36 results about "Potential energy surface" patented technology

The invention relates to an intelligent state monitoring method and system for a high-voltage isolation switch operating mechanism, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: enabling a collected real-time sequence to update a standard operation response potential energy surface, obtaining a real-time operation response potential energy surface, generating a phase response difference tensor through data projection, and obtaining a real-time operation response potential energy surface; and depicting deviation characteristics of actual and standard operation states. And then, in the constructed graph attention network, on the basis of a real-time sequence, monitoring parameters and video motion characteristics, constructing a multi-modal sequence state graph, and determining a state typing result through fine-grained recognition and interpretable analysis of a fault. And finally, introducing a cyclic fingerprint residual model, performing clustering optimization based on a real-time sequence, determining an abnormal deviation index, and combining the abnormal deviation index with a state typing result to determine a fault diagnosis result. Therefore, the accuracy of intelligent state monitoring of the high-voltage isolation switch operating mechanism can be effectively improved, and the method can be suitable for non-ideal environments, low dominant anomalies and other conditions.

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 invention relates to the technical field of computational chemistry and drug screening, and discloses a fluorinated derivative screening method based on computer-aided design, which comprises the following steps: acquiring an initial binding conformation of fluorine-containing molecules and a target system, and constructing a follow-up local orthogonal coordinate system by taking the axial direction of a fluorocarbon bond as a reference; projection coordinates of the environmental particles relative to the fluorocarbon bonds are calculated and mapped as a spherical coordinate index, a preset anisotropic potential energy surface grid is called, and a potential energy value is calculated through interpolation; the method comprises the following steps: generating an anisotropic correction term, monitoring the modulus length of the energy gradient in real time, and when the modulus length of the energy gradient exceeds a sensitive threshold value, adaptively and nonlinearly reducing a sampling step length and executing high-frequency local sampling in a sensitive area. The problem of false negative activity prediction caused by insufficient sampling when a traditional equal-step algorithm is used for processing anisotropic distribution of fluorine atom electron clouds is solved, and the recognition precision and screening efficiency for weak directional interaction such as halogen bonds are improved.

This invention discloses a multi-objective adaptive control method for a power generation, grid, load, and storage system in an industrial park, relating to the field of energy management technology. The method first constructs a generalized state space of physical equipment and virtualized production tasks, mapping the production tasks to a virtual energy storage model and constructing a congestion risk potential surface function. Then, it calculates and decomposes the total regret value of the strategy, dynamically updates the objective function weights based on external regrets, and corrects the virtual model parameters online based on internal regrets. Subsequently, it constructs a collaborative optimization proposition using the updated weights and parameters, incorporating congestion risk into the optimization objective, and solves for the optimal control sequence. Finally, it decouples the control sequence into physical charging / discharging commands and production operation commands. This invention achieves compatibility between discrete production and continuous energy control through virtualization mapping and utilizes a two-layer regret mechanism to achieve adaptive adjustment of strategies and parameters, improving the system's economy and robustness.

The invention discloses a tug system maintenance operation risk supervision method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: collecting multi-physical-quantity data, accessing the Internet of Things, carrying out the preprocessing of original data, and generating standard input; inputting the preprocessed data to a coupling resolver, capturing characteristics among physical quantities, and dividing a coupling state; generating risk factors based on abnormal coupling, and constructing a first safety potential energy surface and a second safety potential energy surface; comparing the two types of potential energy surface data, and outputting execution admission judgment of the maintenance step; and collecting the track of the maintenance personnel, comparing with the path template, and triggering the behavior lock if deviation occurs. According to the invention, through a cooperation mechanism of multi-physical quantity coupling analysis, potential energy surface admission determination and personnel trajectory supervision, real-time risk identification and safe controllable execution of the whole tug maintenance operation process are realized.

The invention discloses a method for searching a minimum energy path from a discrete potential energy surface and a related product, the method is applied to a discrete potential path optimizer, and the method comprises the steps that an energy coordinate data set in an input file is read, and each energy coordinate data in the energy coordinate data set comprises a three-dimensional coordinate and an energy value corresponding to the three-dimensional coordinate; the energy coordinate data set is simplified based on a preset energy cutoff value, a simplified energy coordinate data set is obtained, and the energy value corresponding to each three-dimensional coordinate in the simplified energy coordinate data set is smaller than or equal to the energy cutoff value; searching energy coordinate data corresponding to a minimum energy path from the simplified energy coordinate data set based on a path connection condition between a preset starting point coordinate and a preset end point coordinate; and sorting the energy coordinate data corresponding to the minimum energy path based on preset grid precision to obtain the minimum energy path corresponding to the discrete potential energy surface. Therefore, the minimum energy path can be searched from the discrete potential energy surface.

A machine learning potential energy surface model construction method comprises the following steps: collecting data for training a model, the data comprising data of one or more systems, the data of each system comprising atomic coordinates of the system and corresponding properties of the system; performing data inspection and data storage on the collected data; constructing a machine learning potential energy surface model, comprising: establishing a feature engineering; obtaining a final data set for training the model through the feature engineering; constructing a machine learning model; and training the machine learning model using the final data set to obtain the machine learning potential energy surface model. By using a data-driven method, the present application avoids manual feature extraction, and the model autonomously iterates learning from the data set, thereby avoiding the introduction of human bias. The model of the present application has very strong scalability, can be scaled to larger systems, and is not limited by the properties of the target system, and simultaneously supports multi-phase systems and periodic / non-periodic systems.

The application discloses a kind of optimization method and device for identifying Cu-Zn alloy cluster structure potential energy surface, comprising: randomly generating several Cu-Zn alloy clusters, constitute population structure library n pop ; develop shell script that can efficiently call Gaussian16 quantum chemistry software, carry out first local structure optimization to n pop ; innovative surface atom self-adapting reorganization strategy is proposed, iteratively move cluster surface atoms to self-adapting generated empty space sites, execute second stage structure optimization;Through crossover, variation operation, in combination with similarity detection and potential energy comparison, extract the optimal structure of binary Cu x Zn y Cluster.Using the technical scheme of the application, the local and global spatial search capability of genetic algorithm is optimized and improved, and the stability and electronic structure of Cu-Zn alloy cluster can be efficiently analyzed by calling quantum chemistry software.

The invention discloses a diacetylene topological chemical reaction method which comprises the following steps: spin-coating a diacetylene solution on a substrate to form a coating film; and irradiating the coating film by adopting an infrared light source, and reacting to generate polydiacetylene. Compared with side reaction and degradation caused by the fact that high-energy rays enable the material to enter an electron excitation state and material fragmentation caused by severe spatial change of molecules under high polymerization, mid-infrared light vibration excitation in the method serves as a means capable of triggering molecular vibration under an electron ground state, and under the mild condition, the mid-infrared light can be used as a means capable of triggering molecular vibration under the electron ground state. Therefore, various bad effects generated in the electron excitation state can be avoided.

This invention provides a method for the controllable synthesis of high-quality quantum dots based on pre-nucleated clusters that reach a minimum potential energy surface through chemical self-assembly, belonging to the field of semiconductor nanomaterials technology. The quantum dots of this invention are prepared by reacting a pre-nucleated sample of semiconductor material with pre-prepared quantum dots to obtain high-quality quantum dots grown heterogeneously (core-shell structure) or homogeneously. This method, based on the pre-nucleated clusters, allows for the control of quantum dot size growth, narrowing the quantum dot distribution and avoiding the need to control the bonding temperature of the semiconductor material. This method improves the accuracy of quantum dot size control, and the quantum dots prepared by this method also exhibit significant luminescent properties, effectively expanding the applications of quantum dots and showing great promise.

A method for training and subsequently executing a machine learning network of both an autoencoder and a machine learning model in the context of machine learning inter-atomic potential is disclosed. The systems described herein are configured to embed atomic locations and categories of a given atomic system, and apply them to an autoencoder to learn auxiliary attributes, and to a machine learning model to learn local energy. The auxiliary attributes are then used to generate an auxiliary Hamiltonian description. Attributes, such as the total energy of the atomic system, are determined by combining both the auxiliary Hamiltonian description and the local energy. By processing machine learning via both an autoencoder and a machine learning model, such methods ensure that both long range and short range effects are considered while also appropriately achieving realistic discontinuities and / or transitions within potential energy surfaces.

The invention belongs to the field of molecular simulation, and particularly relates to a method for constructing a reactive force field based on a multi-pole moment model. Comprising the following contents: a charge penetration effect, a polarization effect, a charge transfer effect, Van der Waals interaction and a three-body potential energy form suitable for a chemical reaction; a dynamic electric multi-pole moment method suitable for chemical reaction; the invention relates to a method for constructing a potential energy surface data set with six degrees of freedom. The invention provides a simple and effective force field model for researching chemical reaction and a parameter optimization method for users. According to the method, a potential energy surface data set composed of a large number of conformations with six degrees of freedom can be generated, and a data basis is provided for model development and parameter optimization. The method is mainly used for researching the chemical reaction process and the mechanism thereof, and has a wide application prospect in the research fields of chemistry, material science and the like.

This invention relates to the field of engineering machinery safety technology, specifically disclosing a load stability prediction system for telescopic boom forklifts based on the force exerted by the boom. The system collects motion sensing data and stress wave sensing data at the boom hinge; calculates a cross-spectral coherence function based on the motion data to generate a hinge coherence disorder index; performs time-frequency transformation on the stress wave data and automatically identifies energy impact patches, extracting their peak energy, center frequency, and duration; and generates a transient collision intensity spectrum index based on sensitive frequency band mapping and aggregation; constructs a time-series comprehensive feature vector from the two indices, inputs it into a pre-trained multi-layer gated recurrent unit structure for nonlinear dynamic system identification, and outputs a fused stability assessment state quantity; maps this state quantity to a virtual potential energy surface for multi-step forward extrapolation, calculates the dynamic stability margin value, and issues an early warning when the margin is lower than an adaptive threshold; this invention achieves early and accurate prediction of nonlinear jump instability.

A method for training and subsequently executing a machine learning network of one or more machine learning (ML) models is disclosed. The systems described herein are configured to embed atomic locations and categories of a given atomic system and apply them to ML model (s) to learn charge transfer attributes and local energy. By constructing atomic charges from the learned charge transfer attributes, local and global charge neutrality is ensured. Atomic charges are then used to generate an auxiliary Hamiltonian description. Attributes, such as the total energy of the atomic system, are determined by combining the auxiliary Hamiltonian description and the learned local energy. By determining the total energy from the local energy described and learned by the auxiliary Hamiltonian, such a method ensures that both long range and short range effects are considered while also appropriately achieving true discontinuities and / or transitions within potential energy surfaces.

Methods for a machine learning network that trains and subsequently executes one or more machine learning (ML) models are disclosed. The system described herein is configured to embed atomic positions and species of a given atomic system and apply those to ML model(s) to learn charge transfer properties and local energies. By constructing atomic charges from learned charge transfer properties, both local and global charge neutrality is ensured. The atomic charges are then used to generate an auxiliary Hamiltonian description. By combining both the auxiliary Hamiltonian description and the learned local energies, properties such as total energy of the atomic system are determined. By determining total energy from the auxiliary Hamiltonian description and the learned local energies, such methods ensure that long and short range effects are accounted for, while also appropriately enabling for realistic discontinuities and / or transitions within the potential energy surface.

The present application relates to the intersection field of computational chemistry and artificial intelligence, and in particular to a multi-phase chemical reaction transition state certainty generation method based on physical constraints and adaptive optimization, a system and a storage medium; The method is executed by a computer system, and the method comprises the following steps: obtaining initial structure data of reactants and products; calling a nonlinear initial guess module to perform path search based on a coarse-grained potential energy surface to generate a nonlinear initial guess path; and calling a constraint optimal transport module to generate a candidate transition state TS structure; The present application provides a multi-phase chemical reaction transition state certainty generation method based on physical constraints and adaptive optimization, a system and a storage medium, which not only maintains the high efficiency of the machine learning method, but also realizes accurate, robust and reliable simulation of complex and real chemical scenes.

The application relates to a solid-state electrolyte prediction screening method and system based on an improved bond valence site energy method, which comprises the following steps: obtaining the crystal structure of a solid-state electrolyte, constructing a three-dimensional calculation grid of a migration ion in a unit cell by using the crystal structure, calculating a global potential energy surface by using an improved BVSE model, and determining a migration energy barrier; the improved BVSE model is obtained by introducing a Coulomb repulsion term between migration ions in an original BVSE model; identifying migration ion sites based on the global potential energy surface, calculating the site energy of each migration ion site, obtaining characteristic Coulomb repulsion energy, and performing equivalent surface analysis and simple harmonic vibration analysis to obtain an equivalent surface surrounding volume and a site vibration frequency; and comparing the migration energy barrier, the characteristic Coulomb repulsion energy, the equivalent surface surrounding volume and the site vibration frequency with preset screening criteria to screen out potential solid-state electrolyte materials. The application can promote high-throughput screening of solid-state electrolyte materials.

The application provides a method for evaluating explosive safety by neural network potential calculation, comprising the following steps: S1, obtaining a microstructure image of a binder explosive by in-situ transmission electron microscopy (TEM); S2, establishing a molecular model of an energetic material and a molecular model of a binder according to the obtained microstructure image and modifying the microstructure image; S3, performing neural network training on data obtained by simulation, fitting a potential energy surface of the molecular model of the energetic material and the molecular model of the binder; S4, simulating evolution of the explosive with time under the action of a load; and S5, analyzing simulation results. The method for evaluating explosive safety by neural network potential calculation can evaluate all explosives that have been applied and have application potential by using the binder explosive real microstructure safety calculation method based on the neural network potential, greatly promotes application of new explosives, and brings great convenience to ammunition safety engineering personnel.

The application provides a potential function model acquisition method and device for conductor / insulator interface simulation, and the method comprises the following steps: the dielectric response of the conductor and the insulator is respectively processed in two sub-frames and then combined into the overall dielectric response of the system. Based on the hybrid description of the dielectric response, the charge distribution of the system under the given external electric field boundary condition can be obtained. Then, the long-range and short-range interaction separation is adopted to describe the potential energy surface of the system, and finally, the machine learning potential energy surface model containing the dielectric response of the conductor / insulator interface is obtained. The application can simultaneously describe the dielectric response of the conductor and the insulator.

The application relates to the technical field of material science, in particular to a construction method of a potential function model of a yttrium-doped antimony-tellurium phase change storage material and the potential function model. The construction method of the potential function model of the yttrium-doped antimony-tellurium phase change storage material comprises the following steps: obtaining a data set, wherein the data set comprises a plurality of training data, each piece of training data comprises a Y-Sb-Te model and a material property corresponding to the Y-Sb-Te model; training a preset NEP model by using the data set to obtain the potential function model of the yttrium-doped antimony-tellurium phase change storage material. The method solves the problem of low efficiency of current machine learning potential, can accelerate the calculation under a graphics computing card, realizes the calculation efficiency close to the classical potential function, simultaneously makes up for the shortcoming that the classical potential function cannot be applied to the complex potential energy surface scene of multiple phases and multiple configurations, and realizes the molecular dynamics simulation close to the first principle accuracy.

The application provides a molecular strain energy calculation method, a molecular screening method and related devices. The calculation method comprises the following steps: determining all rotatable dihedral angles of a target molecule based on an initial conformation of the target molecule; performing one-dimensional scanning on each rotatable dihedral angle to obtain a one-dimensional potential energy surface conformation set corresponding to each rotatable dihedral angle; calculating a first energy value of each first conformation in each one-dimensional potential energy surface conformation set to determine a first conformation set corresponding to a local energy minimum point in each one-dimensional potential energy surface conformation set; generating a pseudo two-dimensional scanning conformation set according to the first conformation sets; calculating a second energy value of each pseudo two-dimensional scanning conformation; determining a global energy minimum target conformation in the first conformation sets and the pseudo two-dimensional scanning conformation set according to the first energy value and the second energy value; and determining the strain energy of the target molecule based on an initial energy value of the initial conformation and a target energy value of the target conformation. The application improves the calculation efficiency of the strain energy.

The present application relates to a kind of material thermodynamic parameter calculation method considering thermal expansion and phonon reorganization.The temperature-dependent effective potential (TDEP) method is used to non-perturbatively calculate phonon reorganization, independently scale asymmetric equivalent cell parameters to simulate thermal expansion, and the temperature-dependent thermodynamic data of material can be accurately calculated, which can help to quantitatively reveal the relative contribution of entropy and enthalpy to free energy, and evaluate the influence of phonon reorganization and thermal expansion on material stability.The core idea of the temperature-dependent effective potential method is to use a temperature-dependent effective harmonic potential to approximate the true atomic vibration potential, and the temperature dependence is reflected in the displacement of the atoms from their equilibrium positions due to thermal and quantum fluctuations.The use of efficient random sampling to estimate the potential energy surface can greatly reduce the computational cost compared to molecular dynamics calculations.

The invention provides a quasi-reaction path adaptive enhanced sampling method, which is used for constructing a reaction machine learning potential database. The method comprises the following steps: firstly, generating a catalyst ligand candidate space by using a computer-aided molecular design tool based on a skeleton, and quickly generating a transition state initial conjecture through a GENiniTS-RS algorithm; the method comprises the core steps of combining an intrinsic reaction coordinate path of a semi-empirical quantum chemistry method (GFN2-xTB), and performing disturbance sampling around an IRC path by using normal modulus sampling so as to capture complex characteristics of a reaction potential energy surface. Then, a committee queries an active learning strategy to screen a high-information-entropy configuration to carry out high-precision density functional theory (DFT) marking; compared with full DFT sampling, the method has the advantages that the calculation cost is remarkably reduced, meanwhile, the success rate and precision of a machine learning model in transition state optimization are improved through enhanced sampling, and key data support is provided for high-throughput screening of efficient catalysts.

This application discloses a method, apparatus, medium, and device for predicting the thermodynamic properties of fluorinated compound molecules, relating to the fields of computational chemistry and materials simulation. The method includes: performing quantum chemical calculations on a target fluorinated compound molecule to obtain its true potential energy surface; extracting and subtracting the full-strength nonbonding interaction energy of 1-3 atomic pairs within the target fluorinated compound molecule based on the true potential energy surface, eliminating redundant calculations of intramolecular nonbonding interactions, and obtaining a pure harmonic force constant; establishing a dynamic coupling relationship between the nonbonding energy activation factor and temperature, which can adjust the strength of nonbonding interactions according to temperature changes; and loading the pure harmonic force constant in molecular dynamics simulations and dynamically calling the nonbonding energy activation factor corresponding to the dynamic coupling relationship according to the simulation temperature, thereby predicting the thermodynamic properties of the target fluorinated compound molecule from low-temperature solid to high-temperature supercritical fluid phases.

The invention discloses a construction method of a photolysis full-dimensional potential energy surface of a first excited state of nitrite molecules, and relates to the technical field of computational chemistry and molecular reaction kinetics. According to the invention, a multi-reference configuration interaction method is adopted to carry out system evaluation and optimization selection on a molecular active space, sampling and calculation of data points are carried out in a molecular full-dimensional global configuration space, and a key reaction channel for generating hydroxyl and nitric oxide products through dissociation of nitrous acid molecules is completely covered. A multi-reference configuration interaction method is adopted to systematically evaluate and optimally select an active space, so that a key reaction channel for generating hydroxyl and nitric oxide products through dissociation of nitrous acid molecules is completely covered, and the potential energy description precision in a strong electron association region such as O-N bond dissociation is improved by the strategy; the problem of potential energy surface deviation possibly caused by insufficient theoretical precision of an existing method is solved, and a reliable full-dimensional potential energy surface data basis is provided for accurately describing the dynamic evolution process of excited state molecules along a main reaction channel.

The application discloses a method for diacetylene topological chemical reaction, which comprises the following steps: spin-coating a diacetylene solution on a substrate to form a coating film; and irradiating the coating film with an infrared light source to generate polydiacetylene through reaction. Compared with the side reactions and degradation caused by high-energy rays which make materials enter an electronic excited state, and the material fragmentation caused by the violent spatial change of molecules under high polymerization, the mid-infrared light vibration excitation in the method can be used as a means for initiating the vibration of molecules in an electronic ground state, and can be more widely close to a potential energy surface under mild conditions, so that various adverse effects generated in the electronic excited state can be avoided.

The application discloses a kind of hot aerodynamic elastic system across saddle transition dynamics stability analysis method, comprising: S1, construct nonlinearly heated wallboard aeroelastic system analysis model, obtain the buckling modal configuration and vibration modal configuration of linearized system;S2, construct the reduced-order analysis model of nonlinearly heated wallboard aeroelastic system, for analyzing the stability characteristics of system initial equilibrium position;S3, construct the potential energy surface of heated wallboard structure, for analyzing the balance point species, quantity and position of wallboard under different temperatures;S4, consider aerodynamic elastic effect and load aerodynamic load to construct the linearized across saddle transition dynamics analysis model of hot aeroelastic system, for analyzing the across saddle transition dynamics characteristics of multi-stable non-conservative dynamic system saddle point local jump behavior.The application realizes high-fidelity prediction to the nonlinear thermal aeroelastic behavior of wallboard structure, provides key theoretical tool for the thermal strength reliable design and accurate evaluation of wallboard structure.

Methods for a machine learning network that train and then execute both an autoencoder and a machine learning model within a context of machine-learned interatomic potentials are disclosed. The system described here is configured to embed atomic positions and species of a given atomic system and apply these to an autoencoder to learn an auxiliary property, and to a machine learning model to learn local energies. The auxiliary property is then used to generate a Hamiltonian auxiliary description. By combining both the Hamiltonian auxiliary description and the local energies, properties such as the total energy of the atomic system are determined.By processing machine learning through both an autoencoder and a machine learning model, such methods ensure that long-range and short-range effects are taken into account, while also conveniently enabling realistic discontinuities and / or transitions within the potential energy surface.

This invention belongs to the interdisciplinary field of artificial intelligence and computational chemistry, specifically relating to a method and system for predicting molecular properties based on multi-view equivariant graph neural networks, particularly suitable for high-precision molecular potential surface fitting and large-scale molecular dynamics simulations. Addressing the problems of existing equivariant networks relying on tensor products, resulting in high computational complexity, and being limited by local views in capturing long-range interactions, this invention first constructs a multi-granularity topological view encompassing microscopic atoms, mesoscopic motifs, and macroscopic pharmacophores. Second, utilizing vector inner products and scalar gating mechanisms, it performs scalar-vector decoupled equivariant interactions without tensor products, implicitly extracting local geometric features. Subsequently, it performs equivariant information fusion through cross-view attention, aggregating long-range nonlocal interactions. Finally, it combines physical priors to infer energy and forces, thereby obtaining molecular properties.