Atomic Coordinate Prediction of Carbon Nanotubes Using Regression Tree Ensembles

Document Type : Research Paper


Construction and building engineering technology, Al esraa university college, IRAQ


In this paper, regression tree ensembles (bagged and boosted) have been utilized in predicting atomic coordinate of Carbone nanotubes (CNTs). The aim of this study is to use ensembles classifiers to compute the atomic coordinates of Carbone nanotubes rather than other simulation tools. The dataset we used in this paper are provided by the UCI Repository of Machine Learning and it has a total of (10721) instances with (8) attributes (five as inputs and three as outputs) and it has no missing data. Various performance measures are also calculated to evaluate the classifiers we employed. The results show that there is a slight difference in performance between bagged and boosted trees, however, they are preferable classifiers for carbon atom coordinates prediction due to their high accuracy and short computation time. Using these predicted atomic coordinates as early coordinates for the simulation tool, the actual atomic coordinates can be retrieved in minutes or seconds instead of days by minimizing the iterations in the computation process.