| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Hume-Rothery Symposium on First-Principles Materials Design
|
| Presentation Title |
Computational Materials Design and Discovery for Next-generation Solid-state Batteries |
| Author(s) |
Yan Eric Wang |
| On-Site Speaker (Planned) |
Yan Eric Wang |
| Abstract Scope |
Computational modeling based on density functional theory has become a cornerstone of materials design, by providing insights into fundamental processes that are not easily accessible in experiments, and enabling fast and efficient prediction even before material synthesis. Such predictive power has made computational modeling a critical tool to design new materials with desired properties and accelerate the development of next-generation batteries. In this talk, we will present recent findings in the physical and chemical design principles for solid-state materials with high ionic conductivity and stability. More specifically, I will discuss crystallographic features which would enable fast ionic transport in inorganic solids, and how high-throughput calculations can be applied with such features in the design and discovery of ionic conductors. I will also discuss our most recent efforts at Samsung, where we are developing tools for inorganic battery materials research by combining high-throughput computation and robotic synthesis machines. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Energy Conversion and Storage, Modeling and Simulation |