| 报告题目 | Computational High-throughput Screening for Discovery of Novel Materials in Materials Genome Initiative |
| 报告人 | 王振斌 博士 |
| 报告人单位 | 加州圣地亚哥分校 |
| 报告时间 | 2017-06-28 |
| 报告地点 | 合肥微尺度物质科学国家实验室九楼会议室（9004） |
| 主办单位 | 合肥微尺度物质科学国家实验室 |
| 报告介绍 | Abstract：
Powered by methodological breakthroughs and computing advances, first-principles calculations under the framework of density functional theory have today become an indispensable toolkit in the materials designer’s arsenal. In this talk, I will discuss two emerging trends that holds the promise to continue to push the envelope in computational design of materials. The first trend is the development of robust software and data frameworks for the automatic generation, storage and analysis of materials data sets. The second is the advent of reliable central materials data repositories, such as the Materials Project, which provides the research community with efficient access to large quantities of property information that can be mined for trends or new materials. I will show how we have leveraged on these new tools to accelerate discovery and design in luminescent materials (e.g. phosphors) for next-generation solid-state lighting technology. I will also provide my perspective on future challenges in high-throughput computational materials design.
Biosketch：
Zhenbin Wang is a PhD candidate in Department of NanoEngineering at University of California, San Diego. He received his Bachelor and Master degree from Harbin Institute of Technology in 2011 and University of Science and Technology of China in 2014, respectively. His research interests are in the development of materials informatics approaches to create and analyze rich materials data and leverage them to discover new materials for solid-state lighting. |
