Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO nanoparticles.

In the present work a series of design rules are developed in order to tune the morphology of TiO nanoparticles through hydrothermal process. Through a careful experimental design, the influence of relevant process parameters on the synthesis outcome are studied, reaching to the develop predictive models by using Machine Learning methods. The models, after the validation and training, are able to predict with high accuracy the synthesis outcome in terms of nanoparticle size, polydispersity and aspect ratio.