LLM Predicts Double Perovskite Space Groups with Few-Shot Learning
Key takeaways
- DyRIS is an LLM-agent framework for predicting space groups of double perovskites.
- It uses dynamic, diversity-enhanced few-shot prompting to handle imbalanced datasets.
- Rule-guided inference incorporates crystallographic domain knowledge for refinement.
- DyRIS significantly improves prediction accuracy for underrepresented "minor" space groups.
Who benefits
Summary
DyRIS, an LLM-agent-based framework, predicts space groups of double perovskites using dynamic and diversity-enhanced few-shot prompting combined with rule-guided inference. It achieves competitive overall accuracy and significantly improves performance on underrepresented "minor" space group classes in imbalanced materials datasets.
Why it matters
For materials scientists and chemists, DyRIS offers a powerful new AI tool to accelerate the discovery and design of novel double perovskite materials by accurately predicting their crucial structural properties, especially for rare or understudied compositions.
How to implement this in your domain
- 1Explore using LLM-agent frameworks like DyRIS for materials discovery tasks, especially with imbalanced datasets.
- 2Integrate domain-specific rules and expert knowledge into LLM-based prediction systems to enhance accuracy and reliability.
- 3Implement diversity-enhanced few-shot prompting strategies to improve model performance on minority classes.
- 4Apply this methodology to other scientific domains facing similar challenges with imbalanced data and complex predictions.
Original post by Jongwon Park, Inhyo Lee, Junhyeong Lee, Seunghwa Ryu
"arXiv:2608.10483v1 Announce Type: new Abstract: Double perovskites (DPs) offer broad compositional tunability, but predicting the space groups (SGs) of stable structures remains difficult because available datasets are often strongly imbalanced toward dominant SG classes. We refe…"
View on XOriginally posted by Jongwon Park, Inhyo Lee, Junhyeong Lee, Seunghwa Ryu on X · view source
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