About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Hume-Rothery Symposium: Data-Driven Materials Discovery and Phase Stability
|
| Presentation Title |
Computational discovery of materials for energy storage |
| Author(s) |
Donald Siegel |
| On-Site Speaker (Planned) |
Donald Siegel |
| Abstract Scope |
The performance of most energy storage devices is strongly influenced by the properties of the materials at their core. Examples of devices where this assertion holds include batteries, thermal energy storage, and adsorbents for chemical fuels such as hydrogen and natural gas. Thus, the drive to improve a storage device often involves a search for better materials. While the periodic table is finite, the chemical space of potential new materials is essentially infinite. Techniques for guiding experimental synthesis efforts towards the most promising materials would therefore be of great value. This seminar will describe recent computational approaches to identifying promising materials for several energy storage applications. Techniques employed include quantum mechanical simulations, classical atomistics, empirical correlations, high-throughput screening, and machine learning. An example focusing on new salt hydrates for thermal energy storage will be described in detail. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Energy Conversion and Storage, |