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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()
# Integraci贸n con simulaciones cu谩nticas
def quantum_simulation(material_structure):
from qiskit import QuantumCircuit
qc = QuantumCircuit(4)
qc.h(range(4))
qc.measure_all()
# Simulaci贸n de propiedades electr贸nicas
return execute(qc, backend=QasmSimulator()).result() |