| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Late News Poster Session
|
| Presentation Title |
NOW ON-DEMAND ONLY – M-27: Machine Learning-enabled Framework for the Screening of Hydrogen Storage Materials |
| Author(s) |
Amit Singh Bundela, Rahul M R |
| On-Site Speaker (Planned) |
Amit Singh Bundela |
| Abstract Scope |
Design of new materials with enhanced hydrogen storage properties is needed for future energy applications. Recently the applicability of high entropy alloys (HEA) for hydrogen storage is getting wider attention due to their unique structure and properties. Identifying the right composition from the huge compositional space of HEAs is challenging. A database was developed based on the reported hydrogen storage materials and identified a HEA system in the current study. High throughput studies were carried out to develop the data to identify the right composition in the system. The data distribution was analyzed using various data analysis methods. Machine learning algorithms were used to identify the right composition from the high throughput data by incorporating the design parameters etc. The ML prediction is validated with experimental results, and the developed framework can accelerate the identification of Hydrogen storage materials. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Energy Conversion and Storage, High-Entropy Alloys, Computational Materials Science & Engineering |