Update README.md

README.md

@@ -6,13 +6,24 @@ The domain-specific recipe language model (RLM) is developed for an emerging sci
 
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-## 2. Model Description
+## 2. Introduction
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+Materials innovation have been undergoing rapid development with the vast combinatorial exploration of recipes; however, the related research suffers from time-consuming trial-and-error synthesis and labour-intensive fabrication. As a promising alternative, robotics enables high-throughput experimentation and data collection; however, the resulting numerical datasets are often insufficiently analysed and fail to provide effective feedback for semantic recipe optimisation.
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+Here, we present a domain-specific recipe language model (RLM) developed for an emerging scientific tool of robotic boxes (perovskite solar cell research as a demonstration). For iterative fine-tuning of the RLM, seven artificial intelligence (AI) layers, including learning, generating, RecipeQA, fine-tuning, reasoning, evaluation, and optimisation, have been designed with a language agent. During the loops of seven AI layers, both numerical and semantic recipes were continuously learned and optimised for the RLM. Guided by this RLM, eleven robotic boxes executed the controllable synthesis, fabrication and characterisation of 50,764 samples. Simultaneously, more than 578 million tokens were generated and augmented to improve the ability to recommend a recipe and mechanistic reasoning, reaching a level comparable to that of an experienced researcher.
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+Therefore, the integration of the RLM with robotic boxes enables an AI and robotics discovery process in which specialised language modelling and modularised robotic hardware continuously improve one another, suggesting an evolution of physical AI for materials research.
+Here, we present a domain-specific recipe language model (RLM) developed for an emerging scientific tool of robotic boxes, with perovskite solar cell research as a demonstration. For iterative fine-tuning of the RLM, seven artificial intelligence (AI) layers, including learning, generating, RecipeQA, fine-tuning, reasoning, evaluation, and optimisation, have been designed with a language agent. During the loops of seven AI layers, both numerical and semantic recipes are continuously learned and optimised for the RLM.
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+---
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+## 3. Model Description
 
 To train this domain-specific RLM, the workflow starts from encoded formulas and parameters as recipe inputs, proceeds through seven AI layers with the language agent, and produces in situ characterisation and device performance assessment as mechanistic outputs. The language agent encodes these machine-readable recipes into structured formulas and parameters sequences, which are translated into tokens for subsequent fine-tuning of the RLM and execution by the robotic boxes. As a result, the fine-tuned RLM incorporates the encoded recipes, robotics, and characterised results to form a closed recommendation–synthesis–fabrication–characterisation–mechanism loop for exploring the large space of the recipes and their underlying mechanisms.
 
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-## 3. Key Features
+## 4. Key Features
 
 Recipe recommendation: the ability to generate recommended recipes with recipe integrity, formula rationality, parameter rationality, and experimental validity.
 
@@ -20,7 +31,7 @@ Mechanistic reasoning: the ability to provide mechanistic explanations with doma
 
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-## 4. Training Details
+## 5. Training Details
 
 The model is trained using a multi-stage pipeline:
 
@@ -37,7 +48,7 @@ The model is trained using a multi-stage pipeline:
 
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-## 5. Intended Use
+## 6. Intended Use
 
 RLM is designed to assist researchers, scientists, and industry professionals in recipe-driven domains that involve the vast combinatorial exploration of recipes and are often constrained by time-consuming trial-and-error synthesis and labour-intensive fabrication.