Virtual Material Design Framework for Secondary Battery Development
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Solution Overview
Problem
Current methods for developing new materials for secondary batteries are time-consuming and costly, requiring separate experiments and meta-heuristic calculations across molecular, microstructure, and cell structure stages, with limited data integration and accuracy in evaluating material characteristics.
Innovation Solution
A virtual material design service framework based on a microservice architecture, which integrates data across various scales using a combination modeling scheme and artificial intelligence, allowing for simultaneous checking of results across stages and improving accuracy through image-based AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate experiments and meta-heuristic calculations are performed across molecular, microstructure, and cell structure stages, then comprehensive material evaluation is achieved, but development time and costs increase
Solution Approach 1:
The system segments the material development process into independent microservices corresponding to different structural stages (molecular, microstructure, cell structure). Each microservice handles specific calculations and experiments independently, allowing parallel execution and simultaneous result checking, thereby reducing overall development time while maintaining comprehensive evaluation accuracy.
Solution Approach 2:
The system performs preliminary virtual screening and filtering at earlier stages using AI models trained on historical data. This preliminary action identifies promising candidates before committing to time-consuming experiments and detailed calculations, significantly reducing the number of materials that require full-stage evaluation and thus reducing overall development time.
2Measurement precision
If separate experiments and meta-heuristic calculations are performed across molecular, microstructure, and cell structure stages, then comprehensive material evaluation is achieved, but development costs increase
Solution Approach 1:
The system segments the material development process into independent microservices corresponding to different structural stages (molecular, microstructure, cell structure). Each microservice handles specific calculations and experiments independently, allowing parallel execution and simultaneous result checking, thereby reducing overall development time while maintaining comprehensive evaluation accuracy.
Solution Approach 2:
The system performs preliminary virtual screening and filtering at earlier stages using AI models trained on historical data. This preliminary action identifies promising candidates before committing to time-consuming experiments and detailed calculations, significantly reducing the number of materials that require full-stage evaluation and thus reducing overall development time.
3Measurement precision
If data is integrated across multiple scales using combination modeling, then material discovery accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the material development process into independent microservices corresponding to different structural stages (molecular, microstructure, cell structure). Each microservice handles specific calculations and experiments independently, allowing parallel execution and simultaneous result checking, thereby reducing overall development time while maintaining comprehensive evaluation accuracy.
Solution Approach 2:
The system introduces an AI service as an intermediary that integrates data from multiple scales (molecular, microstructure, cell structure) through combination modeling. This AI service acts as a mediator that combines simulation models with experimental data, managing the complexity of multi-scale data integration while improving material discovery accuracy through unified analysis.
4Measurement precision
If image-based AI models are used to evaluate material characteristics, then evaluation accuracy is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary virtual screening and filtering at earlier stages using AI models trained on historical data. This preliminary action identifies promising candidates before committing to time-consuming experiments and detailed calculations, significantly reducing the number of materials that require full-stage evaluation and thus reducing overall development time.
Solution Approach 2:
The system segments the material development process into independent microservices corresponding to different structural stages (molecular, microstructure, cell structure). Each microservice handles specific calculations and experiments independently, allowing parallel execution and simultaneous result checking, thereby reducing overall development time while maintaining comprehensive evaluation accuracy.
Data Source
AI summary
Proposed is a virtual material design service framework system for developing a material for a secondary battery based on a microservice architecture. The system may include a user interface unit receiving information on a new material having at least one structure of a molecular structure, a microstructure, and a cell structure, an experiment database unit storing experiment data information corresponding to the information on the new material, and a simulation unit generating a simulation model by performing simulations on the received information on the new material. The system may also include a data modeling unit generating a data model based on the experiment data information, a combination modeling unit combining the simulation model and the data model according to a combination modeling scheme, and an artificial intelligence service unit converting the combination model into an image and deriving a candidate material based on a predetermined image recognition scheme.