One of two-dimensional
transition metal dichalcogenide materials,
tungsten disulfide (WS
2
), has aroused much research interest,
and its mechanical properties play an important role in a practical
application. Here the mechanical properties of h-WS
2
and
t-WS
2
monolayers in the armchair and zigzag directions
are evaluated by utilizing the molecular dynamics (MD) simulations
and machine learning (ML) technique. We mainly focus on the effects
of chirality, system size, temperature, strain rate, and random vacancy
defect on mechanical properties, including fracture strain, fracture
strength, and Young’s modulus. We find that the mechanical
properties of h-WS
2
surpass those of t-WS
2
due
to the different coordination spheres of the transition metal atoms.
It can also be observed that the fracture strain, fracture strength,
and Young’s modulus decrease when temperature and vacancy defect
ratio are enhanced. The random forest (RF) supervised ML algorithm
is employed to model the correlations between different impact factors
and target outputs. A total number of 3600 MD simulations are performed
to generate the training and testing dataset for the ML model. The
mechanical properties of WS
2
(i.e., target outputs) can
be predicted using the trained model with the knowledge of different
input features, such as WS
2
type, chirality, temperature,
strain rate, and defect ratio. The mean square errors of ML predictions
for the mechanical properties are orders of magnitude smaller than
the actual values of each property, indicating good training results
of the RF model.