A machine learning potential energy surface model construction method
By employing data-driven feature engineering and a multi-layered nested architecture for machine learning models, the problems of insufficient fitting accuracy and computational speed of existing machine learning potential surface models are solved, achieving broad applicability and efficient computation for multiphase and periodic/aperiodic systems.
CN117273169BActive Publication Date: 2026-02-10TIANJIN UNIV
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Patent Information
- Application Number
- CN202311242223.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-25
- Publication Date
- 2026-02-10
- Estimated Expiration
- 2043-09-25
AI Technical Summary
Technical Problem
Existing machine learning potential surface models are insufficient in terms of fitting accuracy and computational speed, and are difficult to apply to multiphase systems and periodic/aperiodic systems.
Method used
A data-driven approach is used to construct a machine learning potential surface model. Through feature engineering, the model learns autonomously and iteratively, avoiding human bias. It supports multiphase and periodic/non-periodic systems and is trained using a multi-layered nested architecture machine learning model.
Benefits of technology
It improves the model's fitting accuracy and computation speed, is applicable to multiphase and periodic/aperiodic systems, shortens computation time, and enhances computational efficiency.
✦ Generated by Eureka AI based on patent content.
Abstract
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.
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