<｜begin▁of▁sentence｜>### Instruction:
As a Nanomaterials Engineer working on a new project for optimizing the structural blueprint of a novel nanomaterial, imagine we are looking at the atomic bonding patterns represented as an intricate graph-network of interactions between atoms. These connections, or 'edges', in the graph signify potential points of stability within the material. Specifically, for this project, we're examining a blueprint where atom-to-atom interactions can be described by the connections [(1, 2), (2, 3), (3, 4), (4, 1), (1, 3)].

To refine our design for maximum stability, we must identify the largest subset of interconnected atoms where each atom is bonded to all others within the group, akin to finding a 'maximum clique' in a graph-theoretic sense. Would you be capable of utilizing NetworkX or a similar computational tool to locate the most substantially interlinked cluster of atoms in this nanomaterial framework?
### Response:
import networkx as nx
from networkx.algorithms import approximation as approx

# Create an undirected graph
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 1), (1, 3)])

# Find the maximum clique
max_clique_set = approx.max_clique(G)

print('The maximum clique of the graph:', max_clique_set)
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