  Standard molecular dynamics (MD) and Monte Carlo (MC) simulation deals with
spherical particles. Extending these standard simulation methodologies to the
non-spherical cases is non-trivial. To circumvent this problem, non-spherical
bodies are considered as a collection of constituent spherical objects. As the
number of these constituent objects becomes large, the computational burden to
simulate the system also increases. In this article, we propose an alternative
way to simulate non-circular rigid bodies in two dimensions having pairwise
repulsive interactions. Our approach is based on a machine learning (ML) based
model which predicts the overlap between two non-circular bodies. The machine
learning model is easy to train and the computation cost of its implementation
remains independent of the number of constituents disks used to represent a
non-circular rigid body. When used in MC simulation, our approach provides
significant speed up in comparison to the standard implementation where overlap
determination between two rigid bodies is done by calculating the distance of
their constituent disks. Our proposed ML based MC method provided very similar
structural features (in comparison to standard implementation) of the systems.
We believe this work is a very first step towards a time-efficient simulation
of non-spherical rigid bodies.
