10 results about "Potential energy surface" patented technology

The application belongs to the field of semiconductor process, and discloses a high-speed microscopic design method for semiconductor process based on an artificial intelligence algorithm. The application firstly trains a high-dimensional potential energy surface model of a system by using an artificial intelligence algorithm of deep learning and active learning, then tests the accuracy of the model, and under the premise of ensuring high accuracy of the first principle calculation, uses the potential energy surface model to optimize the semiconductor process. The model based on deep potential can more quickly calculate phase transition of atomic lattices, diffusion of impurity atoms, ion implantation, photolithography, etching, thin film deposition and growth problems in the semiconductor process. The application realizes fitting of the high-dimensional potential energy surface based on the algorithm of artificial intelligence, can perform microscopic design on the semiconductor process, and has the characteristics of high calculation accuracy, fast calculation speed, short design period and low design cost.

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.

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.

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.

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 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.

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.

The application discloses a molecular dynamics trajectory prediction method and system fusing physical force field prior, and relates to the fields of computational chemistry and artificial intelligence. The method acquires initial structure data of a to-be-detected molecular system and physical prior parameters (such as partial charge) between atoms; through a double embedding and residual enhancement mechanism, the physical prior parameters are mapped into high-dimensional features; an improved isometric graph neural network based on physical force field perception is constructed; in the message passing stage, the high-dimensional features are used to construct an electrostatic attention mechanism, to generate electrostatic interaction force features and to be reduced and fused into the updating process of edge features; finally, trajectory prediction is outputted based on the updated edge features through the isometric network. The application implicitly learns potential energy surface features without destroying isometricity, and significantly improves the physical authenticity and long-term stability of the model for molecular dynamics long trajectory prediction.