| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
|
| Presentation Title |
Exploring the Properties of Grain Boundaries and Compositionally Complex Ceramics in High Dimensions |
| Author(s) |
Jian Luo |
| On-Site Speaker (Planned) |
Jian Luo |
| Abstract Scope |
In this talk, I will first review a series of studies to compute the grain boundary (GB) “phase” diagrams using thermodynamic models and atomistic simulations [Interdisciplinary Materials 2:137-160 (2023)]. To further extend prediction power, machine learning was used to predict GB properties as functions of five GB macroscopic (crystallographic) degrees of freedom plus temperature and composition for a binary alloy in a 7-D space [Materials Today 38:49 (2020)] or as functions of four independent compositional variables and temperature in a 5-D space for a given general GB in high-entropy alloys [Materials Horizons 9:1023-1035 (2022)]. In a second line of studies, we proposed to extend high-entropy ceramics (HECs) to compositionally complex ceramics to include non-equimolar compositions that can outperform their higher-entropy equimolar counterparts [Journal of Materials Science 55:9812-9827 (2020)]. Here, we use active learning to explore the microstructural and ionic properties of non-equimolar compositionally complex perovskites in high dimensions [unpublished results]. |