12 results about "Chemical computing" patented technology

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.

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.

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.

To provide a computer program, an information processing device, an information processing method, and a learning model generation method capable of performing calculation of interaction between molecules faster than quantum chemical calculation.SOLUTION: The computer program causes a computer to execute a process of acquiring a feature value of a complex and a molecule contained in the complex, acquiring a molecular formation energy of the complex and the molecule contained in the complex output by a learning model by inputting the acquired feature value to the learning model that outputs the molecular formation energy of the complex and the molecule contained in the complex when the feature value of the complex and the molecule contained in the complex is input, and estimating an interaction energy between the molecules contained in the complex based on the acquired molecular formation energy.SELECTED DRAWING: Figure 1

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.

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 provides a hybrid computing task processing method and device and related equipment, and relates to the technical field of quantum computing, and the method comprises the steps: carrying out the grouping of Fouli items of a target molecule based on the opinion relationship between Fouli operator strings, and obtaining at least two measurement item groups; packaging each measurement item group into a quantum measurement task; constructing a directed acyclic graph (DAG) on the basis of iterative logic of a variable component child solicitation solver (VQE) algorithm; and in the process of carrying out the classic calculation task, obtaining a quantum measurement task of a next iteration round based on the DAG, and compiling a corresponding quantum circuit. The assembly line work of quantum and classical tasks is realized, and the quantum and classical computing resource utilization rate is improved. The method can be applied to chemical calculation scenes such as drug discovery and material design.

The application discloses a heavy gas turbine combustor reaction simulation acceleration method based on sparse dynamics, wherein the method comprises the following steps: dynamically adjusting the intensity of the heat release source term based on the difference between the CFD unit temperature and the corresponding reactor temperature, and eliminating the error caused by the uniform distribution assumption of the heat release through feedback adjustment; constructing a sparse reaction network containing multiple reactor nodes based on the Cantera toolkit; based on the sparse reaction network, real-time evaluation of the reaction intensity in the CFD area where the reactor unit is located is carried out, only for the high active area, the complete PSR reactor unit is activated to perform complete chemical calculation, for the low active and non-active area, the reduced mechanism or the frozen treatment is adopted; dynamically distributing the activated reactor unit to independent computing threads and GPU task blocks, automatically performing cross-scale load balancing adjustment according to the time step, and synchronously maintaining the two-way boundary coupling between the reactor and the CFD, so as to ensure the dynamic balance of communication efficiency and calculation stability.

A computer-readable recording medium stores therein a number-of-loops adjustment program for causing a computer to execute a process including: determining a maximum number-of-loops when a quantum chemical calculation using a configuration interaction method is executed for a substance under analysis, the maximum number-of-loops being determined based on: a first energy value obtained by the quantum chemical calculation using a first coupled cluster method for the substance under analysis, a second energy value obtained by the quantum chemical calculation using a second coupled cluster method having a different maximum excitation count from the first coupled cluster method for the substance under analysis, and a number-of-loops of the first coupled cluster method when the first energy value is obtained.

This invention relates to the interdisciplinary field of materials computation and spectroscopic analysis. To address the problems of low experimental throughput, high computational cost, and difficulties in analysis caused by a lack of standard spectral libraries in existing technologies, it provides a Mössbauer spectral research method that combines theoretical calculation with data-driven approaches. This invention obtains and predicts candidate crystal structures from a historical crystal structure database, performs thermodynamic stability screening and all-electron quantum chemical calculations to acquire their Mössbauer spectral parameters, and then uses a symbolic regression algorithm to construct a predictive model between structural features and spectroscopic parameters after structural feature extraction. This enables second-level spectral parameter prediction, expands the theoretical standard library, and is used for accurate analysis of experimental spectra. This invention significantly improves prediction efficiency and analytical accuracy, and provides physical insights for materials design.

The invention provides a monomolecular conductivity and spectral property prediction method and device, electronic equipment and a storage medium, and relates to the technical field of molecular property prediction. The method comprises the following steps: acquiring structural data of a molecule to be predicted, and converting the structural data into a molecular map; extracting an atomic feature vector and a key feature vector, and performing feature learning on the molecular map by using a directed message passing neural network; calculating a feature weight by adopting an attention mechanism, and performing weighted aggregation on the updated atomic feature vector to obtain a molecular-level feature embedding vector; and outputting a single-molecule conductivity predicted value and a spectral characteristic parameter according to the molecular-level characteristic embedding vector. The method aims to solve the problems of complex experimental measurement operation, high cost, long period, high quantum chemistry calculation complexity, long calculation time and the like in a single molecule conductivity and spectral property acquisition mode in the prior art, and realizes efficient, accurate and collaborative prediction of single molecule conductivity and spectral properties.

The invention relates to a computer implemented method (100) for computing quantum-enhanced chemical property of a chemical system comprising the following steps: - Providing a Hamiltonian (120) of the chemical system subject of the quantum-enhanced chemical computing; - Providing a basis set (130) for the chemical system; - Preparing an initial quantum state (140), on the quantum computation means (10), to represent the chemical system according to the Hamiltonian and using the provided basis set; - Applying, on the quantum computation means (10), a quantum solver (150) on the prepared quantum state in the basis set; and - Computing, on classical computation means (40), a density-based basis-set correction (160) to modify the density-dependent terms in the Hamiltonian