Application of machine learning model in material simulation using Python on Google Colab

Abstract

This study builds a machine learning model using artificial neural networks to train machine learning-based atomic interaction potentials from data by molecular dynamics simulations. The goal is to predict the formation energy of armchair-edge graphene nanoribbon with a missing carbon atom. The deep learning algorithm is implemented on the Google Colab platform using Python language.