报告时间：2019年3月4日（星期一）上午10:00

报告地点：催化基础国重楼国家重点实验室会议室

报告人：欧阳润海，上海大学材料基因组工程研究院特聘副研究员

　　报告摘要：

　　The materials-genome initiative has fostered high-throughput calculations and experiments, leading to large amount of materials data available in literature and databases. Identifying the trends and correlations hidden in the materials data and thereby accelerating materials discovery is the core of the emerging fourth paradigm of science for materials science: data-driven material discovery. In this regard, efficient data-driven approaches for descriptor identification are crucial, and many methods falling under the umbrella name of (big-) data analytics (e.g. data mining, machine learning, compressed sensing, etc.) have being developed and applied to the wealth of materials-science data. In this talk, Ouyang will introduce their recently developed novel data-driven method SISSO based on compressed sensing theory for identifying low-dimensional descriptors for materials’ properties and functions. Then he will show several successful applications of SISSO in materials science to demonstrate the efficiency, e.g. new tolerance factor for predicting the geometry stability of perovskite, physical descriptor for predicting the Gibbs energy of crystalline solids, materials map for predicting topological insulators. In addition, SISSO is expected to be an efficient tool for big-data catalysis.

　　报告人简介：

　　欧阳润海博士，上海大学材料基因组工程研究院特聘副研究员，上海市青年东方学者，于2013年毕业于中科院大连化物所理论催化课题组。他先后在澳大利亚悉尼大学、美国加州大学河滨分校、德国马普FHI研究所做博士后研究，并于2019年加入上海大学。特别地，在德国期间，他隶属于FHI理论部大数据材料科学课题组，主要研究方向是在欧盟H2020地平线计划NOMAD项目内发展大数据分析方法及数据驱动新材料预测。他目前的主要研究成果是在化物所期间与导师李微雪研究员发展的反应气氛下纳米催化材料Ostwald熟化理论，以及在德国与Scheffler教授和Ghiringhelli博士等人发展的基于压缩感知理论的数据驱动方法SISSO。他编写的SISSO并行程序免费供学术和商业使用(github.com/rouyang2017/SISSO)，可广泛用于材料、化学等领域数据驱动描述符或模型的建立及新材料预测。

　　联系人：502组 文杨

　　联系电话：84379765