<｜begin▁of▁sentence｜>### Instruction:
Imagine you're developing a nanoscale communication network where each node represents a molecule and each directed edge indicates a transfer pathway for energy or information. In your design, the current pathways are as follows: energy transfer can occur from molecule 1 to 2 and 3, molecule 3 can transfer energy back to 1 and also to 2 and 5, molecule 4 can send energy to molecules 5 and 6, molecule 5 can reciprocate the energy transfer to molecule 4 and also send to molecule 6, and molecule 6 can transfer energy back to molecule 4.

To optimize the performance of this molecular network, you'd like to analyze its community structure by calculating the directed modularity matrix. This will help you understand the strength of divisions in your network and potentially improve energy transfer efficiency within molecular clusters. Could you transform the given inter-molecular energy transfer pathways into a directed modularity matrix using the NetworkX toolkit? This data-driven insight might guide future nanomaterials designs for more effective molecular communication systems.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
# Add edges
G.add_edges_from([(1,2), (1,3), (3,1), (3,2), (3,5), (4,5), (4,6), (5,4), (5,6), (6,4)])
# Calculate the directed modularity matrix
B = nx.directed_modularity_matrix(G)
print(B)
<|EOT|>
