| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
|
| Presentation Title |
Machine Learning Based Prediction of Cation Distribution in Complex Spinel Oxides as a Function of Processing Temperature |
| Author(s) |
Ying Fang, Siming Zhang, Guofeng Wang |
| On-Site Speaker (Planned) |
Ying Fang |
| Abstract Scope |
It has been found that both cation chemistry and degree of inversion play an important role in technically relevant properties of spinel oxides. In this study, we have developed and applied a machine learning based computational approach to predict the equilibrium cation distribution in multi-cation spinel oxides at high temperatures. The database was constructed to contain the density functional theory calculated energies of the spinel oxides with various cation distributions. We applied the machine learning techniques (i.e., linear regression and neural network) to find the relation between the system energy and structural features of the spinel oxides and performed the atomistic Monte Carlo simulations to predict the equilibrium cation distribution as a function of processing temperature for single spinel AB2O4 and double spinel AB2-xCxO4. Our predicted cation distributions for material systems of CoFe2O4, NiFe2O4, MgAl2O4, CuAl2O4, and MgAl2-xFexO4 are found to agree well with available experimental results. |