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README.md

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+# RLM 
+
+## 1. Overview
+
+RLM (Recipe Language Model) is a domain-specific language model designed for recipe learning, recommendation and mechanistic reasoning in materials research. With perovskite solar cells used in this work as a demonstration system.
+
+---
+
+## 2. Model Description
+
+RLM (Recipe Language Model) is trained to understand and generate structured and natural-language experimental procedures for scientific research.
+
+The model supports:
+
+- Experimental recipe generation
+- Process parameter optimization
+- Performance prediction
+- Mechanism-aware reasoning
+
+It is coordinated by a language agent and iteratively improved through a seven-layer AI system.
+
+---
+
+## 3. Key Features
+
+- Domain-specific modeling for scientific experimentation
+- Recipe generation for experimental workflows
+- Mechanism-informed reasoning (physics + chemistry)
+- Closed-loop optimization with experimental feedback
+
+---
+
+## 4. Training Details
+
+The model is trained using a multi-stage pipeline:
+
+- Data sources:
+  - Scientific literature
+  - Experimental datasets
+  - Generated RecipeQA data
+- Training strategy:
+  - Supervised fine-tuning (SFT with LoRA)
+  - Preference optimization (DPO)
+- Data format:
+  - Question-answer (RecipeQA)
+  - Mechanism reasoning data (Chain-of-Thought)
+
+The training process is organized within a seven-layer AI architecture:
+Learning → Generating → RecipeQA → Fine-tuning → Reasoning → Evaluation → Optimization
+
+---
+
+## 5. Intended Use
+
+This model is intended for:
+
+- Materials science research
+- AI-assisted experimental design
+- Process optimization
+- Integration with laboratory automation systems
+
+---
+
+## 6. Limitations
+
+- The model does not replace real experimental validation
+- Predictions may be biased toward training data distribution
+- Mechanism explanations are learned approximations, not full physical simulations
+- Performance may degrade outside scientific experimentation domains
+