877results about "Chemical structure search" patented technology

The invention provides a thermal power plant coal blending combustion proportion analysis method, system equipment and a medium. According to the embodiment of the invention, physical and chemical parameters of to-be-blended-combusted fuel are collected; fuel characteristic data are determined according to the basic coal type, the standard combustion parameters corresponding to the coal types and the physical and chemical parameters of the fuel to be subjected to blending combustion; preliminarily setting a blending combustion proportion range by adopting a multi-objective optimization algorithm according to the fuel characteristic data; performing a small-scale blending combustion test based on the blending combustion proportion range, and collecting test data; and analyzing test data and adjusting a blending combustion ratio to optimize a preset combustion model so as to analyze the coal blending combustion ratio, so that through accurate blending combustion ratio control, the utilization of fuel energy is maximized, the power generation efficiency is improved, the emission of harmful gases such as NOx and SOx is reduced, the stricter environmental protection standard is met, the fuel cost and the combustion benefit are balanced, and the total operation cost is reduced. The market competitiveness of the thermal power plant is enhanced.

The invention relates to an inverse synthesis route planning method and system based on a multi-modal large model, and belongs to the technical field of chemical synthesis and artificial intelligence crossing. The method comprises the following specific steps: firstly, extracting three-dimensional molecular structure characteristics of a target product through a molecular encoder, and aligning the three-dimensional molecular structure characteristics with text modals of molecules; secondly, reasoning a possible reaction mechanism and a reaction type, and reducing an inverse synthesis search space; secondly, splitting a target product into reasonable synthesis subunits through structured marking and vocabulary expansion, data-driven atomic assignment learning and reaction center recognition and activity prediction; finally, SMILES representations and reaction paths of the reactants are generated. According to the method, the accuracy and interpretability of inverse synthesis path planning can be improved, and a new technical means is provided for automation and intellectualization in the fields of new drug development, material design and the like.

The invention discloses a soil organic pollutant identification method and system based on combination of AI and high-throughput screening, and relates to the technical field of pollutant screening, and the method comprises the following steps: extracting a substance peak from high-resolution mass spectrum data of a soil sample; the method comprises the following steps: establishing an organic pollutant mass spectrum database, extracting spectrogram characteristics and structural characteristics of compounds in the organic pollutant mass spectrum database, establishing a molecular fingerprint prediction model, and establishing a mapping relation from a spectrogram to a structure; constructing a spectrogram vector prediction molecular fingerprint according to the extracted substance peaks, and retrieving by using an organic pollutant mass spectrum database to obtain candidate chemical structures; scoring the candidate chemical structures by predicting the molecular fingerprints, selecting the chemical structures meeting a preset standard, constructing a recognition basis according to the selected chemical structures, and obtaining an organic pollutant recognition result of the soil sample. According to the method, non-targeted intelligent analysis is carried out through cross fusion of artificial intelligence and mass spectrometry, and rapid and accurate identification of soil organic pollutants is realized.

The invention discloses a molecular property prediction method based on a molecular line graph structure. The method comprises the following steps: firstly, collecting SMILES molecular formulas of drug molecules, and carrying out pretreatment to extract molecular fingerprints and molecular descriptors; then, the molecular line graph structure serves as model input, feature representation of a molecular line graph is calculated through a graph neural network MolGraphTrans based on Transformer, and molecular properties are predicted. According to the method, substructure masks, a multi-head attention mechanism, path coding and distance coding are introduced, so that molecular structure characteristics can be better captured. And finally, the prediction performance is optimized through a low-rank linear layer and a multi-head attention layer. Compared with the prior art, the accuracy and efficiency of molecular property prediction are improved, and the method is suitable for drug screening and optimization.

The invention relates to the technical field of water conservancy projects, in particular to a hydropower station safety monitoring method and related equipment, and the method comprises the steps: obtaining the water quality information of a water body of a water area where a target hydropower station is located; judging whether the water quality information meets a preset water quality requirement; if the water quality information does not meet the preset water quality requirement, obtaining abnormal information; based on the abnormal information, processing a water area body of the target hydropower station; if the water quality information meets the preset water quality requirement, acquiring the change condition of the water quality information; judging whether the target hydropower station has a potential risk or not based on the change condition; and if the target hydropower station has the potential risk, processing the potential risk. The accuracy of safety monitoring of the hydropower station is improved, and the safety risk of the hydropower station is reduced.

A method for machine learning aided modeling of two interacting structures may include: (a) receiving an input structure comprising an interaction region; (b) generating a plurality of candidate structures using a first differentiable machine learning model; (c) docking one or more candidate structures of the plurality of candidate structures at the interaction region of the input structure using a second differentiable machine learning model to predict a docking geometry; (d) ranking the one or more candidate structures of the plurality of candidate structures docked in (c) using a third differentiable machine learning model to predict a score; and (e) backpropagating the score to (i) the first differentiable machine learning model to update the plurality of candidate structures or (ii) the second differentiable machine learning model to update the docking geometry.

The invention provides a metal matrix composite microstructure strengthening simulation method and system, and relates to the technical field of composite mechanical property simulation, and the method comprises the following steps: establishing a polycrystalline aluminum matrix atomic model and a spherical silicon nitride particle strengthening phase atomic model unit; embedding the spherical silicon nitride particles into the polycrystalline aluminum matrix according to the target embedding position coordinates, and integrating to generate an atomic scale structure model; according to the method, an embedded atom potential function is adopted to describe interaction among atoms in a polycrystalline aluminum matrix, a Tersoft potential function is adopted to describe covalent bond interaction among atoms in spherical silicon nitride particles, and a Lennard-Jones potential function is adopted to describe interaction among heteroatoms at a Si3N4 / Al interface; based on the interaction potential between atoms, molecular dynamics simulation is adopted for conducting uniaxial stretching simulation on the atomic scale structure model, mechanical property parameters of the particle reinforced metal matrix composite are obtained through calculation, and a visualization method is combined for analyzing the microcosmic strengthening mechanism of the particle reinforced metal matrix composite.

The present application relates to a construction method for a corrosion-resistance degree-mechanical property analysis model of a metal material, and the use thereof. The construction method for the analysis model comprises the following steps: using a metal material as a test object, and acquiring corrosion test experimental data sets at different equivalent corrosion time points under corrosion test conditions and mechanical property test experimental data sets at different corrosion degrees corresponding to the different equivalent corrosion time points; and respectively establishing an empirical relational expression of corrosion parameters changing with an equivalent corrosion time and an empirical relational expression of mechanical parameters changing with the equivalent corrosion time.

The invention relates to a construction method and application of a corrosion resistance degree-mechanical property analysis model of a metal material. The application relates to an electric device comprising a battery system. The construction method of the analysis model comprises the following steps: taking a metal material as a test object, and obtaining a corrosion performance test experiment data set at different equivalent corrosion time points under a corrosion test condition and a mechanical performance test experiment data set at different corrosion degrees corresponding to the different equivalent corrosion time points; based on the corrosion performance test experiment data set, a first group of empirical relational expressions of corrosion parameters changing along with the equivalent corrosion time are established, based on the mechanical performance test experiment data set, a second group of empirical relational expressions of mechanical parameters changing along with the equivalent corrosion time are also established, and the corrosion test conditions are used for simulating a target service environment. The analysis model can be effectively used for mechanical property prediction and service life evaluation of the metal material, and the metal material can comprise an aluminum alloy material and can further comprise a battery box body material.

A compound safety prediction device includes an input unit for inputting the structural formula of at least one molecule, a safety prediction unit for predicting a safety evaluation of the molecule and computing a confidence score level of the prediction, a similar molecule data searching unit for acquiring the safety evaluation data of a similar molecule similar to the molecule, and an output unit for outputting the result of prediction of the safety evaluation of the molecule, the confidence score level of the prediction, and the safety evaluation data of the similar molecule.

The invention relates to the technical field of metal plastic forming, and discloses a titanium alloy radial forging reduction rate collaborative optimization method based on material testing, which comprises the following steps of: acquiring dynamic characteristic data in a forging process in real time by acquiring multi-dimensional material performance parameters, and constructing a multi-target collaborative optimization model; and intelligent collaborative configuration of the reduction rate parameters is realized by adopting a self-adaptive weight distribution strategy. And a thermal coupling factor normalization equation and a phase field free energy functional normalization model are established, and the technical problems that in a traditional forging process, due to empirical configuration of reduction rate parameters, the structure is uneven, and residual stress exceeds the standard are solved. A lightweight model is deployed through edge computing nodes, millisecond-level dynamic correction of an optimization scheme is realized in combination with a 5G industrial private network, and the grain size qualification rate and the size precision of the titanium alloy forgings are improved. The method improves the yield of the grain size of the forge piece, ensures that the residual stress of the forge piece is reduced, stably controls the size precision, and improves the batch stability of high-end titanium alloy parts.

Machine learning can be used to identify and / or predict the properties of molecules. A mathematical model is trained to generate a combined embedding space that includes language embeddings and molecule representation embeddings. The mathematical model may be a fine-tuned language model. The fine-tuned language model may receive queries related to molecules and properties of molecules and provide predictions of properties of the queries molecules, similar molecules, and / or may find molecules with a desired property.

The invention relates to a data cleaning method, device and system for interstellar molecules and a storage medium, and the method comprises the steps: obtaining the compound basic information of to-be-cleaned interstellar molecules; inputting the compound basic information of the interstellar molecules into a plurality of preset large language models for preliminary cleaning to obtain interstellar molecule one-dimensional structures under different large language models; comparing the interstellar molecule one-dimensional structures under different large language models, and inputting the interstellar molecule one-dimensional structures meeting preset requirements into a molecule linear input specification program for final cleaning to obtain a target interstellar molecule one-dimensional structure; the preset requirement is that the similarity of the interstellar molecule one-dimensional structures under different large language models meets the preset requirement; and storing the target interstellar molecule one-dimensional structure in a corresponding server. Through the interstellar molecular data cleaning method and device, the problem of low interstellar molecular data cleaning efficiency is solved, and the interstellar molecular data cleaning efficiency is improved.

The present application relates to the technical field of metal plastic forming, and discloses a titanium alloy radial forging reduction rate collaborative optimization method based on material testing, which acquires dynamic characteristic data of a forging process in real time through multi-dimensional material performance parameter collection, constructs a multi-objective collaborative optimization model, and realizes intelligent collaborative configuration of the reduction rate parameter by using an adaptive weight distribution strategy. A thermal coupling factor normalization equation and a phase field free energy functional normalization model are established to solve the technical problems of uneven structure and excessive residual stress caused by the empirical configuration of the reduction rate parameter in the traditional forging process. A lightweight model is deployed through an edge computing node, and millisecond-level dynamic correction of the optimization scheme is realized in combination with a 5G industrial private network, thereby improving the grain size qualification rate and size accuracy of titanium alloy forgings. The present application improves the grain size qualification rate of forgings, ensures stable control of the size accuracy while reducing the residual stress of forgings, and improves the batch stability of high-end titanium alloy components.

The invention discloses a multi-component catalyst active site prediction system and method fused with quantum embedding, and relates to the technical field of catalysis and material informatics, and the system comprises a structure and site enumeration module which generates candidate sites; the adaptive quantum embedding calculation module obtains key reaction microcosmic parameters; the unified site fingerprint and feature engineering module constructs and fuses standard site fingerprints; the physical consistency machine learning module predicts adsorption energy and other parameters and uncertainty thereof; the active learning and sample selection module selects a high-value sample optimization model; the microdynamics evaluation module calculates index values such as activity; and the multi-objective optimization and sorting module generates an optimization sorting list. According to the method, the unification of calculation precision and efficiency is realized, the problem of non-unification of locus characterization is solved, the model interpretability and extrapolation reliability are improved, and the comprehensive evaluation and optimization sorting of multi-target performance are completed.

The present disclosure relates to systems and methods for chemical toxicity prediction. The methods of the present disclosure comprise: receiving test data from an analysis instrument; selecting a candidate chemical according to the test data; determining a hazard translated level and a hazard evaluation level of the candidate chemical according to a molecular fingerprint of the candidate chemical; and predicting the toxicity of the candidate chemical by using a quantitative structure-activity relationship (QSAR) model based on the hazard translated level and the hazard evaluation level.

The invention discloses a construction method and system for volatile organic compound release behavior characterization, and belongs to the technical field of air analysis and risk assessment. According to the method, feature analysis is carried out based on multi-source information such as a molecular structure, a release carrier and environmental conditions, modeling is carried out on a release process by utilizing a mapping operator, and a three-dimensional representation vector V = [I, S, E] reflecting release intensity, environmental sensitivity and time evolution is constructed. The representation is output in a behavior vector or digital label mode, and unified quantitative benchmarking of different substances in a cross-scene mode is achieved. According to the method, depth characteristics such as molecular polarity and desorption activation energy are introduced, so that the dependence on long-period environmental chamber experiments is reduced, the generalization prediction capability on unknown pollutants is improved, the logic connection problem between a bottom-layer physical mechanism and an upper-layer intelligent management and control system is solved, and the reliability of the system is improved. And standardized and explainable behavior input is provided for prospective risk research and judgment.

An interaction impact evaluation method that enable highly accurate prediction of properties or a search for new substitute materials having desired properties is provided. According to the present invention, a step for selecting a kind of interaction due to a plurality of element materials, and a step for evaluating the degree to which the selected interaction is involved with the property of the composite material are performed to evaluate the impact of interaction with respect to the property of a composite material including a plurality of types of element materials.

The invention belongs to the technical field of biological information, and relates to an acetylcholin esterase inhibitor prediction method based on Stacking ensemble learning and molecular feature fusion, which comprises the steps of data collection and preparation, data annotation and optimization, feature extraction and analysis, construction of a Stacking model, result verification and feedback and construction of a prediction platform. The molecular fingerprints and the property descriptors are used as features, and an acetylcholin esterase inhibitor classifier is successfully constructed by adopting a Stacking algorithm. According to the method, the problems that the efficiency of finding the acetylcholin esterase inhibitor by a traditional experimental method is low, and a common quantitative structure-function relationship method is high in complexity and poor in generalization ability can be solved, the new drug finding speed is increased, experimental candidates are accurately positioned, and resource waste is reduced.

In one aspect, a method for identifying residues in a particular conformation of a protein molecule capable of forming a stable binding between a compound and the protein molecule is provided. The method can include identifying, for inclusion in a perturbation set, one or more residues from the conformation of the protein molecule that exhibit a protection factor satisfying one or more thresholds. One or more residues may be excluded from the perturbation set based at least on a spatial arrangement and / or an orientation of the one or more residues. One or more rounds of molecular dynamics simulations whose result is indicative of an interaction between the compound and the residues in the perturbation set may be performed. The residues in the perturbation set may be identified as forming a stable binding between the compound and the protein molecule based at least on the result of the molecular dynamics simulation

The invention belongs to the technical field of traditional Chinese medicine information processing, and provides a traditional Chinese medicine disease prescription data base construction method and system.The method comprises the steps that five types of data including ancient literatures, modern medical records, prescription data, traditional Chinese medicine resources and medical record manuscript images are accessed through an extensible adapter frame; a traditional Chinese medicine term service platform is constructed by using multi-source term resources, and standardized data is generated from the analyzed data; the method comprises the following steps: extracting diseases, syndromes, prescriptions and traditional Chinese medicine entities by adopting a rule engine and a domain self-adaptive entity recognition model, on the basis of a public traditional Chinese medicine standard knowledge graph, aggregating neighbor node information through GAT, and introducing a contrast learning optimization entity relationship classifier to obtain an enhanced entity vector and classify a semantic relationship; constructing a knowledge graph based on the extracted semantic relationship; and carrying out data quality and algorithm performance monitoring and tracing on data access analysis, standardization processing, knowledge extraction and graph construction. Uniform access and standardized integration of multi-source heterogeneous traditional Chinese medicine data are realized.

The invention relates to the field of in-situ pH value prediction, and discloses an in-situ pH value prediction model construction method and device in an oil and gas field pipeline, equipment and a medium, which can obtain the value ranges of a plurality of in-situ pH influence parameters in the oil and gas field pipeline, and generate a plurality of environmental condition data according to the value ranges of the plurality of in-situ pH influence parameters. And performing in-situ pH value simulation on each piece of environmental condition data to obtain a corresponding in-situ pH simulation value, taking each piece of environmental condition data and the corresponding in-situ pH simulation value as training data, and performing symbolic regression analysis based on all the training data to generate an in-situ pH value prediction model of the oil and gas field pipeline. The in-situ pH value prediction model can be used for predicting the in-situ pH value of the oil and gas field pipeline under different working conditions, the model construction cost is low, the reliability is high, the prediction condition is simple and feasible, the prediction process is efficient and accurate, and the in-situ pH value prediction model can be widely applied to the in-situ pH value prediction of the oil and gas field pipeline under different environment conditions.

The invention relates to the technical field of coating preparation, and provides a polyurethane conformal coating preparation method which comprises the following steps: S1, inputting requirements of a conformal coating product and used environmental conditions into a weighing module by an operator, and triggering the weighing module to automatically adjust the ratio of raw materials; s2, sequentially adding the weighed polyurethane resin, curing agent, diluent and assistant into a mixing module, mixing to form a mixed raw material, and collecting state data in the mixing process; s3, obtaining the state data through an evaluation module, and carrying out the evaluation to form an evaluation result; s4, if the mixing qualified threshold value is met, heating the mixed raw material through a heating module; s5, transferring the heated mixed raw material into a reaction kettle to form a reaction raw material; s6, rapidly cooling the reaction raw materials to room temperature, and removing impurities to obtain a conformal coating mother solution; and S7, adding the pretreated nano filler into the mother liquor of the conformal coating, and adding a multi-interface reinforcing agent to form a conformal coating product.

The invention provides a method and a device for determining hyper-parameters of a potential energy model based on a graph neural network. The method comprises the following steps: acquiring a key sample from a plurality of samples of a data set for molecular dynamics simulation; determining a truncation radius configuration of the potential energy model based on the key sample, the truncation radius configuration including a truncation radius of each element combination; training of the potential energy model is performed based on the key sample and the truncation radius configuration, and the truncation radius configuration is adjusted based on an inference result of the trained potential energy model, where an input of the potential energy model is an atomic graph determined based on the key sample and the truncation radius configuration, and an output of the potential energy model is an atomic graph determined based on the key sample and the truncation radius configuration. The output of the potential energy model comprises energy parameters and force parameters of atoms.

The present disclosure provides a method, device, electronic device and storage medium for molecular screening, and relates to the fields of deep learning technology, graph theory technology and biological and information technology in the field of artificial intelligence technology. The method is as follows: obtain a first label map of the molecule to be screened and a second label map of the reference molecule, each molecule to be screened and the reference molecule form a molecule pair, for each molecule pair, obtain the mapping between the vertices of the first label map and the second label map and the conflict information between the mappings to generate a mapping map of the molecule pair, sample the mapping map, obtain the maximum weight fully connected subgraph of the molecule pair, and according to the maximum weight fully connected subgraph of each molecule pair, screen out the molecule to be screened with the greatest similarity to the reference molecule as the target molecule. A framework for molecular screening based on Gaussian boson sampling is proposed, so that Gaussian boson sampling can be used to efficiently implement the problem of ligand-based drug virtual screening and improve the efficiency of molecular screening.

The invention relates to a design method of an infinite layer nickel oxide with a low sodium ion migration barrier, which comprises the following steps of: constructing an infinite layer rare earth nickel oxide and titanium carbide model and expanding cells to ensure that contact surfaces are consistent; constructing a heterojunction model and converting the heterojunction model into a three-dimensional atomic coordinate file; optimizing the surface structures of the two materials, combining and adding a vacuum layer, and then calculating the interface energy; structural changes and sodium ion migration barriers are analyzed, the ionic conductivity optimization rate is calculated, and a theoretical basis is provided for sodium ion battery negative electrode material design. Compared with the prior art, the ion conduction capability of the titanium carbide two-dimensional material as the sodium ion battery negative electrode is calculated and optimized through the first principle, the migration potential barrier is reduced, the infinite layer nickelate and titanium carbide heterojunction is constructed, the sodium ion conduction performance is improved, and a new thought is provided for the design of the sodium ion battery negative electrode material.

The invention relates to the technical field of machine learning, and discloses a doped cluster ground state structure optimization method and system based on machine learning, and the method comprises the steps: collecting and optimizing original doped cluster data as a doped cluster standard data set, and dividing the doped cluster standard data set into a training set, a verification set and a test set; constructing a doped cluster potential energy surface fitting machine learning network by adding an interatomic interaction attention mechanism in an original SchNet, and training by using a training set and a verification set; inputting the test set into the trained doping cluster potential energy surface fitting machine learning network, and obtaining and outputting a final doping cluster potential energy surface; and performing structure search and optimization on the final doped cluster potential energy surface by using an artificial bee colony algorithm, and continuously updating the structure until a ground state structure with the lowest energy is found. According to the method, the fitting accuracy of the potential energy surface is improved, the calculation benefit is improved, and good transportability is achieved.

The invention discloses a method for preparing a high-strength heat-resistant die-casting Mg-Gd-Zr alloy by regulating physical parameters, and relates to the technical field of alloy die-casting processing. According to the method for researching and developing the die-casting high-strength heat-resistant Mg-Gd-Zr alloy by adjusting the Gd / Y ratio, the Zn / RE ratio and the Al content in the Mg-Gd-Zr alloy, the feasibility of the scheme is preliminarily verified through low-cost gravity casting starting from alloy component screening, a large number of elements beneficial for improving the fluidity of the alloy are screened in combination with thermodynamic calculation and dynamic calculation, and the high-strength heat-resistant Mg-Gd-Zr alloy is obtained. On the basis of keeping high strength, the die casting defects are reduced, the elongation of the alloy is improved, finally, the high-strength heat-resistant Mg-Gd-Zr alloy subjected to high-pressure die casting is successfully prepared, and the technology starts from the alloy, improves the alloy flowability, reduces the defects brought in the die casting process and improves the alloy performance.

A computer-implemented method for estimating the glass transition temperature of a polymer from the chemical structure of the corresponding polymer is provided. The invention provides a method for graphically representing the chemical structure of a polymer. And a method and system for calculating a glass transition temperature of a corresponding polymer from graphic data of the polymer by machine learning a chemical structure of the polymer based on information defining an interconnection relationship between each atom constituting a repeating unit of the polymer and a connection node to which the repeating unit of the polymer is connected, wherein in addition to the chemical structure of the polymer, the molecular weight information is also reflected.

In one aspect, a method is provided for identifying a residue in a particular conformation of a protein molecule that is capable of forming a stable bond between a compound and the protein molecule. The method can include identifying one or more residues from the conformation of the protein molecule to include in a perturbation set, the one or more residues exhibiting a protection factor that satisfies one or more thresholds. One or more residues may be excluded from the perturbation set based at least on the spatial arrangement and / or orientation of the one or more residues. One or more rounds of molecular dynamics simulations may be performed, the results of which are indicative of interactions between the compound and the residues in the perturbation set. The residues in the perturbation set may be identified as forming a stable binding between the compound and the protein molecule based at least on the results of the molecular dynamics simulation.