Perovskite-RL

Perovskite-RL is a domain-adapted large language model for perovskite solar-cell additive engineering. It is trained to reason about additive molecules, defect passivation, crystallization modulation, interfacial protection, ion migration, electronic effects, and stability-related mechanisms.

Perovskite-RL is one component of a closed-loop discovery workflow for perovskite precursor additive discovery. The workflow connects literature-derived mechanism reasoning, additive candidate generation, descriptor extraction, feedback evaluation, and iterative refinement.

The workflow is available at: https://github.com/WD928/LEAP

Model Details

• Base model: Qwen3-32B
• Training pipeline: supervised fine-tuning followed by GRPO reinforcement learning
• Training framework: ms-swift / Transformers / PEFT
• Primary domain: perovskite photovoltaics and molecular additive design

Training Data

Perovskite-RL was trained using curated perovskite-additive reasoning data.

• SFT training set: 90,749 examples
• SFT validation set: 1,000 examples
• GRPO dataset: 5,800 examples

The data include literature-derived mechanism reasoning, molecular-property reasoning, and additive-selection tasks.

Training Procedure

SFT

The base model was first fine-tuned with LoRA on instruction-response examples for perovskite additive reasoning.

Key settings:

• LoRA fine-tuning
• Learning rate: 3e-5
• Epochs: 2
• Batch size per device: 1
• Gradient accumulation: 16
• Scheduler: cosine
• Seed: 42

GRPO

The SFT model was further optimized with GRPO using reward signals designed for mechanism-aware additive selection.

Key settings:

• GRPO
• LoRA rank: 16
• LoRA alpha: 32
• LoRA dropout: 0.05
• Learning rate: 2e-5
• Epochs: 1
• Number of generations: 8
• Max length: 8192
• Reward focus: answer correctness, format compliance, content recall, and reasoning quality

Evaluation

On the mechanism-consistency benchmark:

Model	Accuracy
Perovskite-RL	25 / 32, 78.1%

The benchmark tests whether a model can identify paper-specific mechanistic explanations rather than relying only on generic materials-science priors.

Intended Use

Perovskite-RL is intended for research use in:

• perovskite additive mechanism analysis
• molecular additive hypothesis generation
• mechanistic descriptor generation
• literature-based reasoning for perovskite photovoltaics
• assisting computational screening workflows

Limitations

• The model is not a substitute for experimental validation.
• Generated additive suggestions may be chemically invalid, commercially unavailable, or experimentally unsuitable.
• The model may overstate mechanistic confidence when evidence is incomplete.
• Use outputs as hypotheses, not final scientific conclusions.

Citation

Please cite the associated arXiv preprint if you use this model:

https://arxiv.org/abs/2605.20242