Predicting <i>h-</i>BCN Geometric Structures Using Clustering and Regression Methods

Despite the fact that hexagonal graphene-like boron–carbon–nitrogen (h-BCN) monolayer, a synthesized material that has received a great deal of attention thanks to its intriguing properties and its potential application, there is no consensus on its geometric structure. We report here results of our machine learning approach that utilizes clustering and neural networks to find the lowest energy structure of h-BCN. Our dataset consists of 300 randomly generated h-BCN structures, optimized using density functional theory based calculations. To characterize the atomic environment of the atoms, a pattern recognition scheme based on neighbors was developed. We found that our model accurately predicts the total energy of h-BCN structure with a R-squared score as high as 0.85, depending on the number of k-means clusters used. We will also discuss the improvement of our predictions using a deep neural network.