Application of machine learning for nanodiamonds shape and surface classification based on X-ray pattern analysis

Three Machine Learning algorithms, namely Random Forest, Neural Networks and Extreme Gradient Boosting, were applied to recognize the shape and surface structure of diamond nanoparticles from powder diffraction data. The algorithms were trained to recognize three types of shapes: 1D - rods, 2D - plates and 3D superspheres and, in the case of plate-like shapes, two types of (111) surfaces, with either one or three dangling bonds per surface atom. The classifiers' training was based on structure functions S(Q) of the nanograin models obtained by Molecular Dynamics simulations. The software tools for models building, diffraction data calculations, and the procedure of grain shape and surface classification are given. It is shown that both Random Forest, Neural Networks and Extreme Gradient Boosting classifiers recognize the shape and surface structure of nanodiamonds with a low number of misclassifications. The derived classifiers were applied to a series of experimental diffraction patterns of diamond nanoparticles with sizes from 1.2 to 3.3 nm. It is shown that ML classification algorithms reproduce very well the results obtained for those samples by real space diffraction data analysis, namely Pair Distribution Function. ML studies prove that the dominating shape of adamantane-synthesized nanodiamonds is a plate terminated by (111) surfaces with 3 dangling bonds of carbon atoms.