Utilizing the Fast Inertial Relaxation Engine (FIRE) Algorithm to Simulate Random Elastic Networks

Studying the energy minima of systems has long been a goal of computer simulation. A handful of methods exist for this task, such as Steepest Descent and Microconvergence. The Fast Inertial Relaxation Engine is a molecular dynamics scheme used to find stable structural equilibrium configurations. The advantages of FIRE as opposed to other methods include its simplicity and speed, allowing for use in systems with many degrees of freedom. The goal of this project is to use established literature on FIRE to write simulation code of random elastic networks, often seen in biological systems, and further study their behaviors under varied conditions. The primary goal is an interconnected network with many nodes built using Delaunay Triangulation. The simulation code is written in Python, and due to its small size and simple algorithms can be run with minimal computing resources. The algorithm has shown promising results for random networks and hopefully will continue to provide new insights. Simulations using this algorithm are a powerful and efficient tool for discovering new mechanical behaviors, and in the future will hopefully be applied to more complex systems.