JH976/Perovskite-RL
JH976/Perovskite-RL is a 32 billion parameter, domain-adapted large language model based on Qwen3-32B, specifically fine-tuned for perovskite solar-cell additive engineering. It is trained to reason about additive molecules, defect passivation, and various stability-related mechanisms in perovskite photovoltaics. This model excels at literature-based reasoning and molecular additive hypothesis generation within this specialized scientific domain. It was developed by JH976 and optimized using supervised fine-tuning and GRPO reinforcement learning.
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Perovskite-RL: Domain-Adapted LLM for Perovskite Engineering
Perovskite-RL is a specialized 32 billion parameter language model, built upon the Qwen3-32B architecture, designed for the complex domain of perovskite solar-cell additive engineering. It has undergone a unique training pipeline involving supervised fine-tuning (SFT) and GRPO reinforcement learning to enhance its reasoning capabilities in this field.
Key Capabilities
- Mechanistic Reasoning: Trained to understand and reason about additive molecules, defect passivation, crystallization modulation, interfacial protection, ion migration, electronic effects, and stability mechanisms in perovskite materials.
- Additive Discovery Workflow: Integrates into a closed-loop discovery workflow for perovskite precursor additive identification, connecting literature-derived reasoning with additive candidate generation.
- Specialized Training Data: Fine-tuned on 90,749 SFT examples and 5,800 GRPO examples of curated perovskite-additive reasoning data, including literature-derived mechanism and molecular-property reasoning.
- Reinforcement Learning Optimization: Utilizes GRPO with reward signals focused on answer correctness, format compliance, content recall, and reasoning quality for mechanism-aware additive selection.
Performance
On a mechanism-consistency benchmark, Perovskite-RL achieved 78.1% accuracy (25/32), demonstrating its ability to identify paper-specific mechanistic explanations.
Intended Use Cases
- Perovskite additive mechanism analysis
- Molecular additive hypothesis generation
- Mechanistic descriptor generation
- Literature-based reasoning for perovskite photovoltaics
- Assisting computational screening workflows
Limitations
It is crucial to note that Perovskite-RL is a research tool and not a substitute for experimental validation. Generated suggestions may be chemically invalid or experimentally unsuitable, and the model may overstate mechanistic confidence. Outputs should be treated as hypotheses, not final scientific conclusions.