Title of Submission
Materials Science and Engineering, PhD
Major Advisor Name
Type of Submission
The electroceramics industry largely relies on various time-consuming and expensive trial-and-error experiments to address new questions which often could otherwise be interpolated from published data. Towards this end, predictive models, which can be derived from empirical evidence, can greatly aid the direction of future development in a meaningful and cost-effective way. This work focuses on deriving predictive models based on empirical data collected for ceramic compounds with the perovskite crystal structure. Specifically, models were made for layered type ordering in the [(NayLi1-y)(1-3x)/2La(1+x)/2]TiO3 system and rocksalt ordering in Ba(Mg⅓Ta⅔)O3.