67 results about "Quantum chemistry" patented technology

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 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 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 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 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 application belongs to the field of chemical thermodynamics, and particularly relates to a thermodynamic model for predicting the thermodynamic properties of a salt-solvent system. The model first obtains the coordination number n of cations and anions in the salt-solvent system through molecular dynamics simulation, then obtains the initial configuration of the cation-n solvent molecule-anion through configuration optimization, and obtains the most stable structure through quantum chemistry calculation program optimization, and finally obtains the VLE (vapor-liquid equilibrium) data prediction value of the salt-solvent system based on the COSMO-RS theory. The model can obtain the prediction value with high accuracy without experiment, thereby saving a large amount of manpower, material resources and financial resources. Meanwhile, the present application also discloses the phenomenon that the salt in the salt-solvent system is volatile.

The invention belongs to the field of Po-210 aerosol characteristic calculation, and particularly relates to a Po-210 aerosol particle characteristic and decay kinetics calculation method. The method comprises the following steps: S1, determining an optimal theoretical method; s2, constructing an initial molecular structure of one or more candidate Po species to be researched, and completing construction of the initial molecular structure; s3, geometric structure optimization and frequency calculation are carried out; s4, a Lennard-JonesLJ potential parameter is calculated, and the Lennard-JonesLJ potential parameter is calculated; s5, determining thermodynamic properties of the candidate chemical species based on the LJ potential parameters obtained in the step 4; and S6, calculating to obtain the characteristics of the Po-210 aerosol. Based on quantum chemistry, molecular dynamics and a Monte Carlo method, particle nucleation, collision and combination mechanisms and decay dynamics of Po-210 in a gas space are disclosed, key mechanism support is provided for predicting environment evolution behaviors and designing source foot control strategies, and a feasible calculation normal form is provided for predicting cross-scale behaviors of highly toxic radioactive substances.

The application discloses an interactive catalytic method and platform based on molecular simulation and artificial intelligence, and realizes intelligent design of a catalyst through data classification, condition optimization, structure modeling and mechanism analysis. After being standardized, the catalytic data uploaded by a user is divided into two categories: data set A is used for recommending optimal reaction conditions by a multi-objective optimization algorithm; and the catalyst structure of data set B is optimized by quantum chemistry software to construct a three-dimensional model, and multiple intermediate, transition state structures and geometric / electronic descriptors are optimized. Through feature screening and dimension reduction technology, a double-model collaborative prediction system of model E and model F is constructed, and finally, model F is used to drive a molecular generation algorithm to screen high-performance catalysts. The application can realize flexible adaptation to specific application scenarios, meet the urgent needs of the industry for efficient and accurate catalyst performance prediction and intelligent design, and effectively help the industry to reduce costs and increase efficiency, and accelerate the catalyst research and development process.

An apparatus and method are provided for optimizing the use of gas hydrate inhibitors by combining real-time "dew point" measurement with quantum-chemical phase-change analysis. A gas hydrate inhibitor reduces gas moisture; by measuring gas humidity and identifying the "dew type" using two chilled-mirror sensors each equipped with an imaging camera, a "dew coefficient" (0–1) is calculated via quantum chemistry analysis of inhibitor solvation. This mesoscale coefficient is used to monitor fluid phase changes. A general relationship for the water / hydrocarbon "dew coefficient" is derived as a function of changes in Gibbs free energy of dew evaporation (inhibitor solvation), gas temperature and pressure, and gas / liquid density. By combining measured "dew point" and "dew type" with quantum chemistry data of phase transformations "dew coefficient", the optimal inhibitor injection rate is determined, minimizing inhibitor consumption while preventing hydrate formation.

The present application relates to the technical field of electric data processing, and provides a screening method and system for anthocyanin stabilizer based on quantum chemistry calculation and machine learning fusion. The method comprises the following steps: constructing a molecular structure dataset; performing geometric structure optimization on anthocyanin molecules and candidate polyphenol molecules to obtain ground state optimized structures; performing multidimensional molecular 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 complex system to obtain a binding free energy numerical set; integrating the polyphenol molecule descriptor matrix and the binding free energy numerical set, constructing a training dataset and performing feature screening; performing model training through a machine learning algorithm to obtain a binding free energy prediction model; and predicting the binding free energy of the anthocyanin polyphenol complex through the binding free energy prediction model to obtain candidate molecules for anthocyanin stabilizer. The present application improves the screening efficiency of anthocyanin stabilizer.

Predict the retention time of the target substance obtained by high-performance liquid chromatography. [Solution] In the retention time prediction method, a parametric function is created using a linear combination of the partition coefficients between the mobile phase and multiple stationary phase models. For multiple standard substances, the partition coefficients between the mobile phase and multiple stationary phase models are calculated by quantum chemical calculations using molecular structure information. Using the retention times and partition coefficients of the multiple standard substances, the parameters of the partition coefficients in the linear combination of the parametric function are determined by linear regression. A specific function is created by substituting the parameters into the parametric function. For the target substance, the partition coefficients between the mobile phase and multiple stationary phase models are calculated by quantum chemical calculations using molecular structure information. Using the specific function, the retention time is calculated from the partition coefficients for the target substance.

The present application belongs to the field of computational chemistry and molecular simulation technology, and relates to a kind of aromatic eutectic solvent multiscale molecular simulation screening method and system based on multi-objective collaborative scoring model.Establish target molecule model library and eutectic solvent candidate component library;Quantum chemistry calculation is carried out on target molecules and eutectic solvent candidate system, and quantum chemistry descriptors are extracted;Molecular dynamics simulation system is constructed, and molecular dynamics descriptors such as aromatic space enrichment coefficient Kπ, glycosidic bond interaction index Ggly and lignin bond type affinity index Lβ are extracted;Multi-scale feature vectors are obtained by normalization processing, input into multi-objective collaborative scoring model, calculate and extract score SE, grading score SF, and judge and output active ingredient extraction priority, lignocellulose grading priority or segmented collaborative processing mode.The present application is suitable for eutectic solvent calculation screening and application mode discrimination when there is a target conflict between plant aromatic active ingredient extraction and lignocellulose grading treatment.

Aspects of the present disclosure describe a method including predicting a first set of ansatz terms and a first plurality of amplitudes associated with the first set of ansatz terms; minimizing energy of the system based on the first set of ansatz terms and the first plurality of amplitudes; computing perturbative corrections using one or more ansatz wavefunctions; determining whether energy of the system converges; and predicting, in response to determining that the energy of the system does not converge, a second set of ansatz terms and a second plurality of amplitudes associated with the second set of ansatz terms.

This invention discloses a linear scaling quantum chemistry calculation method, its applications, and related products based on pre-constrained energy. Belonging to the field of quantum chemistry calculation technology, it solves the core technical pain points of existing technologies, such as the irreconcilable trade-off between computational complexity and accuracy, lack of underlying physical consistency guarantees, and poor adaptability to complex systems. This invention uses the inherent kinetic-potential coupling relationship derived from the generalized virial theorem as a pre-priority hard constraint. Through a core process involving pre-determining inherent constraint parameters, pre-locking physical orbits, effective particle pair screening calculations, and direct calculation of total energy, it completely abandons self-consistent field iteration and Hamiltonian matrix diagonalization operations, achieving a fundamentally rigorous O(N) linear scaling. The calculation results of this invention naturally conform to the basic rules of quantum mechanics, have no approximate truncation error, and are universally applicable to all systems and quantum states. It can perform high-throughput quantum chemistry calculations for large-scale industrial systems on ordinary consumer-grade hardware, and can be widely applied in fields such as drug development, new material design, semiconductor simulation, and superconducting material screening.

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 invention discloses a full-atomic potential function data set construction method and device, and relates to the field of molecular dynamics simulation. The method comprises the following steps: firstly, determining the type of a system for constructing a full-atomic potential function; generating an initial structure library according to the system type; sampling from the initial structure library through a molecular dynamics strategy or a random surface walking strategy to obtain an initial sampling structure, randomly selecting a set structure from the initial sampling structure, and calculating corresponding energy through a quantum chemistry strategy to form an initial data set; for each structure in the initial data set, counting the inter-atomic distance, bond angle and dihedral angle distribution of the structure; then fitting a potential function and sampling a new structure, calculating a structure score according to the three distributions, and selecting a structure with a high score to supplement the initial data set; and recalculating three new distributions, and carrying out iterative updating until the distributions have no vacancy, thereby obtaining a full-atom potential function data set. The potential function simulation stability can be improved, potential function development is accelerated, and molecular system research is assisted.

This invention relates to the field of combustion dynamics technology, and provides a method for modeling the combustion dynamics of nano-aluminum particles based on molecular reaction dynamics. The method includes: S1 constructing a nano-aluminum particle model; S2 establishing an oxidation combustion reaction system; S3 analyzing molecular trajectories; S4 extracting reaction paths; S5 determining gas-phase reaction kinetic parameters; S6 determining surface reaction kinetic parameters; S7 constructing a heterogeneous combustion kinetic mechanism; and S8 verifying and outputting simulation results. This invention directly extracts reaction paths through molecular dynamic trajectories in the reaction force field, reducing the subjectivity of relying entirely on artificially preset elementary reactions. It supplements key gas-phase and surface reaction parameters through quantum chemical calculations and density functional theory calculations, improving the physicochemical basis of the mechanism parameters. By coupling gas-phase and surface reactions into a heterogeneous combustion kinetic mechanism, it more completely describes the structural evolution process of aluminum nanoparticles from melting, rapid oxidation, partial oxidation to equilibrium.

The invention discloses a simulation method for analyzing microscopic behavior properties of an alcohol-water system in a porous carbon material, which is characterized by comprising the following steps of: 1, constructing and optimizing a model; 2, a porous carbon model characterization step; 3, constructing an ethanol-water system model; 4, an ethanol-water-porous carbon model combination step; 5, a dynamic simulation calculation step; and step 6, result statistics and analysis. The microcosmic influence mechanism of the porous carbon material on the alcohol-water system is analyzed by utilizing computer simulation and quantum chemistry theories, and the provided simulation analysis method can provide theoretical support and mechanism interpretation for subsequent optimization of an alcoholic beverage processing technology.

The application discloses a research method for interaction between a new pollutant and DNA, and the method comprises the following steps: using a DFT method in quantum chemistry calculation software to optimize the molecular structure of the new pollutant and deoxy nucleoside; using Multiwfn software to obtain the contribution of each atom to a molecular orbital and atomic charge distribution; finding an electrophilic site of the new pollutant and a nucleophilic site of the deoxy nucleoside; using molecular docking software to perform docking calculation on the new pollutant and a double-stranded DNA fragment and search for an optimal combination position; using a UV-Vis ultraviolet spectrophotometer to perform ultraviolet spectrum titration on the interaction between the new pollutant and the double-stranded DNA; using a super high performance liquid chromatography-mass spectrometry method to perform gradient elution, detecting the deoxy nucleoside through a multiple reaction detection method, calculating a reaction rate, and identifying a deoxyribose unit prone to adduct reaction with the new pollutant; and analyzing the detection result.

An apparatus and method are provided for optimizing the use of gas hydrate inhibitors by combining real-time "dew point" measurement with quantum-chemical phase-change analysis. A gas hydrate inhibitor reduces gas moisture; by measuring gas humidity and identifying the "dew type" using two chilled-mirror sensors each equipped with an imaging camera, a "dew coefficient" (0–1) is calculated via quantum chemistry analysis of inhibitor solvation. This mesoscale coefficient is used to monitor fluid phase changes. A general relationship for the water / hydrocarbon "dew coefficient" is derived as a function of changes in Gibbs free energy of dew evaporation (inhibitor solvation), gas temperature and pressure, and gas / liquid density. By combining measured "dew point" and "dew type" with quantum chemistry data of phase transformations "dew coefficient", the optimal inhibitor injection rate is determined, minimizing inhibitor consumption while preventing hydrate formation.

This invention discloses a multi-agent fusion training-free quantum architecture search method for noisy environments, belonging to the field of quantum architecture search technology. Addressing the problems of high noise interference and extremely high training costs for architecture search in the NISQ era, this invention proposes a two-stage training-free screening scheme: the first stage constructs a directed acyclic graph of quantum circuits and uses the path count feature in the circuit structure to quickly coarsely screen low-potential circuits; the second stage integrates four training-free agent indicators—expressive power, trainability, noise perception, and information theory capability—to perform multi-dimensional refined evaluation of candidate circuits. Through a ranking aggregation strategy and Pareto dominance mechanism, it accurately identifies high-fidelity, robust quantum circuit structures in noisy environments without parameter training. This invention performs excellently in quantum chemical simulation and classification tasks, significantly reducing computational resource consumption and improving search efficiency.

The invention relates to the technical field of steel production, in particular to a steelmaking refining slag system calculation model, based on the first principle of quantum chemistry, a CaO-SiO interface reaction energy barrier is obtained through DFT calculation by using VASP software, a surface reaction kinetic model is constructed, and a molten pool heat transfer and desulfurization reaction rate equation is fused. According to the method, an improved NSGA-II algorithm is adopted for multi-target collaborative optimization, 12 decision variable parameters are subjected to linkage control through a dynamic coupling mechanism, the slag alkalinity R2 is precisely regulated and controlled, and the slag performance is ensured; molten pool stirring can promote reaction and inclusion removal, and the model can simulate the physical process of molten steel and slag and predict internal state distribution; compared with the prior art, the model can accurately and efficiently optimize the steelmaking refining process, collaborative optimization of molten steel components, temperature and desulfurization efficiency is achieved, the production efficiency and the product quality are improved, and the model has excellent flame-retardant and heat-insulating performance, good structural stability and strong ecological restoration capacity and also gives consideration to environmental protection and resource utilization.

The invention relates to the technical field of analytical chemistry, and discloses a component detection method suitable for herbaceous plant essence, and the method comprises the following steps: simulating a theoretical Raman spectrum of a candidate compound by using a quantum chemistry calculation module to construct a database; collecting the actually measured Raman spectrum of the sample to be detected; spectral deep features are extracted through a convolutional neural network model and matched and compared; and outputting an identification result based on dual verification of a matching degree threshold value and a feature similarity threshold value of the theoretical vibration peak position and the actually measured spectrum peak position. The method provided by the invention can solve the problem of insufficient discrimination of isomers and stereoisomers with similar structures in the prior art, and realizes accurate identification of structural analogues in a complex system without complete separation.

The goal is to automate the determination of the active space, which is useful for quantum chemical calculations. [Solution] The active space determination program obtains occupancy data, which includes data showing the time-series changes in the number of electrons occupied in each of multiple molecular orbitals. Using the occupancy data as input, the program calculates the solution to a mathematical optimization model formulated with constraints that keep the sum of the number of electrons occupied in multiple molecular orbitals constant, and an objective function that gives a relatively higher evaluation to combinations of molecular orbitals whose time-series changes in the number of electrons occupied are negatively correlated with combinations whose time-series changes in the number of electrons occupied when selecting a subset from multiple molecular orbitals to be used in quantum chemical calculations, compared to combinations whose time-series changes in the number of electrons occupied are positively correlated. The program then has the computer execute the calculation and output the result.