Spinel Oxides Electrical Properties Prediction Model

events hall

Mr. Yuval Elbaz PhD candidate


David Wang Auditorium, 3rd floor Dalia Meidan Bldg.


Spinel oxides are used for a large variety of applications like electrodes of batteries, supercapacitors, and fuel cells. Spinel oxides’ electrical properties such as band gap and conductivity can be tuned by adjusting their building chemical elements and stoichiometry. The process of finding the desired electronic conductivity is done by trial and error in the lab and it is very time-consuming. Thus, there is a strong demand for predicting material properties prior to the experiment to accelerate the development and discovery of new materials.

In this work, we used density functional theory to calculate the band structure of 210 different spinel compositions. The band structure of each composition was fitted to a tight-binding Hamiltonian which was then used to make electrical current calculations under the non-equilibrium Green’s function scheme. Overall, in this workflow, we built a database of spinel compositions and their associate properties such as band gap and conductivity which were used as a target for various machine learning algorithms to predict. This study has shown an approach that combines numerous methodologies to search for a spinel composition with a desired property. By using this concept, one can look for the highest or lowest conductive spinel stochiometry in advance.


  • BSc in physics and materials engineering from the Technion.
  • MSc in Materials Science and Engineering – Technion

Advisor: Assoc. Prof. Maytal Caspary Toroker