Graph-Based Recipe Batch Selection for Battery Testing
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Solution Overview
Problem
In material science and battery manufacturing, the vast number of possible chemical combinations generated by AI models makes it impractical to test all recipes in laboratory settings, requiring a method to efficiently select a subset of recipes for experimentation that share a maximum number of chemicals while minimizing the total number of chemicals used.
Innovation Solution
The system generates a batch of recipes that share a maximum number of chemicals in common by using a graph-based approach, where recipes are filtered and bucketized based on an edge threshold, and a maximum clique is identified to minimize the number of chemicals used and maximize shared chemicals, allowing for efficient laboratory testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all possible chemical combinations generated by AI models are tested in laboratory settings, then complete experimentation and discovery of advanced materials with desirable properties is achieved, but the time and resources required become impractical due to the vast number of possible recipes
Solution Approach 1:
The patent extracts and selects only the most promising subset of recipes from the vast space of all possible chemical combinations. By using graph-based approaches and maximum clique identification, the system extracts a manageable subset that is likely to contain the best materials, avoiding the need to test every possible combination while maintaining high reliability in the selection process.
Solution Approach 2:
The patent segments the large space of possible recipes into manageable groups using graph theory. Recipes are represented as nodes in a graph, and relationships between them are defined by shared chemicals. This segmentation allows the system to process and test recipes in organized batches rather than individually, significantly reducing the time required while maintaining comprehensive coverage of the chemical space.
2Reliability
If a large number of recipes are tested to ensure comprehensive material discovery, then the likelihood of finding advanced materials with desirable properties increases, but the total number of chemicals used and resources consumed increases proportionally
Solution Approach 1:
The patent merges recipes that share common chemicals into unified testing batches. By identifying maximum cliques in the recipe graph, the system combines multiple recipes into single test batches where shared chemicals are used once for all recipes in the batch. This merging significantly reduces the total number of chemicals needed while maintaining the ability to test a comprehensive set of recipes.
Solution Approach 2:
The patent creates a universal testing framework where a single batch can evaluate multiple recipes simultaneously. The graph-based approach allows one testing operation to serve multiple purposes by testing all recipes in a clique together, making the testing process multi-functional and resource-efficient while maintaining high material discovery probability.
3Loss of substance
If recipes are selected to share a maximum number of chemicals in common, then the total number of chemicals used is minimized, but the complexity of selecting and organizing these recipes increases
Solution Approach 1:
The patent replaces manual or simple filtering methods with automated graph-based computational algorithms. By representing recipes as a graph structure and using maximum clique identification algorithms, the system automatically identifies optimal recipe groupings that minimize chemical usage. This substitution of mechanical/simple methods with intelligent algorithms reduces the complexity burden while achieving the desired chemical minimization.
Solution Approach 2:
The patent changes the approach from evaluating individual recipes to evaluating relationships between recipes through graph theory parameters. By transforming the problem into a graph optimization problem with parameters like edge thresholds and clique sizes, the system can efficiently identify optimal recipe groupings. This parameter transformation simplifies the selection process while achieving the dual goal of minimizing chemicals and managing complexity.
Data Source
AI summary
Disclosed are systems and methods for generating candidate recipes for batch testing battery recipes in robotics laboratory equipment. In one embodiment, the candidate recipes in a batch, share the maximum number of chemicals in common, while as a batch, they utilize a minimum number of chemicals. The candidate recipes are identified by constructing a graph where an initial selection of recipes are placed at each node. The graph yields the candidate recipes in the batch.