In this project, we use several machine learning algorithms to predict atom types from geometrical data of TiO2 nanoparticles. ".txt" files include geometrical data of TiO2 naoparticles produced at different temperatures with density-functional tight-binding (DFTB). "code.R" can be used to fit several machine learning methods to this data. The paper for this study is currently under-review.
Hasan Kurban, Mustafa Kurban
For any questions, please contact hakurban[at]gmail[dot]com