation on the compound by using a conjugate gradient algorithm or a quasi-Newton algorithm to obtain an optimized unit cell structure;
step 2.1, the highly symmetrical K point of this step is still set to 8 x 8, and the force convergence criterion is
Energy convergence criterion is 10-4eV。
And 2.2, firstly setting optimization parameters 'ISIF ═ 3 and IBRION ═ 2', performing optimization calculation on the structure of Fe1Nb1Sb1 by using a conjugate gradient algorithm, and finally reaching a convergence standard to finish the optimization calculation. On the basis of the optimized structure, the calculation of the bulk modulus is carried out to obtain 9 pairs of energy-volume parameters, the bulk modulus of Fe1Nb1Sb1 is found to be 165.625GPa after the fitting of a Vinet state equation, and the sound velocity prediction result is 1792 m/s.
And 3, starting self-consistent calculation after the structure optimization calculation is converged, thereby obtaining the charge density and the total energy of the Fe1Nb1Sb 1. The self-consistent calculated high symmetry K point is set to 15 × 15.
And 4, calculating the electric transport right after the calculation is finished, wherein the K point of the Fe1Nb1Sb1 is set to be 58 x 58 in the electric transport calculation process. Finally, the n-type maximum power factor of Fe1Nb1Sb1 is calculated to be 51 x 10-4W/mK2P-type maximum power factor of 33 x 10-4W/mK2
Example two:
this step is substantially the same as the above embodiment, with the particularity that:
in this embodiment, a method for obtaining material electrical transport properties based on high throughput first principle calculation comprises the following steps:
step 1, carrying out simple magnetic test calculation on Pb4Te4 with the space group number of 62 in the initial cubic system.
Step 1.1, the lattice constants of Pb4Te4 are respectively
The high symmetry K point of this step is set to 4 x 7 x 5.
Step 1.2, through calculation of magnetic tests, the magnetic moment of Pb4Te4 is found to be 0 μ B, and the magnetic moment belongs to a nonmagnetic compound.
Step 2.1, the highly symmetrical K point of this step is still set to 4 x 7 x 5, the force convergence criterion is
Energy convergence criterion is 10-4eV。
Step 2.2, firstly, setting optimization parameters "ISIF is 3, IBRION is 2", performing structure optimization calculation on Pb4Te4 by using a conjugate gradient algorithm, and not reaching the convergence standard after five times. On the basis of the optimized structure, the volume modulus is calculated, the volume modulus of Pb4Te4 is found to be 38.4GPa after the calculation result is fitted by utilizing a Vinet state equation, and the sound velocity prediction result is 425m/s after calculation.
And 2.3, setting the optimization parameters to be ' ISIF ═ 0 ' and IBRION ═ 1 ', and carrying out re-optimization by using a quasi-Newton algorithm to reach the convergence standard.
And 3, setting the self-consistent calculated high symmetry K point to be 7 × 14 × 10.
And 4, calculating the electric transportation from the right time after the calculation is finished, wherein the K point of Pb4Te4 is set to be 26 x 53 x 37 in the electric transportation calculation process. Finally, the n-type maximum power factor of Pb4Te4 is calculated to be 143 x 10-4W/mK2P-type maximum power factor of 264 x 10-4W/mK2
The method of the embodiment can overcome the defects of a traditional trial-and-error method, saves resources and time, is calculated based on the first principle, can obtain a calculation result by inputting the initial crystal structure, and is convenient and quick. According to the method, the samples meeting the requirements are selected for experimental verification through theoretical calculation, so that the experimental time and resources can be saved, the experimental efficiency is improved, the guiding effect is achieved, and the blindness is avoided.
In Table 1700K, the power factor and sound velocity prediction results for some n-type materials
| name | PFmax(10-4W/mK2) | Sound Velocity(m/s) | Space group |
| MIP3D-22204-In4O6 | 537.461 | 1867.23 | 167 |
| MIP3D-26335-Na2S4Sb2 | 484.24 | 864.223 | 12 |
| MIP3D-18728-Ga1O6Sb1Sr2 | 480.713 | 1958.26 | 225 |
| MIP3D-7091-Bi2Na2Se4 | 479.012 | 608.282 | 141 |
| MIP3D-10126-Cd2O6Sn2 | 477.884 | 1784.89 | 148 |
| MIP3D-22206-In4O8Zn2 | 465.678 | 1824.67 | 227 |
| MIP3D-10020-Cd2Ga4O8 | 450.081 | 1988.79 | 227 |
| MIP3D-10059-Cd2In4O8 | 448.407 | 1619.45 | 227 |
| MIP3D-3015-As2Hg1O6 | 446.493 | 1974.93 | 162 |
| MIP3D-28773-Pb4S4 | 429.262 | 345.611 | 64 |
| MIP3D-28725-Pb2S2 | 424.768 | 420.434 | 39 |
| MIP3D-20220-H2In2O4 | 424.362 | 1937.24 | 31 |
| MIP3D-28244-O8Sn2Zn4 | 421.637 | 1993.93 | 227 |
| MIP3D-7593-Br2O2Sb2 | 421.545 | 1010.46 | 129 |
| MIP3D-3056-As2Li2Se4 | 420.154 | 753.418 | 9 |
| MIP3D-7090-Bi2Na2S4 | 412.919 | 781.572 | 141 |
| MIP3D-27989-O6Sn2Sr2 | 404.667 | 1810.49 | 74 |
| MIP3D-26078-N4Zn6 | 397.329 | 1680.36 | 194 |
| MIP3D-22189-In4Mg2O8 | 390.845 | 1881.84 | 227 |
| MIP3D-3018-As2Hg2O6 | 366.547 | 1104.15 | 162 |
| MIP3D-18913-Ga2O4Rb2 | 364.891 | 1234.83 | 227 |
| MIP3D-21971-In2Li2O4 | 364.236 | 1816.56 | 141 |
| MIP3D-6191-Ba3Br2In2O5 | 363.498 | 990.659 | 139 |
| MIP3D-21972-In2Li2O4 | 362.336 | 1813.72 | 141 |
| MIP3D-10267-Cd4O8Sn2 | 356.92 | 1528.88 | 227 |
| MIP3D-6199-Ba3Cl2In2O5 | 356.362 | 1081.12 | 139 |
Table 2700K shows power factor and sound velocity prediction results for partial p-type materials
| name | PFmax(10-4W/mK2) | Sound Velocity(m/s) | Space group |
| MIP3D-28772-Pb4S4 | 514.193 | 352.979 | 36 |
| MIP3D-20513-H6Mg2Os1 | 413.021 | 1651.99 | 225 |
| MIP3D-5242-Ba1Ca3O4 | 380.052 | 1421.39 | 221 |
| MIP3D-5829-Ba2F8Sn2 | 350.13 | 822.474 | 129 |
| MIP3D-28775-Pb4Te4 | 286.776 | 425.522 | 62 |
| MIP3D-19176-Ga9K3 | 274.863 | 527.064 | 119 |
| MIP3D-28773-Pb4S4 | 248.484 | 345.611 | 64 |
| MIP3D-19180-Ga9Rb3 | 246.432 | 460.605 | 119 |
| MIP3D-1393-Al1H6In1Rb2 | 246.419 | 523.211 | 225 |
| MIP3D-167-Ag1Ba2Sb1 | 231.943 | 359.453 | 225 |
| MIP3D-5249-Ba1Ce1O3 | 230.342 | 1512.94 | 221 |
| MIP3D-16879-F4O4W2 | 227.381 | 1872.69 | 26 |
| MIP3D-24322-Li5S4Sb1 | 225.377 | 914.996 | 71 |
| MIP3D-17309-F6Sn2 | 220.445 | 1135.23 | 225 |
| MIP3D-24362-Li8Mg4Si4 | 213.229 | 1110.34 | 215 |
| MIP3D-28725-Pb2S2 | 211.446 | 420.434 | 39 |
| MIP3D-9262-Ca2H4 | 206.204 | 1119.59 | 194 |
| MIP3D-11414-Cl8Si4 | 205.121 | 941.185 | 19 |
| MIP3D-2947-As2Er2O2Zn2 | 202.25 | 1183.09 | 129 |
| MIP3D-2966-As2F2Sr2Zn2 | 201.326 | 857.303 | 129 |
| MIP3D-10162-Cd2S2 | 201.03 | 947.595 | 59 |
| MIP3D-6565-Be2Na2Sb2 | 197.021 | 680.235 | 194 |
| MIP3D-3122-As2O2Y2Zn2 | 193.922 | 1318.95 | 129 |
| MIP3D-23119-La2Na2O4 | 193.49 | 1389.42 | 141 |
| MIP3D-30153-Se2Zr1 | 192.521 | 1031.3 | 164 |
| MIP3D-20207-H2He2Li2 | 191.206 | 614.102 | 36 |
To sum up, the method for obtaining thermoelectric properties of a material through calculation based on a high-throughput first principle in the embodiments uses high-throughput calculation, firstly performs magnetic testing on the material, then performs structure optimization calculation, if the value after the structure optimization reaches a convergence standard, we perform stretching and compression on the optimized structure to obtain different energy-volume data, uses a Vinet equation of state to perform fitting to obtain the volume modulus of the corresponding material so as to obtain the sound velocity of the material, can indirectly screen out the material with lower thermal conductivity, then performs self-consistency and electronic density of state calculation on the compound, and finally further calculates the thermoelectric properties of the compound by using a TransOpt program to obtain more accurate power factors of the material. By predicting the power factor and the sound velocity of the material, the potential novel thermoelectric material is helped to be found.
The embodiments of the method of the present invention are described above with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes may be made according to the purpose of the invention, and any parameter changes or calculation simplification made according to the principle of the technical solution of the present invention may be made within the scope of the present invention as long as the purpose of the present invention is met, without departing from the principle and concept of the method of the present invention for calculating and predicting thermoelectric performance of a material based on the high-throughput first principle.
Claims (5)
1. A method for computationally predicting thermoelectric properties of a material based on high-throughput first principles, the method comprising the steps of:
step 1, utilizing VASP calculation software, firstly utilizing the default calculation parameters of the software to carry out magnetic test calculation on the initial compound crystal structure, and finally obtaining the magnetic moment information of the material;
step 2, performing structure optimization calculation on the compound by using a conjugate gradient algorithm or a quasi-Newton algorithm to obtain an optimized unit cell structure, and then performing calculation and fitting on the bulk modulus of the material to obtain bulk modulus information of the corresponding material, so as to deduce sound velocity information of the material;
step 3, starting self-consistent calculation after the structure optimization calculation reaches convergence, thereby obtaining the charge density, total energy and magnetic moment of the material;
and 4, calculating the electric transport property based on the charge density calculated in the step 3.
2. The method for computationally obtaining material electrical transport properties based on high throughput first principles as claimed in claim 1, wherein said step 1 comprises:
step 1.1, utilizing VASP software to perform magnetic test calculation, acquiring the side lengths a, b and c of unit cells of a calculated material during calculation, then setting high-symmetry K points of the calculated material in a three-dimensional space to be 30/a +1, 30/b +1 and 30/c +1, and finally calculating according to default calculation parameters of VASP;
in step 1.2, if the absolute value of the magnetic moment of any one atom calculated in step 1.1 is greater than 0.02 μ B, then in the following calculation steps 2, 3 and 4, the VASP calculates the control parameter file INCAR with the parameter ISPIN ═ 2, otherwise, the INCAR file is not modified.
3. The method for computationally obtaining material electrical transport properties based on high throughput first principles as claimed in claim 1, wherein said step 2 comprises:
step 2.1, carrying out volume optimization calculation of the step 2.2 on the atomic position, the shape and the volume of the compound by utilizing VASP calculation software, and carrying out optimization calculation of 2.3 if the optimization result of the step 2.2 does not meet the requirement; the calculated symmetrical K points of the material structure optimization are related to the unit cell side length and are set to be 30/a +1, 30/b +1 and 30/c +1, and finally the convergence standard of the force of the material is set to be lower than that of each atom
The energy convergence criterion of the material is 10-4eV；
Step 2.2, optimizing the atomic position and the cell volume of the material at most five times, specifically, when performing VASP calculation, calculating a stress tensor of the material, and simultaneously changing an original package shape and volume of the material, so that the calculation parameters in the step are set to allow the ion position of the material to be changed, the cell volume and the cell shape ISIF is 3, a structure optimization method IBRION is 2, and the calculation result of any one of the five times reaches a convergence standard by using a conjugate gradient algorithm, and then performing the calculation in the step 3;
step 2.3, if the five times of conjugate gradient algorithm optimization does not reach the condition that each atom is lower than
The energy of the material is less than 10-4And performing convergence standard of eV, performing optimization calculation of only the atom position five times, and in order to optimize the atom position in the calculation process, calculating parameter setting parameters of the atom position ISIF which allows the material to be changed to be 0, and calculating a structure optimization method IBRION to be 1, namely using a quasi-Newton algorithm, wherein any one of the five times is performedCalculating in step 3 when the calculation result of the first time reaches the convergence standard;
step 2.4, if the suboptimum state still does not reach the convergence standard, the compound is considered to be incapable of converging, then the compound is marked as structure optimization unconvergence, and the calculation is stopped; if the structure optimization reaches the convergence standard, performing 9 times of material volume simulation stretching compression on the optimized structure, obtaining 9 pairs of energy-volume data through calculation, fitting by using a Vinet state equation to obtain the bulk modulus of the corresponding material, and finally obtaining the bulk modulus based on the formula c ═ k/rho1/2And obtaining the sound velocity of the corresponding material.
4. The method for computationally obtaining material electrical transport properties based on high throughput first principles of claim 1, wherein said step 3 comprises:
step 3.1, judging the material subjected to structure optimization, and if the structure after optimization is lower than that of each atom
The energy of the material is less than 10-4The convergence standard of eV, and the calculation of the step 3 is started;
step 3.2, taking the side lengths a, b and c of the unit cells of the calculated material, setting the high-symmetry K points of the calculated material in a three-dimensional space to be 60/a +1, 60/b +1 and 60/c +1, and calculating by using VASP;
step 3.3, setting the energy convergence standard to 10 in the calculation parameter file-4eV, in the VASP calculation process, if the energy of the material reaches this convergence condition, the charge density, total energy and magnetic moment of the material are obtained through calculation.
5. The method for computationally obtaining material electrical transport properties based on high throughput first principles of claim 1, wherein said step 4 comprises:
step 4.1, judging whether the step 3 reaches 10-4The energy convergence standard of eV, if the energy convergence standard is met, VASP calculation is carried out by utilizing the charge density calculated in the step 3, firstly, the side length a, b and c of a unit cell of the calculated material is obtained, the Brillouin zone high symmetry K point is set to be 240/a +1, 240/b +1 and 240/c +1, and the energy convergence standard is also set to be 10 in a calculation parameter file-4eV, followed by VASP calculations;
step 4.2, if the calculation result of the step 4.1 reaches 10-4And (3) an energy convergence standard of eV, calling a TransOpt program based on the calculation result of the step, setting the deformation potential to be 3eV and the Young modulus to be 100GPa respectively by adopting methods of the deformation potential and the Young modulus, and calculating the point transport property at the calculation temperature of 700K to finally obtain the calculation result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110597707.5A CN113311020B (en) | 2021-05-31 | 2021-05-31 | Method for calculating and predicting thermoelectric performance of material based on high-flux first sex principle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110597707.5A CN113311020B (en) | 2021-05-31 | 2021-05-31 | Method for calculating and predicting thermoelectric performance of material based on high-flux first sex principle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113311020A true CN113311020A (en) | 2021-08-27 |
| CN113311020B CN113311020B (en) | 2023-05-05 |
Family
ID=77376244
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110597707.5A Active CN113311020B (en) | 2021-05-31 | 2021-05-31 | Method for calculating and predicting thermoelectric performance of material based on high-flux first sex principle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113311020B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114218759A (en) * | 2021-11-24 | 2022-03-22 | 电子科技大学长三角研究院(湖州) | Method for predicting thermoelectric performance of perovskite material in high-voltage environment based on density functional theory |
| CN114397420A (en) * | 2021-12-17 | 2022-04-26 | 西安近代化学研究所 | Method for determining compression potential energy of layered stacked energetic compound molecular crystal |
| CN114912296A (en) * | 2022-06-14 | 2022-08-16 | 上海大学 | Method for calculating and screening Peltier active refrigeration material |
| CN116813345A (en) * | 2023-07-14 | 2023-09-29 | 西安科技大学 | Strontium potassium niobate ceramic with high thermoelectric performance and construction method thereof |
| CN116884542A (en) * | 2023-06-29 | 2023-10-13 | 苏州浪潮智能科技有限公司 | First-principles calculation methods, devices, equipment and storage media for a material |
| CN117476117A (en) * | 2023-12-27 | 2024-01-30 | 宁德时代新能源科技股份有限公司 | Method for predicting initial magnetic moment in crystal structure |
| CN117849097A (en) * | 2024-01-05 | 2024-04-09 | 西安交通大学 | A method and system for detecting lattice thermal conductivity of semiconductor materials |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104679965A (en) * | 2015-03-25 | 2015-06-03 | 湖北工业大学 | Method for forecasting thermoelectric conversion power factors of bismuth telluride through WIEN2K software |
| CN106771706A (en) * | 2016-11-22 | 2017-05-31 | 武汉理工大学 | A kind of method of the thermoelectric material of quick screenability optimization |
| CN108365087A (en) * | 2018-01-31 | 2018-08-03 | 许昌学院 | A kind of preparation process based on the surfaces MnAs Half-metallic |
| CN110188429A (en) * | 2019-05-20 | 2019-08-30 | 北京应用物理与计算数学研究所 | A kind of first-principles calculations method and system of the material electric conductivity of electronic metal containing f, thermal conductivity |
| CN110218888A (en) * | 2019-06-20 | 2019-09-10 | 电子科技大学 | A kind of novel Zintl phase thermoelectric material and preparation method thereof |
| CN111062134A (en) * | 2019-12-18 | 2020-04-24 | 哈尔滨工业大学 | A screening method for functional material primitives with optical, electrical and thermal properties |
| CN111581847A (en) * | 2020-05-25 | 2020-08-25 | 长沙理工大学 | Design method of embedded thermoelectric device for road and embedded thermoelectric power generation road surface thereof |
| CN111785831A (en) * | 2020-06-23 | 2020-10-16 | 武汉理工大学 | A method for improving the multi-value storage properties of In2Se3 phase change materials |
| JP2021033964A (en) * | 2019-08-29 | 2021-03-01 | トヨタ自動車株式会社 | Saturation magnetization prediction method and saturation magnetization prediction simulation program |
| US20210118530A1 (en) * | 2019-05-27 | 2021-04-22 | Beijing University Of Technology | Multi-scale method for simulating mechanical behaviors of multiphase composite materials |
| CN112786123A (en) * | 2021-03-03 | 2021-05-11 | 沈阳大学 | Screening efficient AB2O4Theoretical method of spinel semiconductor photocatalytic material |
-
2021
- 2021-05-31 CN CN202110597707.5A patent/CN113311020B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104679965A (en) * | 2015-03-25 | 2015-06-03 | 湖北工业大学 | Method for forecasting thermoelectric conversion power factors of bismuth telluride through WIEN2K software |
| CN106771706A (en) * | 2016-11-22 | 2017-05-31 | 武汉理工大学 | A kind of method of the thermoelectric material of quick screenability optimization |
| CN108365087A (en) * | 2018-01-31 | 2018-08-03 | 许昌学院 | A kind of preparation process based on the surfaces MnAs Half-metallic |
| CN110188429A (en) * | 2019-05-20 | 2019-08-30 | 北京应用物理与计算数学研究所 | A kind of first-principles calculations method and system of the material electric conductivity of electronic metal containing f, thermal conductivity |
| US20210118530A1 (en) * | 2019-05-27 | 2021-04-22 | Beijing University Of Technology | Multi-scale method for simulating mechanical behaviors of multiphase composite materials |
| CN110218888A (en) * | 2019-06-20 | 2019-09-10 | 电子科技大学 | A kind of novel Zintl phase thermoelectric material and preparation method thereof |
| JP2021033964A (en) * | 2019-08-29 | 2021-03-01 | トヨタ自動車株式会社 | Saturation magnetization prediction method and saturation magnetization prediction simulation program |
| CN111062134A (en) * | 2019-12-18 | 2020-04-24 | 哈尔滨工业大学 | A screening method for functional material primitives with optical, electrical and thermal properties |
| CN111581847A (en) * | 2020-05-25 | 2020-08-25 | 长沙理工大学 | Design method of embedded thermoelectric device for road and embedded thermoelectric power generation road surface thereof |
| CN111785831A (en) * | 2020-06-23 | 2020-10-16 | 武汉理工大学 | A method for improving the multi-value storage properties of In2Se3 phase change materials |
| CN112786123A (en) * | 2021-03-03 | 2021-05-11 | 沈阳大学 | Screening efficient AB2O4Theoretical method of spinel semiconductor photocatalytic material |
Non-Patent Citations (6)
| Title |
|---|
| SIRUI FENG: "Quasi-industrially Produced Large-area Microscale Graphene Flakes Assembled Film with Extremely High Thermoelectric Power Factor" * |
| XIN LIA: "TransOpt. A code to solve electrical transport properties of semiconductors in constant electron–phonon coupling approximation", 《COMPUTATIONAL MATERIALS SCIENCE》 * |
| 王聪: "层状热电材料热输运性质第一性原理研究", 《工程科技Ⅰ辑》 * |
| 赵曦: "新型二维热电材料的高通量筛选与计算", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》 * |
| 邓莉: "高温高压下MgSiO3熔体结构和状态方程的第一性原理分子动力学研究", 《基础科学》 * |
| 邹纯鹏: "BiCuChO（Ch=Se,S）热电性能的第一性原理研究" * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114218759A (en) * | 2021-11-24 | 2022-03-22 | 电子科技大学长三角研究院(湖州) | Method for predicting thermoelectric performance of perovskite material in high-voltage environment based on density functional theory |
| CN114397420A (en) * | 2021-12-17 | 2022-04-26 | 西安近代化学研究所 | Method for determining compression potential energy of layered stacked energetic compound molecular crystal |
| CN114397420B (en) * | 2021-12-17 | 2023-12-12 | 西安近代化学研究所 | Determination method for compression potential energy of layered stacked energetic compound molecular crystals |
| CN114912296A (en) * | 2022-06-14 | 2022-08-16 | 上海大学 | Method for calculating and screening Peltier active refrigeration material |
| CN116884542A (en) * | 2023-06-29 | 2023-10-13 | 苏州浪潮智能科技有限公司 | First-principles calculation methods, devices, equipment and storage media for a material |
| CN116813345A (en) * | 2023-07-14 | 2023-09-29 | 西安科技大学 | Strontium potassium niobate ceramic with high thermoelectric performance and construction method thereof |
| CN117476117A (en) * | 2023-12-27 | 2024-01-30 | 宁德时代新能源科技股份有限公司 | Method for predicting initial magnetic moment in crystal structure |
| CN117849097A (en) * | 2024-01-05 | 2024-04-09 | 西安交通大学 | A method and system for detecting lattice thermal conductivity of semiconductor materials |
| CN117849097B (en) * | 2024-01-05 | 2024-11-15 | 西安交通大学 | A method and system for detecting lattice thermal conductivity of semiconductor materials |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113311020B (en) | 2023-05-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113311020A (en) | Method for calculating and predicting thermoelectric performance of material based on high-throughput first principle | |
| CN103577646B (en) | A kind of computational methods of quick estimation integrated circuit yield | |
| Cai et al. | Adaptive radial-basis-function-based multifidelity metamodeling for expensive black-box problems | |
| CN105790258B (en) | Latin hypercube probability load flow calculation method based on normal state Copula functions | |
| CN107341301B (en) | Method for predicting wettability between defect-introduced graphene and metal | |
| Yang et al. | Dual adaptive sampling and machine learning interatomic potentials for modeling materials with chemical bond hierarchy | |
| Milardovich et al. | Machine learning interatomic potential for silicon-nitride (Si3N4) by active learning | |
| Rodin et al. | Microscopic theory of ionic motion in solids | |
| CN114462178A (en) | Design method of wearable two-dimensional asymmetric material with high thermoelectric conversion efficiency | |
| Yamakoshi et al. | Multicrystalline informatics applied to multicrystalline silicon for unraveling the microscopic root cause of dislocation generation | |
| CN118351985A (en) | Battery three-dimensional electrochemical model construction method and calculation method | |
| Michel et al. | New propagation model optimization approach based on particles swarm optimization algorithm | |
| Verreck et al. | 3D TCAD model for poly-Si channel current and variability in vertical NAND flash memory | |
| CN105808508B (en) | It is a kind of to solve the random orthogonal method of deploying for not knowing heat conduction problem | |
| CN116994677A (en) | A high-throughput calculation method for electrical transport properties based on deformation potential approximation | |
| CN115168806A (en) | A method for calculating the off-site threshold energy of semiconductor materials | |
| Bauer et al. | Influence of disorder and surface roughness on the electrical and thermal properties of lithiated silicon nanowires | |
| CN114218759A (en) | Method for predicting thermoelectric performance of perovskite material in high-voltage environment based on density functional theory | |
| Wu et al. | Advances in atomistic modeling for predictive TCAD applications | |
| CN119066895B (en) | Three-dimensional electrothermal characteristics analysis method and system for semiconductor devices | |
| CN118839622B (en) | Acceleration method for high-precision two-dimensional potential simulation of semiconductor device | |
| CN119361041A (en) | A method and system for screening high-performance layered nitride thermoelectric materials | |
| CN110889191A (en) | Method and device for predicting adhesion strength of silver film in low-emissivity glass on substrate | |
| CN117973030A (en) | A simulation design method for anisotropic two-dimensional semiconductor materials and its application | |
| Tian et al. | Optimizing Temperature and Flow Fields of 4H-SiC Epitaxial Growth by Integrating CFD Simulation with Multi-objective Particle Swarm Optimization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |