Material Selection Method Using Synthesizability Feedback
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Solution Overview
Problem
Existing material synthesis methods fail to consider the executability of synthesis in actual manufacturing equipment, leading to inefficient selection of materials that cannot be successfully synthesized or measured, due to reliance on pre-constructed databases without considering past manufacturing results.
Innovation Solution
A method that selects synthesis target materials based on both physical property information and execution possibility, updates the database with synthesis results, and prioritizes materials considering their synthesizability and measurability, using a system that includes a physical property prediction model and an execution possibility model to guide material selection and update databases accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If material selection is performed using only pre-constructed database information, then the search process is simple and fast, but the synthesizability and success rate in actual manufacturing equipment are poor
Solution Approach 1:
The system performs preliminary evaluation of synthesizability and measurability for candidate materials before actual synthesis. By pre-assessing whether materials can be successfully synthesized and measured in actual equipment, the system filters out unrealistic candidates early, maintaining search efficiency while improving the reliability of selected materials.
Solution Approach 2:
The system incorporates feedback from actual synthesis results to update the database and refine future material selections. By feeding back successful and unsuccessful synthesis outcomes, the system continuously improves its ability to predict synthesizability, thereby increasing the success rate of material synthesis over time.
2Adaptability or versatility
If all candidate materials from the database are selected for synthesis, then the possibility of finding new materials is high, but the number of unsuccessful synthesis attempts increases
Solution Approach 1:
Before conducting actual synthesis, the system performs preliminary assessments of synthesizability and measurability for all candidate materials. This preliminary filtering identifies the most promising candidates that are likely to succeed in actual manufacturing, thereby maintaining high material discovery capability while reducing the number of unsuccessful synthesis attempts.
Solution Approach 2:
Instead of synthesizing all database candidates, the system performs partial synthesis on a selectively filtered subset of materials that meet predefined synthesizability and measurability thresholds. This partial action approach maintains versatility in material discovery while significantly reducing time loss from unsuccessful attempts.
3Ease of operation
If material selection considers only physical property parameters, then the selection process is straightforward, but the executability of synthesis in actual equipment is not considered
Solution Approach 1:
The system merges multiple evaluation criteria including physical property parameters, synthesizability assessment, and measurability assessment into a unified material selection process. By combining these previously separate considerations into an integrated evaluation framework, the system maintains operational simplicity while comprehensively considering executability of synthesis in actual equipment.
Data Source
AI summary
A synthetic material selection method according to the present disclosure performs material selection for selecting synthesis target materials based on material physical property information and execution possibility information of a database, instructs a control device to perform synthesis processing for the selected materials, and updates the execution possibility information of the database based on a result of the synthesis processing from the control device.