The reconstruction of electrical impedance tomography is a non-linear and ill-posed inverse issue. As a consequence of the non-linearity, the computing cost of a method is high, and regularisation and the most relevant observations must be utilized to minimize ill-posedness. Study: Machine learning enhanced electrical impedance tomography for 2D materials. In an article published in the journal Inverse Problems, a machine learning adaptive electrode selection technique was used to build and apply a unique approach to measurement enhancement. Altogether, this study showed how electrical impedance tomography (EIT) might be used for 2D materials and emphasized the importance of machine learning in both the numerical and computational components of electrical impedance tomography.