import torch
import torch.nn as nn
from torch_geometric.nn import GCNConv

class MaterialsGenerator(nn.Module):
    def __init__(self, latent_dim=512):
        super().__init__()
        self.encoder = nn.Sequential(
            GCNConv(128, 256),
            nn.ReLU(),
            GCNConv(256, latent_dim)
        
        self.decoder = nn.Sequential(
            nn.Linear(latent_dim, 1024),
            nn.ReLU(),
            nn.Linear(1024, 3)  # x,y,z coordinates
        )
    
    def forward(self, graph_data):
        encoded = self.encoder(graph_data)
        return self.decoder(encoded)
    
    def generate_novel_material(self, properties):
        with torch.no_grad():
            fake_graph = torch.randn(1, 128)
            return self(fake_graph).numpy()

# Integración con simulaciones cuánticas
def quantum_simulation(material_structure):
    from qiskit import QuantumCircuit
    qc = QuantumCircuit(4)
    qc.h(range(4))
    qc.measure_all()
    
    # Simulación de propiedades electrónicas
    return execute(qc, backend=QasmSimulator()).result()