Update README.md

README.md

@@ -7,8 +7,7 @@
 
 **A geometric deep learning framework for the inverse design of 3D crystal structures using E(n)-Equivariant Denoising Diffusion Probabilistic Models (DDPM).**
 
-![Streamlit Web App](cry_test.png)
-*(CrystalDiff interactive web application generating novel Perovskite structures in real-time.)*
+
 
 ---
 
@@ -66,35 +65,7 @@ To prove the model learned actual chemistry and not just random point cloud dist
 2. **Covalent Bonding (~1.9 Å):** The first sharp peak aligns perfectly with standard Titanium-Oxygen bond lengths, demonstrating the model learned local chemical environments from scratch.
 3. **Lattice Formation:** Secondary peaks (> 2.5 Å) confirm the generation of long-range repeating crystalline order.
 
----
 
-## 🚀 Installation & Usage
-
-### Prerequisites
-* Python 3.8+
-* PyTorch
-* Materials Project API Key (`mp-api`)
-
-### 1. Clone & Install
-```bash
-git clone [https://github.com/YOUR_USERNAME/CrystalDiff.git](https://github.com/YOUR_USERNAME/CrystalDiff.git)
-cd CrystalDiff
-pip install -r requirements.txt
-```
-### 2. Fetch the Dataset 
-```bash
-python src/data_loader.py
-```
-
-### 3. Train the Model 
-Train the Equivariant Diffusion model.
-```bash
-python train.py
-```
-### 4. Run the Web Application
-```bash
-streamlit run app.py
-```
 
 ## 📚 References & Inspiration