513results about "Chemical data visualisation" patented technology

The invention discloses a drainage basin nitrogen and phosphorus pollution prediction method based on rainfall runoff migration, and relates to the technical field of water quality pollution prediction, and the method comprises the steps: accurately obtaining land utilization and water body distribution through a remote sensing technology, carrying out the space division of a pollution source through combining a nitrogen and phosphorus load coefficient, and constructing a rainfall runoff model. The runoff volume and time and space distribution under different rainfall events are reflected, and the spatial dynamic change of pollutants is captured through a hydrodynamic model, in combination with a convection diffusion equation and by introducing a conversion model of nitrogen and phosphorus in various forms. By integrating remote sensing data, rainfall runoff simulation, hydrodynamic force and nitrogen and phosphorus form transformation, the space-time migration and transformation process of nitrogen and phosphorus pollution in a drainage basin is described, so that the coupling relation between pollution source distribution and runoff power is revealed, the conveying and diffusion rule of pollutants in a river network is dynamically reflected, and the drainage effect is improved. And refining to transformation evolution of different nitrogen and phosphorus forms, and generating intuitive concentration distribution and thermodynamic diagrams through spatial interpolation.

The invention provides a constructional engineering concrete compressive strength detection method and system.The method comprises the steps that multi-modal information of a to-be-detected concrete member for constructional engineering is collected, and the multi-modal information comprises rebound data, ultrasonic data, resistivity data and temperature data; constructing a hybrid prediction model; and outputting compressive strength data according to the mixed prediction model, and visually displaying the compressive strength data. According to the method and the system for detecting the compressive strength of the constructional engineering concrete, disclosed by the invention, multi-modal information such as ultrasonic, rebound, resistivity and temperature of the to-be-detected concrete member is input into the mixed prediction model for compressive strength prediction, and the model can be used for more accurately processing nonlinear and high-dimensional characteristics in data; a transfer learning mechanism is introduced, so that the model adapts to changes of different regions and materials under limited training data, and the generalization ability and accuracy of prediction are improved. And the compressive strength data is visually displayed, so that the analysis efficiency of engineers is improved.

The invention provides an energy storage battery state simulation and prediction system and method based on digital twinning, and aims to solve the problems that an existing energy storage system is inaccurate in state modeling, insufficient in sensing granularity, unexplained in prediction, lagged in response and the like. According to the method, an equivalent thermal model, an electrochemical model and an aging mechanism model are adopted for modeling a battery, and a self-adaptive gating parameter fusion mechanism and data characteristics are introduced, so that cross-scale state mapping and characteristic reconstruction from a battery cell to a system level are realized; generating a baseline trajectory by using an electric-thermal-aging coupling relationship, realizing multi-time-domain closed-loop prediction by combining a data-driven residual correction mechanism, and identifying potential faults and risks in advance through a multi-time-domain anomaly judgment system; and constructing a three-dimensional topology model to dynamically display the state parameters, generating a graded alarm signal and providing operation suggestions. The system constructed by the invention has high precision, interpretability and prediction and visualization capabilities, and can significantly improve the intelligent operation and maintenance level of the energy storage battery system.

The invention relates to the technical field of soil pollution analysis, and discloses a soil heavy metal intelligent evaluation method, system and terminal, and the method comprises a multi-source heterogeneous data fusion and preprocessing step, a dynamic spatial interpolation modeling step and an intelligent visualization and decision support step. The system corresponds to the method, and the terminal equipment corresponds to the system. According to the method, comprehensive data support for soil heavy metal pollution is realized through multi-source heterogeneous data fusion and preprocessing; through dynamic spatial interpolation modeling and a dynamic parameter adjustment mechanism, high-precision grid surface layer generation is realized, and the fitting precision of pollution spatial distribution is improved; intelligent pollution grading and multi-factor dynamic calculation of pollution indexes are realized through self-adaptive grading and pollution index calculation, and the accuracy of an analysis result is improved; through intelligent visualization and decision support, a dynamic and visual decision basis is provided for multi-department collaborative governance and risk early warning.

The invention discloses a mixed gas absorption spectrum analysis method and system based on variational mode decomposition, specific laser is injected into an optical resonant cavity unit, and a detector unit continuously monitors the light intensity change and records a light intensity attenuation signal; pre-processing the recorded light intensity attenuation signal; carrying out VMD decomposition on the preprocessed ring-down signal to obtain a plurality of IMF components, respectively introducing CO2 and CO gases with known concentrations into the optical resonance unit, and recording spectral data of each single gas component; calculating the similarity and contribution degree of each IMF component, and setting a weight combination to form a joint score; the gas with the highest joint score is selected, the score is compared with an adaptive threshold value, and when the score is higher than the adaptive threshold value, the IMF component is marked as the characteristic component of the corresponding gas; and finally, gathering and outputting the marked IMF components according to gas types to obtain characteristic signals of the gases, thereby realizing multi-component gas separation in the mixed gas absorption spectrum.

A method performed by at least one processor, includes receiving a query comprising at least one of a product, a reactant, a reagent, and a reaction condition; generating a query embedding vector corresponding to the query by inputting the query into a neural network model; extracting a candidate embedding vector from among a plurality of embedding vectors based on a similarity between the query embedding vector and the plurality of embedding vectors corresponding to reaction records stored in a database; and outputting a synthesis recipe corresponding to the query by retrieving a candidate reaction record corresponding to the candidate embedding vector in the database.

The invention belongs to the technical field of biology, and discloses a non-natural pathway generation method and system based on a continuous biological template, and the method comprises the following steps: obtaining an enzymatic reaction data set, and preprocessing the enzymatic reaction data set; constructing a directed reaction diagram on the basis of the preprocessed enzymatic reaction data set, performing similar compound search on a target compound by utilizing the directed reaction diagram, and obtaining a continuous group conversion template of the similar compound in the natural enzymatic pathway by taking the searched similar compound as a starting point and combining a depth-first search mode; the continuous group conversion template is applied to a target compound in a molecular linear input specification form, and an inverse synthesis process of the target compound is simulated to generate a non-natural synthesis pathway based on the continuous group conversion template. The method not only enhances the biological feasibility and construction success rate of the approach, but also has universality, expandability and engineering potential, and provides a generalizable normal form for the field of synthetic biology.

The invention discloses a process parameter regulation and control method and system for the real-time fermentation state of an agricultural microbial preparation, and the method comprises the steps: S1, building a monitoring and data integration platform, carrying out the coding management of a fermentation container, collecting multi-dimensional data, carrying out the standardization processing, and building a historical database; s2, developing a visual display system, presenting fermentation room layout and color block superposition duration information in a graphical interface, and early warning deviation; s3, constructing an evaluation and prediction model, evaluating the state through a deep neural network and a time sequence algorithm, and predicting the progress; s4, developing an adjustment strategy generation engine, automatically analyzing an abnormal reason and generating a multi-parameter collaborative optimization suggestion; and S5, evaluating the regulation and control effect and continuously optimizing. The system comprises a monitoring and data integration module, a visual display module, an evaluation and prediction module, an adjustment strategy generation module and an effect evaluation optimization module. Intelligent upgrading of the whole production process is achieved, the response speed and decision accuracy are improved, the fermentation regulation lag loss is reduced, and high-quality production is guaranteed.

The invention relates to the technical field of radiation environment monitoring, and discloses a dose calculation method and system based on radiation environment monitoring data. The method comprises the following steps: acquiring radiation source data, calculating initial radiation field distribution based on the data, obtaining an initial dose deposition value of each space point in a monitoring area, analyzing interaction among particles, and determining an energy redistribution coefficient of each space point; adjusting the initial dose deposition value point by point by using the coefficient to obtain corrected three-dimensional dose concentration field data, calculating a gradient vector between spatial points, and generating an optimized gradient distribution diagram; extracting a boundary point set with gradient change exceeding a threshold value, constructing a contour surface grid to form a three-dimensional contour surface structure, obtaining a time sequence radiation source data iteration updating coefficient, and obtaining a concentration field time sequence evolution sequence; the high-concentration area is identified and marked based on the sequence, the high-concentration area and the three-dimensional contour surface structure are fused, a visual distribution model reflecting the dose concentration is generated, and the accuracy of radiation dose calculation is improved.

The invention discloses an oil and gas pipeline corrosion prediction method and platform based on deep reinforcement learning, and relates to the technical field of oil and gas pipeline corrosion prediction. Comprising the following steps: collecting time sequence data related to pipeline operation through a multi-dimensional sensor, realizing unequal interval data reconstruction through an improved signal processing technology, constructing a depth Q network prediction framework fused with a time sequence residual error corrosion state prediction algorithm, determining an optimal model through multi-group hyper-parameter combination iteration training optimization, and predicting the corrosion state of the pipeline. Combining a residual error correction mechanism to output a normalized corrosion rate estimation value, and performing inverse transformation to obtain an actual corrosion rate prediction result; and specific processes of prediction framework modeling, network structure construction, residual algorithm embedding, hyper-parameter optimization, test data processing, prediction result correction and the like are carried out, and the platform realizes full-process closed-loop operation of data acquisition, processing, modeling, prediction and analysis by virtue of each functional unit. According to the method, the corrosion prediction accuracy and the time sequence adaptability are remarkably improved, and technical support is provided for safe operation of the oil and gas pipeline.

The invention discloses a galvanized steel pipe surface characteristic prediction method based on process modeling, and relates to the technical field of intelligent manufacturing process optimization, and the galvanized steel pipe surface characteristic prediction method comprises the following steps: basic data acquisition, process state characteristic modeling, dynamic process diagram modeling, physical data dual-drive mixing and surface characteristic prediction. Adopting a derived feature construction method based on an embedded mechanism kernel equation to form process intermediate state variable enhanced data; by establishing a dynamic process diagram model, the state transfer and evolution process between steel pipe procedures is represented; a residual learning mechanism of a physical model and a data-driven model is combined, physical consistency constraint is introduced, a dual-drive fusion prediction framework is constructed, and multi-target surface characteristic hybrid prediction is realized; according to the method, high-precision and multi-target prediction of the characteristics such as the surface thickness, the adhesive force and the smoothness of the galvanized steel pipe is achieved, and the prediction stability and the physical interpretability under the complex galvanizing process are effectively improved.

A method for computational drug design includes defining a population of a plurality of compounds. Each compound includes one or more molecular properties. The method includes defining a training set of compounds from the population for which one or more biological properties are known. The method includes selecting, from the population, a subset of one or more compounds that are not in the training set. The method includes determining a subset score of the selected subset based on molecular properties of the one or more compounds in the selected subset, and evaluating the selected subset based on the determined subset score. The subset score is determined based on a frequency of the molecular properties in the population and on a frequency of the molecular properties in a sampled set comprising the training set and the selected subset.

The invention relates to the technical field of agricultural environment information, and discloses a rice field nitrogen and phosphorus loss amount remote regulation and control method oriented to environment data. The method comprises the following steps: acquiring historical nitrogen and phosphorus loss data, real-time meteorological data and water and fertilizer operation information of the rice field; constructing a mechanism constraint sub-model fusing nitrogen migration and transformation kinetics and phosphorus adsorption and desorption balance; performing multi-source feature extraction on the meteorological data to generate a standardized environment vector; inputting the lightweight residual convolutional neural network to output an initial prediction value; on-line fine adjustment is carried out through a Lagrangian multiplier method in combination with the mechanism model, and a corrected predicted value conforming to substance conservation is generated; and when the predicted value exceeds a threshold value, generating a drainage gate opening degree and fertilization plan adjustment instruction, and issuing the instruction to an edge terminal for execution through the narrowband Internet of Things.

The invention relates to the technical field of water ecological environment remote sensing monitoring, in particular to a river total nitrogen concentration remote sensing estimation method based on multi-source data fusion, and the method comprises the following steps: S1, obtaining and processing total nitrogen measured data, and unifying time and a coordinate reference system; s2, processing the Landsat 8 / 9 image, and extracting water body reflectivity and remote sensing indexes; s3, ERA5-Land meteorological data are acquired, and derivation indexes are calculated; s4, DEM elevation data are extracted; s5, fusing the multi-source data to generate a table type data set; s6, screening sensitive indexes based on SHAP and correlation; s7, constructing and evaluating an inversion model; s8, extracting an MNDWI water body mask; s9, generating a feature stack and inverting and outputting a total nitrogen concentration diagram; according to the method, through fusion of multi-source data, construction of a high-precision inversion model and a cloud platform automatic processing flow, high-temporal-spatial-resolution, stable and reproducible remote sensing estimation of the total nitrogen concentration of the river is realized, and the monitoring efficiency and the application value are effectively improved.

The invention relates to the technical field of metal powder metallurgy process simulation. The high-entropy alloy hot pressing sintering and nanoindentation coupled molecular dynamics simulation method comprises the steps that a three-dimensional multi-particle atomic model of a high-entropy alloy is built, and initial boundary conditions are set; adjusting an initial boundary condition to be a contractility boundary condition, setting process parameters of hot pressing sintering on the model, and executing molecular dynamics simulation of the hot pressing sintering process; performing molecular dynamics and Monte Carlo hybrid simulation on the sintered model; mechanical boundary conditions are set for the optimized atomic model, a virtual indenter is controlled to be pressed into the optimized atomic model, molecular dynamics simulation of a nanoindentation test is executed, and physical field response parameters are collected in the test process; and based on the physical field response parameters, macroscopic mechanical property indexes are extracted. According to the method, the mapping relation between the hot pressing sintering process parameters and the macroscopic mechanical property is established, and a basis is provided for optimization of the hot pressing sintering process of the high-entropy alloy powder.

The invention discloses a polymer property prediction method and system, and relates to the technical field of high polymer material information, the polymer property prediction method comprises the following steps: S1, constructing a polymer multi-property standardized data set containing an active end group label; s2, preprocessing the data set and extracting multi-modal features; s3, constructing an integrated prediction model composed of a geometric graph neural network and a GPR model; and S4, optimizing model parameters and quantizing prediction uncertainty, and fusing multiple GPR model outputs through an integration strategy to obtain a final prediction result. According to the method, the characterization accuracy is improved through data standardization and feature fusion, polymerization sites and connection chemical environments are considered through active end group labeling and multi-modal feature screening fusion, the structural characterization discrimination is enhanced, the sensitivity of a model to different main chain configurations and end group substitution is improved, and the stability of the model is improved. And through integrated modeling and uncertainty quantification, the prediction reliability is enhanced, and the prediction precision and robustness are improved.

The invention discloses a microcosmic multi-modal characteristic analysis and visualization method for a cement-based cementing material. The method comprises the following steps: S1, obtaining a back scattering image, a qualitative element surface spectrum and a quantitative element surface spectrum of a cement-based cementing material sample; s2, extracting a pore structure of the back scattering image, and generating a pore distribution diagram; s3, forming a superpixel-level element feature matrix; s4, constructing an Andata data object, and performing dimension reduction clustering to integrate multi-source heterogeneous data; and S5, based on the constructed Andata data object, realizing visual analysis of multiple types of images. The method is high in universality, is not only suitable for ordinary Portland cement systems, but also can be widely applied to multi-phase identification and quantitative analysis of various complex cement-based composite materials, and provides a powerful micro-analysis tool for researching the relationship between material composition, structure and performance.

This invention relates to the field of mechanical property monitoring technology for mine backfill bodies, specifically to a system and method for acquiring and analyzing tensile strength data of mine backfill bodies. The system comprises a data acquisition unit, a physical information coupling unit, a tensile strength inversion unit, and a spatiotemporal evolution mapping unit. This invention acquires the waveguide flight time and energy attenuation coefficient of the backfill body using an embedded guided wave sensor array. It uses the monotonically increasing law of hydration degree as a physical constraint to train the physical information coupling network, outputting a compliant hydration degree time series. Based on a hydration degree-tensile strength mapping relationship library, it inverts and fits to generate a strength growth curve, which is then loaded into a four-dimensional digital twin model to invert the internal tensile strength gradient field. It automatically identifies local abnormal regions where the growth rate is below the safety threshold, improving the physical rationality and accuracy of the inversion results, and realizing visualization of the spatiotemporal evolution of backfill body strength and early safety warning.

An information processing device includes a control unit. The control unit executes a process including acquiring experimental data including an experimental condition of each of a plurality of times of experiments and an observed value under the experimental condition, and generating graph data indicating a topological structure of a geometric figure representing a correspondence between the experimental condition and the observed value in a multidimensional space with each of the experimental condition and the observed value being a dimension.

This invention discloses a non-invasive method, system, device, and storage medium for monitoring student attention based on gas sensing, belonging to the field of non-invasive sensing technology. The method includes: calculating a net physiological gas signal based on environmental baseline data and initial gas concentration data of the target student; calculating physiological characteristic indicators of the target student based on the net physiological gas signal, and determining the target student's metabolic rate level based on the physiological characteristic indicators; obtaining a state time-series signal based on the net physiological gas signal according to a preset discrimination criterion; inputting the net physiological gas signal and the state time-series signal into a pre-trained attention monitoring model to obtain the probability distribution of the target student's attention state; and determining the target student's attention state based on the target student's metabolic rate level and the probability distribution of the attention state. This invention assesses the cognitive state by capturing the gas released by the student, achieving non-invasive monitoring.

The application discloses a homopolymolybdate hybrid resistive random access memory material based on a tetraphenylstyrene molecule and a preparation method and application thereof. The preparation method comprises the following steps: dissolving a mixture of 0.05 mmol of homopolymolybdate and 0.1 mmol of the tetraphenylstyrene molecule in 10 mL of water, stirring for 2 hours, adjusting the pH to 5.0 by using HCl to obtain a mixed solution; moving the mixed solution into a 25 mL stainless steel reaction kettle lined with polytetrafluoroethylene, reacting at 160 DEG C for 3 days, and obtaining a colorless block crystal after cooling; and after filtration, washing and drying, the homopolymolybdate hybrid resistive random access memory material based on the tetraphenylstyrene molecule is obtained. The method is simple and easy to operate, raw materials are easy to obtain, and the method is easy to realize. No harmful product is generated in the preparation process, and the method is an environment-friendly green synthesis. A multi-level controllable memory storage material is prepared, and the material can be applied to a multi-level memristor.

This invention relates to the field of intelligent predictive control technology, and more particularly to a digital twin predictive-driven electrochemical intelligent control system and method for circulating cooling water, comprising a physical operation device and a control device. The control device collects water quality monitoring parameters through a data acquisition module, and a state assessment module uses the entropy weight method to map and calculate a comprehensive index. A trend inference module inputs the comprehensive index into a long short-term memory network model for forward propagation, outputting a predicted water quality index, which is then iteratively optimized by a hyperparameter optimization unit using a sparrow search algorithm to optimize the network connection parameters. An instruction optimization module compares the predicted water quality index with a safety threshold to generate control instructions, driving the physical operation device to perform electric field adjustment, mechanical scaling, and coordinated water replenishment and chemical dosing. A virtual mapping module constructs a three-dimensional digital model to achieve state early warning. This invention solves the technical problem of lag in existing feedback control, achieving precise pre-control of water quality in circulating cooling water systems.

The application discloses a kind of visualization methods for microcosmic action mechanism of steam displacement heavy oil, which comprises: obtaining the porous structure information of rock sample;According to the structure information of rock sample, design pore network structure and make microfluidic chip;Build steam displacement heavy oil visualization implementation system, and adopt steam displacement heavy oil visualization implementation system to collect the phase distribution and temperature distribution of microfluidic chip in displacement process;Boundary extraction is carried out to experimental picture, and the dynamic change process of each phase in displacement process is obtained and the interaction relationship with temperature distribution change is obtained.The application designs pore network chip according to the pore structure information of rock sample, obtains the phase distribution and temperature distribution in displacement process, and is used to study the microcosmic phase distribution characteristics and temperature distribution characteristics of fluid in heavy oil reservoir reservoir, and then the microcosmic mechanism of reservoir fluid movement is explored, so as to provide a practical visualization method for the research of steam drive oil mechanism.

The present invention relates to a method for searching for a structural gene of glass, including the following steps: determining atomic species for structure search according to the glass system; screening structure on the basis of the first principle to screen out compounds that can be formed by the interaction among each of the atoms; comparing the formation energy and the phonon spectrum of each compound to obtain stable compounds; and constructing a metastable composition diagram of a glass system according to the stable compounds, in metastable composition diagram, a micro-structural unit of a glassy compound near a target glass composition point is the structural gene of glass in the metastable glass composition diagram; designing glass properties based on the characteristics of the structural genes; and realizing the design of high-performance glasses through a hot-melt method.

The present application belongs to the technical field of oil and gas field development chemical agent design, and particularly relates to a design method and system of an ultra-low interfacial tension oil displacement agent based on AI4S. A cross-modal retrieval is used to construct a molecular structure-reservoir condition mapping database, high-dimensional embedding vectors are generated by RDKit / ChemBERTa / Mol2Vec to construct a knowledge graph, historical schemes are retrieved by a RAG mechanism and input into a DeepSeek-R1 / Qwen3 large model, an interpretable molecular design and action mechanism are generated, and the feedback closed loop optimization is verified by experiments, so that the data-AI-experiment closed loop is realized, and the research and development efficiency and design accuracy are significantly improved.

The invention relates to a soil heavy metal risk assessment method for a fuzzy region, fully considers spatial heterogeneity of soil heavy metals and a complex relationship between the soil heavy metals and agricultural products, and provides a fuzzy region averaging method, an improved potential ecological risk index method and an agricultural product risk index method. Therefore, soil heavy metal risk assessment of the fuzzy region is realized, and technical reference is provided for regional soil heavy metal risk assessment.

This invention relates to the field of semiconductor material detection technology, specifically to a method and system for detecting gallium vacancy concentration in gallium nitride (GaN) materials. The method includes: acquiring photoluminescence spectral data sets of a GaN sample under multiple excitation wavelengths; extracting the emission spectral intensity sequence corresponding to each excitation wavelength; calculating the peak intensity ratio between the yellow band emission peak and the band-edge emission peak; and constructing an initial GaN characterization parameter sequence. An improved Kalman filter algorithm with a dynamic weight allocation mechanism for multiple observation matrices is used to suppress temporal noise in the initial parameter sequence, resulting in an optimized GaN characterization parameter sequence. The optimized parameter sequence is then imported into a pre-trained concentration inversion model for feature mapping, outputting the detected GaN vacancy concentration value of the GaN sample. Based on this detection result, material quality grading is determined. This method broadens the dimensions of spectral information acquisition, effectively suppresses temporal noise interference, and achieves quantitative analysis of GaN vacancy concentration.

The application discloses a short-fiber reinforced ablative material bulk ablation simulation method, and belongs to the technical field of multifunctional composite materials and structure numerical simulation.The application comprises the following steps: constructing a discrete system composed of a series of spherical particle units by using a discrete element method; calculating the contact area, contact force and connecting force of the particle units by using a Hertz-Mindlin contact model and a parallel-bond model at each calculation time step; calculating the temperature of the particle units by using an energy conservation principle and a Fourier heat conduction law; calculating the displacement and angular displacement of the particle units by continuously integrating the momentum conservation equation twice; updating the velocity, displacement and diameter of each particle unit, and updating the porosity and pore pressure of the discrete system; iteratively solving and visualizing until a specific condition is met, and ending the simulation.The application characterizes the internal structure features of the short-fiber reinforced ablative material by using the discrete element method, so that the numerical simulation accuracy of the ablation process is improved, and the possibility of capturing the micro ablation mechanism is provided.

The invention relates to the technical field of atmospheric environment intelligent treatment, and discloses an ozone pollution dynamic prediction and control area discrimination method and system based on mixed symbol-numerical reasoning. The method comprises the following steps: constructing a knowledge graph oriented to the atmospheric photochemical reaction field, wherein the knowledge graph comprises chemical species nodes and edges with temperature-irradiance dependent reaction rates; taking the knowledge graph as a chemical kinetics constraint, taking real-time monitoring data as input, guiding the large model to carry out end-to-end prediction on the ozone peak concentration, and introducing a chemical kinetics consistency penalty term; reversely updating the reaction rate weight in the knowledge graph based on the prediction error and the residual error of the observation value to form a closed-loop optimization mechanism; and judging whether the current atmospheric environment belongs to a VOC control region, a NOx control region or a transition region by combining the uncertainty of large model prediction and the VOC dominant reaction path confidence of the knowledge graph. According to the invention, credible discrimination of ozone generation dominant factors on the principle level is realized.

This invention provides a system and program that facilitates the process of generating and / or visualizing molecular networks for library molecular structure content. [Solution] In system 500, the molecular network generation system 502 for library molecular structure content includes an evaluation component 512 that performs a comparison between a first molecular fingerprint including first molecular structure data of a first molecular structure and a second molecular fingerprint including second molecular structure data of a second molecular structure, and a visualization component 516 that generates display data that visualizes a representation of the structural similarity score obtained from the first molecular structure, the second molecular structure and the comparison. The representation by the visualization component includes edges corresponding to the structural similarity score that extend between pairs of nodes corresponding to the first molecular structure and the second molecular structure.