About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Discovery and Design of Emerging Materials
|
Presentation Title |
Machine Learning Guided Search for Single Phase High Entropy Oxides |
Author(s) |
Shruba Gangopadhyay, Prasanna Balachandran |
On-Site Speaker (Planned) |
Shruba Gangopadhyay |
Abstract Scope |
High entropy oxides (HEOs) are made up of (near) equi-molar solid solutions of four or more complex oxides (e.g., binary oxides) that forms a single phase, likely stabilized by configuration entropy. One of the intriguing characteristics of this materials class is that not all “parent” oxides have the same structure. Further, there are insights in the literature that indicate that the HEO is in a metastable state. This presents a formidable challenge for materials discovery because of the lack of a priori design rules for guiding experiments and the vast search space (~200,000) of possible oxide combinations. A data-driven machine learning (ML) approach will be discussed to accelerate the search for novel single phase HEOs. We constructed a dataset from surveying the literature and explored several ML strategies (unsupervised and supervised learning methods). New single phase HEOs are also predicted, which we recommend for experimental synthesis. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |