English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Standard molecular dynamics (MD) and Monte Carlo (MC) simulation deals withspherical particles. Extending these standard simulation methodologies to thenon-spherical cases is non-trivial. To circumvent this problem, non-sphericalbodies are considered as a collection of constituent spherical objects. As thenumber of these constituent objects becomes large, the computational burden tosimulate the system also increases. In this article, we propose an alternativeway to simulate non-circular rigid bodies in two dimensions having pairwiserepulsive interactions. Our approach is based on a machine learning (ML) basedmodel which predicts the overlap between two non-circular bodies. The machinelearning model is easy to train and the computation cost of its implementationremains independent of the number of constituents disks used to represent anon-circular rigid body. When used in MC simulation, our approach providessignificant speed up in comparison to the standard implementation where overlapdetermination between two rigid bodies is done by calculating the distance oftheir constituent disks. Our proposed ML based MC method provided very similarstructural features (in comparison to standard implementation) of the systems.We believe this work is a very first step towards a time-efficient simulationof non-spherical rigid bodies.

Simulation of Noncircular Rigid Bodies: Machine Learning Based Overlap Calculation Technique with System Size Independent Computational Cost