Update README.md

README.md

@@ -13,11 +13,11 @@ Model Type: Domain-Specific Large Language Model (LLM)
 
 Base Model: QwQ-32B
 
-Overview
+Overview:
 
 This model is a domain-specific large language model fine-tuned from QwQ-32B, specialized in the field of perovskite solar cells, particularly focusing on precursor additives. It is designed to assist researchers, engineers, and material scientists by providing knowledge, insights, and suggestions related to perovskite solar cell formulation, additive effects, and experimental design.
 
-Intended Use
+Intended Use:
 
 Research Support: Assist in understanding the role and mechanisms of precursor additives in perovskite solar cells.
 
@@ -27,13 +27,13 @@ Literature Summarization: Summarize and interpret scientific literature in the p
 
 Hypothesis Generation: Suggest potential novel additive strategies for optimization.
 
-Training Data
+Training Data:
 
 Fine-tuned on a curated dataset of academic papers and drug libraries related to perovskite solar cells and precursor additives.
 
 Data includes information on additive types, concentrations, processing conditions, device performance metrics, and observed effects.
 
-Model Architecture
+Model Architecture:
 
 Base: QwQ-32B
 
@@ -41,7 +41,7 @@ Fine-tuning: Instruction-tuning on domain-specific literature.
 
 Capabilities: Answer questions, generate explanations, summarize research findings, and provide guidance on additive selection in perovskite solar cells.
 
-Evaluation
+Evaluation:
 
 Domain Accuracy: Tested on domain-specific literature QA tasks and additive effect prediction tasks.
 
@@ -49,6 +49,6 @@ Human-in-the-Loop Validation: Recommendations cross-checked with experimental re
 
 Limitations: May hallucinate additive effects or interactions not reported in literature; not a replacement for experimental verification.
 
-Reference
+Reference paper:
 
 https://arxiv.org/abs/2507.16307