156 results about "Quantum chemistry" patented technology

The invention provides a gradient crosslinking process of photosensitive resin for photocuring 3D printing, and relates to the technical field of photocuring 3D printing materials. The gradient crosslinking process of the photosensitive resin for photocuring 3D printing comprises the following steps: S1, based on double-initiator system design, adopting molecular dynamics simulation screening, respectively adding a 405nm sensitive type TPO-L initiator and a 365nm sensitive type I TX initiator into an epoxy acrylate prepolymer, and verifying initiation energy barrier matching through quantum chemistry calculation, and generating a pre-initiation gradient resin formula. Through molecular dynamics simulation, initiator combinations are screened, energy barrier matching is verified, the ratio of dual-wavelength sensitive initiators is optimized, and accurate triggering of surface layer and deep layer cross-linking reaction is ensured. And a three-roller grinding and dispersing process is combined with ultrasonic oscillation to eliminate nanoparticle aggregation, so that the mechanical uniformity and light scattering effect of a resin system are improved.

The invention provides a method and a system for evaluating the luminescence performance of a near-infrared two-region molecular fluorescent probe, and relates to the technical field of fluorescent probes, the method comprises the following steps: obtaining the initial configuration of the near-infrared two-region molecular fluorescent probe in an aqueous solution, and carrying out molecular dynamics simulation on the initial configuration to obtain a plurality of representative conformations of the probe in the aqueous solution; performing quantum chemical calculation on each representative conformation to obtain first system energy of each representative conformation in a stable state and second system energy and intensity parameters of each representative conformation in a lowest singlet excitation state; generating a comprehensive evaluation factor based on the first system energy, the second system energy and the intensity parameter; and sorting the luminescence properties of the probes based on the comprehensive evaluation factor, and screening out the probe with the optimal performance. According to the method provided by the invention, quantitative evaluation on the luminescence property of the near-infrared two-region molecular fluorescent probe is realized, and technical support is provided for optimal design and clinical application of the probe.

The invention discloses a reaction site prediction method and device based on chemical and physical prior driving, and the method comprises the steps: extracting set features through the multi-modal input of a fusion molecular map, an SMILES sequence and a three-dimensional conformation; generating atomic embedding by using a message passing neural network, and calculating a mixed feature fusing a topological path and a three-dimensional distance; combining the key type weight to construct a graph position code of chemical environment correction; injecting the mixed distance and the charge difference into a Transform attention mechanism, and explicitly modeling an inter-atomic long-range electron effect; a model is jointly trained through double tasks of comparative learning and mask prediction, the comparative learning adopts a directional negative sample to enhance generalization, and mask prediction synchronously recovers an atom type and a charge transfer matrix; and finally, injecting quantum chemistry priori constraint attention weights such as a Fuzzy well function, outputting an atomic-scale reaction activity probability, generating a thermodynamic diagram, and realizing high-precision and interpretable active site labeling. According to the method, the drug design and reaction mechanism analysis efficiency can be remarkably improved.

Provided are an information processing method and the like in which it is possible to efficiently obtain a compound capable of having a good property. In the information processing method, includes acquiring a spectral indicator obtained by quantum chemical calculation in a plurality of compounds to be candidates, classifying, on the basis of the acquired spectral indicator, each of the compounds into a group in which the spectral indicator satisfies a predetermined condition and extracting a group in which the spectral indicator does not satisfy the predetermined condition, and a light emitting compound classified into the group in which the spectral indicator satisfies the predetermined condition.

The invention discloses an asphaltene molecule nano-scale characteristic analysis method based on quantum chemistry calculation, which comprises the following steps: acquiring composition information of asphaltene components, and constructing a representative asphaltene molecule three-dimensional model containing a fused aromatic ring skeleton and an alkyl side chain; performing geometric configuration optimization on the representative asphaltene molecule three-dimensional model by adopting a quantum chemistry density functional theory method to obtain a stable configuration of the asphaltene molecule; determining key nano parameters of the asphaltene molecules according to the stable configuration of the asphaltene molecules; and associating the key nano-scale parameters of the asphaltene molecules with the macro-performance of the asphalt material, and establishing an asphalt performance association model. According to the method, the key parameters capable of representing the asphaltene microstructure and property are obtained, so that the understanding of the relationship between the asphaltene composition and the performance of the asphalt material is deepened, the performance of the asphalt material is evaluated from the molecular scale, and a new thought is provided for performance prediction and modification of the asphalt material.

The invention discloses a molecular simulation method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring molecular system information of a current molecular configuration; calculating the first energy and the first gradient of the key atom region by adopting a variable component sub-feature solicitation solver algorithm, and calculating the second energy and the second gradient of the non-key atom region; wherein the first gradient is used for expressing the stress condition of each atom in the key atom area; the second gradient is used for expressing the stress condition of each atom in the non-key atom area; and updating the current molecular configuration according to the first energy, the first gradient, the second energy and the second gradient to obtain a target molecular configuration. According to the method and the device, the technical problem that the accuracy of the simulation result cannot be ensured under the condition that the computing resources are limited due to excessive computing resources consumed by the quantum chemical computing mode when the molecules are simulated by adopting the quantum chemical computing mode in the related technology is solved.

The invention relates to a quantum chemistry Hamiltonian rapid generation method and device based on a stability sub-table, and the method comprises the steps: obtaining a single electron integral item, a double electron integral item and a nuclear energy item of a target Fermi Hamiltonian, and storing the nuclear energy item; dividing the equal double-electron integrals into the same calculation object class, and deleting double-electron integral items corresponding to the double-electron integrals of which the absolute values are smaller than a threshold value; converting the single-electron integral item and the double-electron integral item into a Bose Hamiltonian expression form; filling the stable sub-form table based on all Fouli items to obtain a Fouli check matrix, and recording a coefficient vector; the same row vectors in the Follicle check matrix are merged, the Follicle coefficients corresponding to the merged row vectors are added, and the row vectors with the Follicle coefficients being 0 are deleted; and storing the Follicle check matrix and the coefficient vector. According to the technical scheme, the efficiency of converting the Fermi Hamiltonian into the Bose Hamiltonian can be improved.

The invention discloses a gold mine exploration method based on geological data analysis, and relates to the technical field of mineral geological exploration, and the method comprises the following specific steps: collecting rock samples of different stratums in a region, determining the content of gold element and associated elements thereof, observing the occurrence state of the elements, and establishing an element occurrence state database; by introducing quantum chemical calculation and a particle swarm optimization algorithm, refined simulation of migration behaviors of the gold element and associated elements thereof in a geologic body is realized, and chemical reaction characteristics and microscopic parameters of diffusion coefficients of the elements under different conditions can be described more accurately; in addition, a mathematical model reflecting actual conditions can be constructed according to the parameters, reliability and accuracy of a tracking result are ensured, the process enables the possibility of gold mineralization to be predicted in a larger range, the risk of blind drilling is reduced, and therefore the efficiency of exploration work is remarkably improved.

The invention discloses a biomass pyrolytic reaction network prediction method based on a graph theory and quantum chemistry, which comprises the following steps: analyzing the molecular structure of a biomass initial reactant, abstracting the molecular structure into a directed graph molecular model, marking a potential conversion path after molecular mechanics optimization, generating a unique identifier by using an atom numbering rule and a Hash algorithm, and predicting the biomass pyrolytic reaction network. Functional groups are extracted and packaged into sub-graph modules, chemical bond parameters are obtained in combination with quantum chemistry calculation and are converted into fracture probabilities, basic reaction building blocks are predefined to traverse and mark breakable bonds, fragments are recombined and then de-weighted through Hash, and then reaction activation energy weights are given to side chemical bond fracture reactions; an energy optimal path is screened by means of a priority queue search algorithm, accurate analysis of a chemical bond fracture path and intermediate product conversion in the biomass pyrolysis reaction network is finally achieved, reaction path and product distribution are efficiently predicted, and the calculation efficiency and precision of a complex system are balanced.

Aspects of the present disclosure relate generally to systems and methods for optimizing an implementation of a quantum chemistry task in a quantum circuit. The method includes obtaining a chemistry task presented as a Hamilton to implement in a Variational Quantum Eigensolver (VQE) circuit. The method also includes mapping the chemistry task to a qubit space of the VQE circuit with Unitary Coupled Cluster with Single and Double excitations (UCCSD) ansatz. The method also includes decomposing the fermionic excitations to Givens rotations and controlled-Z (CZ) gates by passing exponentiated Pauli forms in the VQE circuit to a cloud compiler. The method further includes after commuting the CZ gates through Givens rotations, cancelling the commuted CZ gates. The method further includes dropping CZ gates at the beginning and the end of the quantum circuit.

The invention provides a semiconductor material precise modeling method based on structured defect information, and relates to the technical field of ultra-wide forbidden band semiconductors, and the method comprises the steps: obtaining material structured defect information, and obtaining the lattice structure, forbidden band width, Raman spectrum, defect energy level, defect atomic percentage and the like of an experiment; normalizing the defect atom percentage to obtain the calculated defect atom molar concentration and the like; establishing an initial density functional theoretical model, and performing structural relaxation optimization processing to obtain a stable model; using a defect fitting algorithm of a hybrid hybridization functional and a defect quantum chemical correction factor to calculate the electron structure properties around atoms corresponding to the stable model, and when the electron structure properties are consistent with experimental structured defect information fitting, determining the model as an accurate ultra-wide forbidden band semiconductor material model at the moment. By means of the method, accurate digital modeling, material optimization growth and device optimization design reference of semiconductor materials adopted for actual device preparation can be achieved.

The invention discloses an interactive catalysis method and platform based on molecular simulation and artificial intelligence. According to the method, intelligent design of a catalyst is achieved through data classification, condition optimization, structure modeling and mechanism analysis. Catalytic data uploaded by a user is subjected to standardization processing and then is divided into two types: a data set A is used for recommending an optimal reaction condition by a multi-objective optimization algorithm; a three-dimensional model is constructed after a catalyst structure of the data set B is optimized through quantum chemistry software, a plurality of intermediates and transition state structures are optimized, and geometric / electronic descriptors are calculated. And constructing a dual-model collaborative prediction system of the model E and the model F through a feature screening and dimension reduction technology, and finally screening the high-performance catalyst by utilizing a model F driven molecule generation algorithm. According to the method, flexible adaptation to specific application scenes can be achieved, the urgent requirements of the industry for efficient and accurate catalyst performance prediction and intelligent design are met, cost reduction and efficiency improvement of the industry are effectively assisted, and the research and development process of the catalyst is accelerated.

The invention relates to an efficient energy-saving control method for industrial furnace complete equipment, in particular to the field of industrial furnace complete equipment, and successfully realizes remarkable improvement of the energy efficiency of an industrial furnace through coupling four key technologies of high-frequency laser spectrum entropy weighting, quantum chemical combustion mechanism modeling, reinforcement learning dynamic optimization and digital twin closed-loop correction. According to the scheme, the traditional thermodynamic limit is broken through, the feeding transient response speed is increased, CO emission is effectively reduced, and in actual measurement of the aluminum smelting furnace, the problems of gas waste and temperature fluctuation caused by imbalance of the air-fuel ratio are successfully solved, so that an energy-saving closed loop from a microreaction mechanism to macroscopic system control is formed, and the energy-saving effect is achieved. And the combustion efficiency and the system stability are greatly improved.

The invention aims to provide a method and equipment for constructing a machine learning force field for a biological system, a medium and a program product, and the method comprises the steps: carrying out the molecular dynamics simulation based on a first biological system, and obtaining a plurality of pieces of corresponding frame information; determining a plurality of pieces of structure information corresponding to each piece of frame information by using a molecular cutting algorithm; calculating system property information corresponding to each piece of structural information by using a quantum chemistry method; based on the system property information corresponding to each piece of structure information, determining a corresponding first sample data set; and training and obtaining a first machine learning force field by using the first sample data set. The research simulation of the biological system is carried out through the first machine learning force field, and the calculation efficiency can be improved while the calculation precision is guaranteed. Moreover, a molecular cutting algorithm is adopted to obtain a large amount of structural information for each frame of information to form training data, so that the generalization ability and prediction precision of a machine learning force field are further improved.

The invention relates to a chemical reaction path prediction method and system. The method comprises the following steps: defining a reaction pair containing reactants and potential products; performing combined optimization treatment on the reactants and the potential products by adopting a molecular dynamics method and a quantum chemistry method to generate an initial reactant configuration and an initial product configuration; based on the aligned initial reactant configuration and initial product configuration, determining initial reaction path information connecting the initial reactant configuration and the initial product configuration by adopting a quantum chemistry method; the invention provides a chemical reaction path prediction method and a chemical reaction path prediction system using the method, which are used for positioning and verifying accurate transition states of reactants and potential products based on initial reaction path information by adopting a quantum chemistry method, and can improve the initial guess quality for high-precision calculation and improve the accuracy of the chemical reaction path prediction. Therefore, the convergence success rate and accuracy of the transition state search are greatly improved, and the overall calculation cost is further reduced on the premise of ensuring the prediction precision.

The application discloses a kind of method, device and medium for calculating the energy of target system using quantum chemistry, method includes: first determining target system information, target system information includes the Hamiltonian of target system, electronic information and electronic spin-orbit information, based on electronic information and electronic spin-orbit information, obtain the Hartree Fock state of fermion form of target system, by pre-setting coding mode, the Hartree Fock state of fermion form is encoded to qubit, and the quantum state of Pauli operator form is obtained, finally, according to the Hamiltonian of target system, and based on the quantum state of Pauli operator form and evolution wave function, the energy of target system is calculated, it can support the realization of quantum chemistry simulation to calculate the energy of target system, improve the calculation speed and calculation accuracy, promote the further development of quantum chemistry simulation application.

This invention provides a method and system for intelligent directional aging of baijiu (Chinese liquor) based on quantum sensing and multi-physics field synergistic regulation. The method includes: S1, using quantum sensors to detect dynamic information at the molecular scale of the liquor; S2, intelligent decision-making and flavor path planning reaction pathways based on artificial intelligence and quantum chemical simulation; S3, applying a preset energy excitation to the liquor through multi-physics field synergistic regulation to guide the aging process. This invention transforms "passive aging" into "active brewing": from an uncontrollable natural process to a designable, programmable, intelligent production process. By precisely lowering the target reaction energy barrier, the formation cycle of core flavor substances is shortened from "years" to "months" or even "weeks," achieving an order-of-magnitude efficiency improvement and breaking down the "time barrier." Based on market demand, products with different flavor emphases can be produced by adjusting the "target flavor fingerprint" and corresponding field regulation "prescription."

The application discloses a kind of based on machine learning prediction amide bond molecular synthesis conversion rate method, belong to chemical technical field.The application is based on high-throughput experiment collection reaction conversion rate data, establishes data set, and utilizes quantum chemistry calculation means to extract the feature descriptor of reaction related molecule, and the model after training finds that random forest model only needs a small amount of data to reach good prediction accuracy.Through the prediction of unknown aromatic amine molecular conversion rate in amidation reaction, it is found that a small amount of relevant reaction data can be added in the training set of random forest model to significantly improve the prediction performance of unfamiliar data.This has important guiding significance for exploring the synthesis of amide bond and the future application of machine learning to the field of chemical synthesis.

The invention discloses a quantum chemical screening method for extracting arbutin, and relates to the technical field of natural product extraction, and the quantum chemical screening method comprises the following steps: constructing an arbutin single-molecule model and a hydrogen bond donor and hydrogen bond receptor single-molecule model by using GaussView software, firstly performing conformation search through MOPAC and molplus programs to obtain a most stable molecular structure, and then extracting the arbutin from the molecular structure; carrying out geometric optimization and single-point energy calculation by utilizing Gaussian; the optimized hydrogen bond donor and hydrogen bond acceptor establish random clusters according to different molar ratios, the random clusters with target molecules are established, a cluster model with an optimal structure is selected for geometric optimization and single-point energy calculation, meanwhile, the binding energy between the eutectic solvent and arbutin is calculated, and IGMH analysis, ESP analysis and AIM analysis are carried out. The invention provides a universal method for green and efficient extraction of active ingredients of natural products.

The invention relates to the technical field of electrical data processing, and provides an anthocyanin stabilizer screening method and system based on fusion of quantum chemistry calculation and machine learning. The method comprises the following steps: constructing a molecular structure data set; performing geometric structure optimization on the anthocyanin molecules and the candidate polyphenol molecules to obtain a ground state optimization structure; performing multi-dimensional molecule descriptor calculation on the candidate polyphenol molecules to obtain a polyphenol molecule descriptor matrix; performing structure optimization, single-point energy calculation and free energy calculation on the anthocyanin polyphenol compound system to obtain a combined free energy value set; integrating the polyphenol molecule descriptor matrix and the combination free energy value set, constructing a training data set, and performing feature screening; performing model training through a machine learning algorithm to obtain a combined free energy prediction model; and predicting the binding free energy of the anthocyanin polyphenol compound through the binding free energy prediction model to obtain candidate molecules of the anthocyanin stabilizer. The screening efficiency of the anthocyanin stabilizer is improved.

The application discloses a kind of multi-element doped system simulation methods for improving the performance of 2:17 type samarium-cobalt permanent magnet, comprising: step one, using the Visualizer module of material calculation software to establish the theoretical model of 2:17 type rare earth cobalt permanent magnet material;Step two, based on the GO module of quantum chemistry calculation software, the theoretical model of different doped systems is analyzed, and then the system with stronger magnetism is found;Based on the quantum chemistry calculation software, the doped model and virtual crystal approximation model of different doped ratios are established to obtain the better doped ratio by comparison.The application discloses a kind of multi-element doped system simulation methods for improving the performance of 2:17 type samarium-cobalt permanent magnet, the theoretical research of the characteristics of rare earth cobalt permanent magnet material itself and doping characteristics is carried out through the first principle, through the implementation of the project, the theoretical analysis of rare earth cobalt permanent magnet material is formed, which provides design idea for developing rare earth cobalt permanent magnet material with better system performance.

The invention belongs to the technical field of computational chemistry and artificial intelligence crossing, and provides a molecular potential energy surface prediction method based on deep learning. The method comprises the following steps: inputting a structure file of an unoptimized molecule, and reading and predicting energy and force of a current molecular structure by utilizing a machine learning potential model; and then, judging whether the structure is converged or not according to a prediction result by adopting a GeomeTRIC algorithm, and if not, adjusting coordinates to generate a new structure and performing circular prediction until the structure is converged. According to the method, the quantum chemistry and the deep learning technology are combined, the calculation efficiency can be greatly improved, complex inter-atomic interaction in molecules can be fully captured, and the prediction accuracy is ensured; and meanwhile, batch processing and automatic generation of a quantum chemistry software interface are supported, and the method is particularly suitable for high-throughput molecular structure optimization in the fields of drug discovery, material science and chemical product design.

The present invention relates to the fields of chemistry and materials science, and discloses a visualization system for large-scale quantum chemistry, which calculates the energy information of each structure in the calculation system, and finds the minimum-energy structure in the system as the optimized structure by means of gradient descent, and displays the structures before and after optimization in the form of an atomic coordinate matrix for easy visualization; at the same time, it can also obtain information such as charge density, differential charge density tensor, difference tensor, etc. according to the energy information of the structure, and draw visual images by using these information.

The parameterized quantum circuit construction method, device, storage medium and computer device provided by the application utilize unitary matrix linear combination to reduce the depth and required quantum bit number of a variational circuit, thereby effectively constructing an arbitrary target quantum state, reducing the amount of training parameters, and optimizing the efficiency and accuracy of the variational quantum algorithm and improving the practicability of the algorithm under limited quantum resources; and the application is particularly suitable for a noisy intermediate-scale quantum (NISQ) device, and by proposing a circuit design with low depth and quantum bit number, the problem that the application of the prior art on the NISQ device is limited can be solved, for example, the application can efficiently solve VQA problems such as quantum state learning and Hamiltonian ground state solving on a quantum device, thereby opening up a new way for practical applications in the field of quantum chemistry.

The invention discloses a method for evaluating photoelectric conversion efficiency of an organic solar cell material. The method comprises the following steps: firstly, acquiring the stacking morphology of a material by utilizing molecular dynamics simulation and performing molecular slicing; then, constructing a multi-dimensional geometric feature matrix containing internal deformation and external relative positions of characterization fragments and angle features based on a centroid fitting plane; performing dimension reduction processing on the feature matrix by adopting principal component analysis, extracting principal component features reflecting deformation and spatial distance, and performing morphological clustering according to the principal component features to screen effective clustering morphologies; and finally, in combination with the electron coupling value calculated by quantum chemistry and the corrected equivalent distance based on the principal component, establishing a mapping relationship to evaluate the material efficiency. According to the method, by introducing the centroid weight and the deformation parameters, geometric representation errors existing when non-planar and flexible organic molecules are processed through a traditional method are effectively solved, and the prediction precision and robustness of novel organic photovoltaic material screening are remarkably improved.

The invention belongs to the technical field of material information, and particularly relates to a quantum chemistry calculation result intelligent analysis and error correction system and method. The calculation result analysis module is in communication connection with the data preprocessing module and is used for analyzing the standardized data based on a preset rule; the machine learning analysis module is in communication connection with the calculation result analysis module, receives an analysis result sent by the calculation result analysis module, and performs deep analysis on the analysis result based on a machine learning algorithm; and the error correction module is in communication connection with the calculation result analysis module and the machine learning analysis module and is used for performing error correction on the quantum chemistry calculation result based on the analysis result and the analysis result, so that the overall analysis efficiency is improved, the analysis result is continuously optimized, and the practical value is remarkable.

The present invention relates to the field of substance detection technology, and more particularly to a near-infrared fluorescent probe and its application in the detection of aflatoxin B1. In the present invention, quantum chemical calculation software and molecular dynamics simulation are used to guide the synthesis of near-infrared nanoparticles, and near-infrared nanoparticles are prepared by a microemulsion method. The near-infrared nanoparticles have uniform particle size, good stability, and can emit ultra-high brightness fluorescent signals. By coupling the near-infrared nanoparticles with anti-aflatoxin B1 monoclonal antibodies, they can be used as near-infrared fluorescent signal probes for the detection of aflatoxin B1. Because the near-infrared nanoparticles can achieve short-wave excitation and have a large Stokes shift, they can effectively eliminate interference between excitation light and emitted light, avoiding autofluorescence disturbances. The near-infrared fluorescent signal probe prepared therefrom can significantly improve detection sensitivity.

The invention provides a quantum storage structure optimization method for a plane wave basis group Hamiltonian, relates to the field of quantum calculation and quantum chemical simulation, and aims to solve storage and calculation challenges caused by huge scale of a Hamiltonian matrix in quantum calculation. Through structural analysis, double-layer index coding and structured QROM module construction, efficient loading and dynamic calling are achieved. The quantum random access memory routing module supports sub-channel loading, path compression reduces the number of control gates, and resource efficiency and flexibility are improved. The method is suitable for large-scale periodic material simulation, such as crystal structure optimization and electronic state analysis, is in seamless connection with a mainstream quantum algorithm, and remarkably reduces the circuit depth and auxiliary bit requirements.

The present invention discloses a method and system suitable for data mining of energetic compounds, belonging to the technical field of structure-activity relationship of energetic compounds in chemical informatics calculation, comprising the steps of: S1, data preprocessing; S2, extracting molecular topological structure features and features of quantum chemical calculation to generate fingerprint features; S3, calculating Pearson correlation and information entropy, screening out linearly correlated and non-correlated features, performing feature dimensionality reduction, and deleting redundant features whose linear correlation between features is higher than a set value; S4, knowledge mining and model verification, exploring the correlation and knowledge association between feature libraries. The present invention can classify data of energetic materials according to skeletons and groups, mine multi-dimensional linear features and non-linear features, embed more features based on chemical expertise into the graph structure model training process, and utilize the data mining engine of the present invention to screen out strongly correlated molecular features, which can make up for the shortcomings of existing models.