Machine-learning aided materials design
In materials, understanding and predicting the composition–structure–property relationship is the key to developing novel materials. Such predictions are typically hindered by the complex physics happening at different length and time scales, along with the large number of structural and compositional arrangements possible. As an alternative route, data-driven approaches such as machine learning can prove key to predict structure and composition of materials for tailored applications. The aim of this research is to rely on the large database available in the literature from previous experiments and simulations to design and test new compositions and structures of materials for targeted applications.
