46 results about "High-throughput computing" patented technology

A new energy material safety evaluation method and system based on multi-agent reinforcement learning, and an electronic device, relating to the field of data mining and analysis for the safety of new energy materials against heavy object impact and thermal stimulation. The new energy material safety evaluation method based on multi-agent reinforcement learning comprises: using high-throughput computing to acquire data sets; providing a safety evaluation threshold on the basis of a hotspot formation and evolution process; labeling each sample in an initial data set with a safety label on the basis of the threshold to obtain a training data set; establishing a mapping relationship between the training data set and multi-agents; using an agent action selection policy to select an action having the maximum reward value to obtain a current cluster distribution result of the multi-agents; calculating a sample silhouette coefficient, a cluster silhouette coefficient, and an overall silhouette coefficient; using a reward function to calculate reward values of the multi-agents, and obtaining cluster centroids when the number of iterations satisfies a preset threshold; and determining a new energy material safety evaluation result on the basis of the cluster centroids.

The invention relates to a data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method, which belongs to the technical field of metal material design and development, and comprises the following steps: calculating a stable high-strength deformation nickel-based superalloy system based on a first principle, and on this basis, calculating a deformation nickel-based superalloy system; constructing a deformed nickel-based superalloy component design space based on the knowledge of the nickel-based superalloy field; based on a deformed nickel-based superalloy component design space, the deformed nickel-based superalloy component design space is reduced by adopting an empirical formula in combination with thermodynamic high-throughput calculation to obtain the reduced deformed nickel-based superalloy component design space, and based on the reduced deformed nickel-based superalloy component design space, based on machine learning and a genetic algorithm, the deformation nickel-based superalloy component design space is obtained. And a deformed nickel-based high-temperature alloy reverse design model is established, and the deformed nickel-based high-temperature alloy with the target performance is screened. Compared with the prior art, the oriented development method for the high-strength and high-toughness nickel-based high-temperature alloy is achieved by fusing cross-scale calculation, domain knowledge constraint and machine learning reverse design.

The invention discloses a method for screening an MOFs material with excellent CH4 adsorption separation performance based on high-throughput calculation, a method for improving the screening efficiency and a preparation method. The screening method comprises the following steps: constructing a screening database of MOFs material structure information; calculating physical and chemical characteristic parameters by utilizing computational chemistry and material simulation software, and further setting a screening standard aiming at CH4 adsorption; carrying out preliminary screening through a high-throughput screening technology; predicting the competitive adsorption behavior of the primarily selected material in the mixed gas by means of a molecular simulation technology, and performing secondary screening; and finally, determining the final MOFs material in combination with an actual application environment. According to the method, the high-performance MOFs material is rapidly and accurately screened out from a large number of materials, the research and development period is effectively shortened, the development cost is reduced, an efficient and feasible solution is provided for resource utilization of ultralow-concentration CH4 in coal mine windblown gas, and energy utilization and environmental protection are assisted.

The invention relates to an iron-based high-temperature alloy design method based on multi-model machine learning and an alloy thereof, and the method comprises the following steps: 1, obtaining iron-based high-temperature alloy components, carrying out high-throughput calculation, and constructing a data set; 2, correlation analysis is carried out on the data set, feature importance sorting is carried out based on a random forest model, and key components are screened out; 3, constructing a machine learning algorithm model, training by using the key components, and adjusting and optimizing hyper-parameters of the machine learning algorithm model through an optimization algorithm to obtain a trained prediction model; 4, constraint conditions are constructed and optimized, an optimal solution set is obtained, the optimal solution set is input into the trained prediction model, and optimal alloy components are obtained; and 5, laser powder bed melting forming is conducted according to the optimal alloy components, and the iron-based high-temperature alloy is obtained. A data-driven machine learning method is adopted to replace a traditional trial and error method, the alloy research and development period is greatly shortened, and the research and development cost is reduced.

The invention relates to a nickel-based powder superalloy component treatment method which comprises the steps that the mass percent range and the step length of target alloy elements in a superalloy are determined on the basis of preset target performance parameters; based on the mass percentage range and the step length of the target alloy element, high-throughput calculation is conducted, a high-throughput calculation result is obtained, and the high-throughput calculation result comprises all candidate alloy component combinations, the volume fraction of gamma'phases corresponding to the candidate alloy component combinations and the equilibrium precipitation proportion of harmful TCP phases; and a target alloy component combination is screened out from all the candidate alloy component combinations. According to the method, the volume fraction of the gamma'phase corresponding to each candidate alloy component combination, the equilibrium precipitation proportion of the harmful TCP phase and all the preset mechanical property parameters can be accurately calculated; and furthermore, a target alloy component combination which can meet the excellent high-temperature performance and effectively inhibit harmful phase precipitation at the same time can be quickly screened out from all candidate alloy component combinations.

The present application relates to the field of catalytic material research and development, and particularly relates to a catalytic material high-throughput calculation method and screening system. The method comprises the following steps: S1, setting configuration information of catalytic material high-throughput calculation, wherein the configuration information comprises project calculation information, initial structure file, calculation control file and external calculation resource information; S2, concurrently completing multiple high-throughput calculations based on the configuration information; the screening system adopts a micro-service architecture, wherein the micro-service architecture comprises a registration center module, an input file generation service module, a Web service module, a calculation scheduling service module, a calculation execution service module and a result collection service module, each module is highly cohesive and lowly coupled, and each module calls each other, so that the normal operation of the entire program is realized. The present application realizes multiple job concurrent automatic calculation according to the characteristics of the catalyst material, and improves the efficiency of simulation calculation of a large number of catalytic materials.

The application discloses an alloy component optimization design method for additive manufacturing and belongs to the technical field of additive manufacturing. The alloy component optimization design method for additive manufacturing expands the content numerical range of the alloy component to include all existing alloy component contents on the basis of existing alloy components, combines thermodynamic calculation and high-throughput calculation, optimizes suitable alloy components according to a strain rate hot cracking criterion based on a hot cracking sensitivity index, prepares alloy powder for additive manufacturing according to the optimized alloy components, performs laser additive manufacturing, observes the microstructure of the sample after additive manufacturing and tests the performance of the sample, and selects the component optimization meeting the actual alloy performance. The application takes the alloy component optimization design as a main influencing factor, adopts thermodynamic software and computer language, optimizes the component through the hot cracking sensitivity index to reduce the hot cracking sensitivity of the additive manufacturing alloy, and is beneficial to industrial large-scale production and popularization and use.

The invention belongs to the technical field of data processing, and discloses a cable insulation material type selection method and device, electronic equipment and a medium. The method comprises the steps of obtaining candidate material data, carrying out high-throughput calculation on the candidate material data to obtain corresponding target microscopic features, inputting material components and the corresponding target microscopic features into a target prediction model to obtain corresponding performance prediction vectors, and taking macroscopic performance represented by the performance prediction vectors as an optimization target to obtain a target prediction model. The method comprises the following steps: performing multi-target iterative optimization solution on candidate material data on the basis of a non-dominated sorting genetic algorithm, introducing a dynamic coupling constraint rule in an optimization process to obtain a Pareto optimal solution set, and screening target material data from the Pareto optimal solution set, so that microscopic features of mass material data can be generated through high-throughput calculation, and the accuracy of the material data is improved. And a multi-objective optimization algorithm is utilized to realize cooperative adaptation of the cable insulation material and the working condition, and the problems of low efficiency and difficulty in multi-objective coupling optimization of traditional model selection are solved.

This invention relates to a high-throughput calculation and analysis method for the glass transition temperature of polyurethane resin. First, a surface cell model of the polyurethane to be tested is constructed using Material Studio software. Then, the LAMMPS module in Matclid+ is used to further optimize the constructed surface cell model of the polyurethane to be tested. Finally, the correspondence between the polyurethane structure and the glass transition temperature is quickly established, reducing unnecessary physical experiments and enabling targeted experimental research and development of polyurethane resin. Comparison with experimental values ​​shows that this high-throughput calculation method has good predictive accuracy. Through its unique high-throughput calculation method, it can also calculate the glass transition temperature of polyurethane systems with various raw material components, greatly saving experimental time and costs.

The invention discloses a multifunctional catalyst performance prediction method based on high-throughput calculation and machine learning, and relates to the technical field of catalytic material prediction. The method comprises the following steps: acquiring basic structure information of a target catalyst, correspondingly calculating catalytic performance data and related characteristic data of the target catalyst, and pairing the basic structure information and the related characteristic data to establish a data set; importing the data set into a plurality of machine learning models for training, and performing hyper-parameter tuning by using multi-target Bayesian optimization; based on the screened optimal model, sorting and discriminating key features influencing the performance of the catalyst by using an SHAP value, and importing the key features into an interpretable machine learning model SISSO for multi-task training; based on an interpretable machine learning model SISSO and through a symbol regression method, obtaining an explicit mathematical relationship between the feature combination and the catalytic performance as a descriptor formula for application; the multi-aspect catalytic performance of the catalyst can be rapidly predicted, and the research and development efficiency of the catalyst is greatly improved.

According to the time-space-spectrum coding high-throughput calculation imaging architecture based on lithium niobate interference and electro-optical regulation, space inconsistency interference spectrum modulation and time dynamic controllable coding are achieved in a single device, an intelligent reconstruction algorithm is combined, the dimension limitation of an existing scheme is broken through, and the space inconsistency interference spectrum modulation and time dynamic controllable coding are achieved. And a brand new technical path is provided for astronomical snapshot spectral imaging and high-speed industrial detection.

The present application relates to a kind of data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method, belong to metal material design development technical field, including the following steps: based on first principle calculation stable high-strength deformation nickel-based superalloy system, and based on this, based on nickel-based superalloy field knowledge, construct deformation nickel-based superalloy composition design space;Based on deformation nickel-based superalloy composition design space, using empirical formula is combined with thermodynamics high-throughput calculation to reduce deformation nickel-based superalloy composition design space, obtain the reduced deformation nickel-based superalloy composition design space, and based on this, based on machine learning and genetic algorithm, establish deformation nickel-based superalloy reverse design model, filter target performance deformation nickel-based superalloy.Compared with prior art, the present application proposes a kind of fusion cross-scale calculation, field knowledge constraint and machine learning reverse design, realize the directional development method of high-strength and high-toughness nickel-based superalloy.

Embodiments of the present application disclose a molecular structure data generation method, device, equipment, medium and program product. The method comprises: obtaining an initial molecule, the initial molecule being represented by an undirected graph without self-loop, the undirected graph comprising nodes for representing atoms and edges for representing chemical bonds; adding preset atoms and / or preset chemical bonds to the initial molecule according to a preset molecule generation rule to obtain a plurality of target molecules, wherein the molecule generation rule comprises an atom addition rule, a chemical bond addition rule, a valence rule and a molecule stability rule, and the target molecules are represented by undirected graphs without self-loop; and converting the plurality of target molecules into a simplified molecular linear input specification (SMILES) format to obtain a plurality of target molecular structure data. The embodiments of the present application can efficiently generate molecular structure data in batches, thereby supporting application scenarios such as machine learning, high-throughput calculation and high-throughput screening.

The application discloses a method for high-throughput calculation of electronic structures of different temperatures of semi-Hasler materials. The calculation is mainly based on a MatHub-3d database. There are 274 semi-Hasler materials in the database. First, the structures of the 274 materials are optimized, and then the electronic structures and phonon spectra are calculated, and 109 stable semiconductor structures with no virtual frequency and a band gap greater than 0.1 eV are screened. Then, the electronic structures at 11 temperatures from 0 to 1000K are calculated by applying Allen-Heine-Cardona (AHC) theory. The method can improve the efficiency by high-throughput calculation, achieve the purpose of quickly calculating the target material system, and provide a theoretical reference for the design of photoelectric and thermoelectric materials.

The invention relates to the technical field of steel and iron material research and development, in particular to an intelligent steel and iron material research and development system and method, and the system comprises a high-throughput calculation data module, a laboratory data module, a machine learning model training module, a material performance prediction module and a multi-objective optimization module. By integrating multi-source data of the whole life cycle of steel and iron material research and development, production and application, information islands are eliminated, comprehensive display and comprehensive analysis of steel and iron material research and development data are achieved, deep mining and application are conducted on the data through a machine learning algorithm, the efficiency and accuracy of new steel grade research and development are improved, a multi-target optimization function is provided, and the research and development efficiency is improved. Various performance indexes of the material can be optimized at the same time, the requirements of complex application scenes are met, a large model auxiliary research and development function is provided, help and guidance can be provided at any time in the steel material data analysis and component process design process by research and development personnel, and the material development process is accelerated.

The application relates to the technical field of simulation calculation, in particular to a high-throughput calculation method and system for catalytic materials based on machine learning. The method comprises the following steps: establishing a two-dimensional sparse matrix by taking a catalyst identifier and a condition parameter as indexes and taking a known performance index value as a filling value; generating a mixed feature vector by mapping through a multilayer perception network based on non-empty coordinates and the catalyst identifier; obtaining a sample score by performing feature fusion on the mixed feature vector through a neural collaborative filtering network and a full-connection neural network, intercepting samples based on the condition parameter, and outputting a to-be-verified list; calculating performance data by using a simulation calculation model, backfilling the performance data to the two-dimensional sparse matrix, and updating the non-empty coordinates; judging based on a termination condition, and when the termination condition is not met, iteratively predicting the non-empty coordinates; and when the termination condition is met, outputting a catalytic material list. The application deeply couples structured space indexing, double-channel deep learning and adaptive closed-loop iteration, and improves data utilization.

The invention relates to a high-throughput calculation method and system for the heat conduction characteristic of a self-assembled monomolecular layer interface and a medium. The high-throughput calculation method comprises the following steps: introducing a terminal group X structure to perform chain splicing with-S (CH2) n-to construct a monomer chain structure; carrying out charge distribution and force field distribution on the single chain structure; performing high-flux modeling on the monomer chain structure subjected to force field and charge distribution, and constructing a high-flux solid-liquid interface simulation system based on a self-assembled monomolecular layer, a gold substrate and water molecules; sequentially performing NVT simulation, NPT simulation and unbalanced molecular dynamics simulation on the high-flux solid-liquid interface simulation system to obtain heat flux density and a temperature gap; and calculating the interface thermal conductivity of the self-assembled monomolecular layer according to the obtained heat flux density and temperature gap. Compared with the prior art, the method has the advantages that high-throughput calculation of the heat conduction characteristic of the interface of the self-assembled monomolecular layer is realized, and the method is of great significance to development of regulation and control design of the heat conduction characteristic of the interface of the self-assembled monomolecular layer under data driving.

The present application provides a kind of material interface phase mechanical property prediction method, device, electronic equipment and storage medium, the prediction method includes: obtaining the micro parameter corresponding to test sample, micro parameter includes filler mechanical parameter, matrix mechanical parameter and interface evaluation parameter;Based on micro parameter, high-throughput calculation is used to obtain the macro parameter corresponding to micro parameter;With micro parameter as input parameter, macro parameter as output parameter, generate the prediction model corresponding to macro parameter;The value of the micro parameter to be measured of the sample to be measured is taken, and the prediction model is substituted into the micro parameter to be measured of the sample to be measured to be measured to obtain the interface mechanical property corresponding to the sample to be measured.The prediction method establishes the prediction model relationship between micro parameter and macro parameter by high-throughput calculation and other means, so as to realize the reverse deduction of micro parameter by macro calculation and test.The prediction method has low running cost, simple operation, and high accuracy of the obtained numerical value.

The invention discloses a transition state initial guess structure generation method, and belongs to the technical field of computational chemistry and computer-aided molecular design. The method comprises the following steps: inputting a substrate three-dimensional template file containing reaction priori knowledge and a two-dimensional topological sequence of a catalyst ligand, and automatically identifying ligand donor atoms and completing coordination assembly of a metal center by utilizing an algorithm; and then connecting the catalyst with the substrate template by adopting a conformation generation algorithm based on chemical constraint, executing restrictive conformation optimization under the condition of freezing the core coordinates of the substrate template, and screening and outputting a transition state initial guess three-dimensional structure with the lowest energy. The method not only can greatly improve the efficiency of establishing the transition state, but also can effectively avoid atomic space conflict and molecular fragmentation, and ensures the stability and rationality of the generated initial guess structure; meanwhile, high-throughput batch processing and automatic generation of quantum chemical calculation software with constraint interfaces are supported, and the method is particularly suitable for molecular design in the fields of reaction mechanism research, catalyst performance prediction and high-throughput calculation catalysis.

The application provides a material design method and system based on agent cooperation, and relates to the technical field of the cross of artificial intelligence and material science. The method comprises: collaborative work of an automatic task planning decomposition module, an automatic thermoelectric knowledge retrieval module, a material system prediction module, a high-throughput calculation module, a result analysis module and a feedback optimization module. The core is that the material system prediction module couples a microscopic physical and chemical rule constraint mechanism when generating a structure sequence of a candidate material by using an autoregressive generation model. The mechanism generates a dynamic word table mask, hard blocks candidate words that do not conform to the physical and chemical rules at each word prediction step, and guarantees the rationality of the generated structure from the source. At the same time, the feedback optimization module compares and analyzes the results and the research and development demand, generates an adjustment instruction feedback to the upstream module, and forms a closed loop optimization. The application can improve the design efficiency from the source, and realizes the automation and intelligent iteration of the whole process of material research and development.

The invention discloses an amorphous nanocrystalline magnetically soft alloy powder grading method, which comprises the following steps: based on a powder close packing model, carrying out high-throughput calculation through a computer program, establishing the correlation between a grading scheme and a packing density parameter, and quickly and accurately obtaining a grading scheme with a high close packing degree. The porosity of the amorphous nanocrystalline soft magnetic composite material can be remarkably reduced, the high-frequency soft magnetic performance is comprehensively improved, and the contradictory relation between high magnetic conductivity and high direct current bias performance is overcome. The amorphous nanocrystalline soft magnetic alloy powder grading method provided by the invention has the advantages of accuracy, high efficiency and the like, and is of great significance to research and development of soft magnetic composite materials with high magnetic conductivity and low loss.

The invention provides a DLP equipment and material collaborative process, and relates to the technical field of high-end electronic material design and intelligent manufacturing, and the DLP equipment and material collaborative process comprises the following steps: based on high-throughput calculation, machine learning and material genome engineering technologies, constructing a multi-target generative AI model fusing a polymerization activity discriminator and printability scoring; the method has the advantages that the problem of compatibility of material chemical rationality and DLP process feasibility is solved by constructing a generative AI and printability dual-threshold screening collaborative system fusing physical constraints, and the sample piece precision, surface quality and performance consistency are remarkably improved by means of dynamic optimization manufacturing and online real-time evaluation; and finally, multi-target collaborative optimization is realized within 30 rounds of iteration through data closed-loop feedback, the research and development period is greatly shortened, the cost is reduced, a portable platform and a database are formed, and a full-chain technical support is provided for autonomous controllability and industrialization of high-end electronic materials.

The invention relates to the technical field of bioinformatics, and discloses a method, a system, equipment and a medium for calculating codon use frequency of species, which solves the problem of deviation caused by neglecting expression quantity difference in the existing method by introducing gene expression quantity data and calculating codon use frequency by adopting a weighting algorithm. The actual use condition of codons in species can be reflected more truly; the method is suitable for single gene analysis, can be expanded to a whole genome level, comprehensively reflects gene sequence evolution difference and codon selection preference of different species by considering species specificity difference, and provides more reliable data support for biological research; the method has the functions of accurate gene sequence inspection, friendly error reporting interface and high-throughput calculation, supports cross-platform operation, and remarkably improves the practicability and application range; through a weighting algorithm and cross-platform software, the calculation accuracy of the codon use frequency is remarkably improved, and a more efficient and reliable tool and method are provided for genetic engineering and biological research.

The invention discloses a CF4 / N2 separation-oriented molecular sieve performance cross-temperature zone rapid prediction and optimization method. The method mainly comprises the following steps: calculating single-component and mixed adsorption amounts of CF4 and N2 in a certain temperature range of a zeolite database based on giant regular Monte Carlo simulation high throughput; fitting a plurality of adsorption isotherm models, and screening the optimal adsorption isotherm model to construct an adsorption capacity-temperature-pressure data matrix; data enhancement is carried out in combination with CCR, and rapid and continuous generation of the adsorption isotherm at any temperature within a wide temperature range is realized; predicting the CF4 / N2 adsorption selectivity based on an IAST interpolation method, and comparing the CF4 / N2 adsorption selectivity with a GCMC calculation result for verification; finally, within the applicable temperature range, the adsorption capacity and the selectivity performance target of the mixed components are integrated and optimized, and intelligent screening of the high-performance molecular sieve is achieved. The method can significantly improve the acquisition efficiency and accuracy of adsorption data at different temperatures, and is suitable for rational design and intelligent optimization of the adsorbent under a wide temperature condition.

The invention discloses a high-throughput topological optimization method for additive manufacturing of wing joint parts, and relates to the field of structural topological optimizing.The method comprises the steps that the sizes of the wing joint parts are collected, and an equal-size finite element model is constructed; establishing a material knowledge base through a machine learning model, and screening part material components based on a high-throughput calculation method; in combination with a service condition and an additive manufacturing process, constraint conditions under different working conditions are added to the model, and topological iterative optimization is carried out until the constraint conditions are met; the optimization results are screened based on the optimization indexes, and an optimization scheme capable of meeting the quality optimization indexes is obtained; and performing post-processing on the optimization model to enable the model to be smoothened, performing final checking on the performance of the model, and performing screening to obtain an optimal structure. According to the method, a topological optimization method and a high-throughput calculation method are combined, material optimization is carried out while structural optimization is carried out on the parts, the optimization efficiency of the joint parts can be remarkably improved, the weight of the parts is reduced, and the cruising ability of an aircraft is improved.

The embodiment of the invention relates to an atomic spatial distribution analysis method and device based on an ADF-STEM graph. The method comprises the following steps: constructing a first GAN model, a second GAN model and a first prediction model; constructing a cross-system and cross-experiment style first data set; performing joint training on the two GAN models according to the first data set according to a training mechanism of a cyclic generative adversarial network; after the training is finished, constructing a second data set by using a first generator of the first GAN model to train a first prediction model; after training is finished, atomic-scale three-dimensional space distribution analysis is conducted on the sample ADF-STEM graph of any system through the first prediction model, and monatomic density analysis, dimer distribution analysis and trimer distribution analysis are conducted on the basis of an analyzed atom set. According to the method, atomic-scale three-dimensional space distribution analysis can be carried out, cross-system cross-experiment style analysis can be carried out, and the method can adapt to a high-throughput calculation scene.

The invention discloses a flat region research and design method for a medium-wave high-temperature infrared detector, and relates to the technical field of semiconductors, and the method comprises the steps: constructing an infrared detector simulation model; setting parameter ranges of the absorption layer and the barrier layer; under the fixed working temperature and the fixed working bias voltage, high-throughput calculation is carried out on a plurality of infrared detector simulation models formed by combining different parameter values, and the dark current magnitude and the flat region range corresponding to each infrared detector simulation model are obtained; and analyzing an influence mechanism of parameter changes of the absorption layer and the barrier layer on the performance of the detector based on the size of the dark current and the range of the flat region. By changing the thickness and doping concentration of the absorption layer and the barrier layer, the detector structure under various different parameters is formed through combination, and by calculating the dark current and the flat region range of detectors of different structures, the quantitative mapping relation among dark current, flat regions and material parameters is established, and theoretical support is provided for dynamic range optimization.

The present invention relates to the field of structural ceramics, and specifically to a high-throughput screening method for novel layered perovskite materials with high damage tolerance. The method comprises: S1, determining the space group and composition range of the material, automatically searching the crystallographic public database, and obtaining a structure file that meets the requirements; S2, calculating and obtaining the elastic constant c ij , and the band gap E g High-throughput calculation; after cell expansion, calculate the interatomic force constants of all materials; S3, screen materials; S4, analyze the anisotropy of materials and define the thermal conductivity anisotropy index A κ =κ x / κ z The present invention uses a high-throughput computational method to quickly grasp the mechanical properties of layered materials and obtain materials that meet the requirements based on mechanical stability and damage tolerance criteria. It can also reveal the anisotropic properties of layered materials, reducing the time and resource consumption required by traditional trial-and-error methods.

The invention belongs to the technical field of thermal stress of thermal barrier coatings, and particularly relates to a high-throughput design method for a gradient structure of an ultrahigh-temperature thermal shock resistant thermal barrier coating. Based on Abaqus finite element simulation software, high-throughput calculation is carried out on a coating gradient structure, a high-throughput calculation method used for analyzing thermal stress of different structures at high temperature and screening optimal coating structure design parameters of different coating materials, thicknesses and base materials is developed, the mapping relation between structure evolution and coating performance is obtained, and high-throughput calculation is carried out on the coating gradient structure. And subsequent screening of customized coating structure parameters is facilitated. The invention provides a full-process analysis algorithm from macroscopic parameter input to final result analysis, the coating structure optimization process is accelerated, and the optimization time is shortened; the high-throughput parameter generation speed and the model component efficiency are improved by several times, and output data can be directly used for a material genome plan database; the analysis of the change rule of part of parameters along with the coating structure has important significance for further prolonging the service life of the coating.

The invention provides a positive electrode material reverse design method, device and equipment, a medium and a vehicle, and the method comprises the steps: responding to a reverse design request for a lithium-rich manganese-based positive electrode material, and carrying out the high-throughput calculation of a plurality of candidate element doping combinations based on a target performance parameter through a machine learning potential function, determining an optimal element formula and atomic scale key parameters meeting target performance parameter constraints; based on the optimal element formula and the atomic scale key parameters, performing phase field simulation by adopting an anisotropic lattice strain model containing Jahn-Teller distortion correction, and determining bulk phase gradient structure features and surface coating layer configuration parameters; and based on the bulk phase gradient structure characteristics and the surface coating layer configuration parameters, calling a mapping model of a process and a structure stored in a preset knowledge graph to perform process inversion, and determining a candidate coating material system and a corresponding synthesis process path thereof, so that the success efficiency and research and development capability of the positive electrode material design are improved.