| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Discovery and Design of Materials
|
| Presentation Title |
Machine Learning Driven Discovery and Modeling of Materials for Hydrogen Storage and Generation |
| Author(s) |
Matthew Witman |
| On-Site Speaker (Planned) |
Matthew Witman |
| Abstract Scope |
Data-driven modeling in recent years has ushered in a new paradigm for rapid discovery and efficient modeling of materials across a plethora of domains in the physical and materials sciences. These methods become particularly invaluable when investigating applications of high-entropy materials, where the combinatorial growth of explorable chemical space makes brute-force experimentation or first-principles simulation intractable. This talk will survey a variety of data-driven discovery exemplars involving (high entropy) materials for hydrogen storage and generation. These range from traditional machine learning approaches for direct hydride thermodynamic property prediction to novel implementations of graph neural networks for direct prediction of defect thermodynamics. Such modeling strategies can rapidly screen materials or feed sampling intensive phase diagram calculations needed to wholistically evaluate the potential of candidates for hydrogen storage and generation across various use cases. |
| Proceedings Inclusion? |
Planned: |