generate.py

import torch
import numpy as np
from src.model import CrystalDiffusionModel

# --- CONFIGURATION ---
# Select the atoms you want to generate!
# Example: BaTiO3 (Barium Titanate) -> Ba=56, Ti=22, O=8
# Example: SrTiO3 (Strontium Titanate) -> Sr=38, Ti=22, O=8
# Example: CaTiO3 (Calcium Titanate) -> Ca=20, Ti=22, O=8

# Let's try generating Strontium Titanate (SrTiO3) this time
TARGET_ATOMS = [38, 22, 8, 8, 8]  # Sr, Ti, O, O, O
MODEL_PATH = "model_weights.pth"
STEPS = 50  # Number of diffusion steps

def save_xyz(pos, z, filename):
    """
    Saves the crystal in XYZ format for visualization.
    """
    with open(filename, "w") as f:
        f.write(f"{len(pos)}\n")
        f.write("Generated by CrystalDiff\n")
        for i in range(len(pos)):
            # Simple periodic table lookup for common perovskite elements
            # You can add more if you generate other materials
            elem_map = {
                8: "O", 22: "Ti", 20: "Ca", 
                56: "Ba", 38: "Sr", 82: "Pb", 
                26: "Fe", 40: "Zr"
            }
            atom_symbol = elem_map.get(int(z[i]), "X") # Default to X if unknown
            f.write(f"{atom_symbol} {pos[i,0]:.4f} {pos[i,1]:.4f} {pos[i,2]:.4f}\n")

def generate():
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print(f"--- 💎 Generating Crystal on {device} ---")
    
    # 1. Load Model
    model = CrystalDiffusionModel().to(device)
    try:
        model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
    except FileNotFoundError:
        print(f"❌ Error: Could not find '{MODEL_PATH}'. Did you run train.py?")
        return
        
    model.eval()

    # 2. Setup Target Chemistry
    z = torch.tensor(TARGET_ATOMS, device=device)
    num_atoms = len(z)
    
    print(f"Target Atoms: {z.tolist()}")
    
    # Create fully connected graph
    row = torch.repeat_interleave(torch.arange(num_atoms), num_atoms)
    col = torch.arange(num_atoms).repeat(num_atoms)
    mask = row != col
    edge_index = torch.stack([row[mask], col[mask]], dim=0).to(device)

    # 3. Start with Pure Noise (The "Chaos")
    # We use a noise scale of 1.0 to match training
    x = torch.randn(num_atoms, 3, device=device)
    
    print(f"Initial State: Random Gas Cloud")
    save_xyz(x, z, "gen_step_00.xyz")
    
    # 4. The Reverse Diffusion Loop
    dt = 1.0 / STEPS
    
    for i in range(STEPS):
        # Time goes from 1.0 -> 0.0
        t_val = 1.0 - (i * dt)
        t_tensor = torch.tensor([[t_val]], device=device)
        
        with torch.no_grad():
            # Predict where the atoms SHOULD be
            x_pred = model(x, z, t_tensor, edge_index)
        
        # Update Position (Euler Integration)
        # We move 10% towards the prediction at each step for stability
        x = x + (x_pred - x) * 0.1 
        
        if i % 10 == 0:
            print(f"Step {i}/{STEPS}: Denoising...")
            save_xyz(x, z, f"gen_step_{i:02d}.xyz")

    # 5. Final Save
    print(f"✅ Final Structure Generated!")
    save_xyz(x, z, "gen_final.xyz")
    print("Check 'gen_final.xyz' to see your crystal.")

if __name__ == "__main__":
    generate()