报告摘要：

Materials design has entered the well-applauded machine learning era. In the near future, artificial systems would be able to predict materials with desired properties without human intervention and may outperform humans in various aspects. It is foreseen that many routine computational studies will be replaced by machines, which brings us a serious question, how can we survive in the machine learning era?  
In this talk, I will give some lessons learned from our recent computational design of two-dimensional boron sheets containing hyprcoordinate transition metals. If well adapted, we should be able to benefit from machine learning, which enables us to focus on more high-value and creative research. Meanwhile, we also need to prepare ourselves and retrain our students to welcome the emerging new materials prediction paradigm.

报告人简介：

陈中方教授1990年进入南开大学化学系学习，1994年获学士学位，2000年获得博士学位。随后在德国马普研究所 、Erlangen-Nürnberg大学和美国乔治亚大学从事博士后研究。2006年至2008年期间分别任职于美国乔治亚大学和伦斯勒理澳门太阳娱乐网站官网。2008年秋季起任美国波多黎各大学化学系副教授，2014年起任正教授。 陈中方博士研究方向为计算化学和计算纳米材料科学，注重理论与实验的紧密结合与互动。 近年主要致力于石墨烯和其他二维纳米材料，纳米催化剂，和纳米材料在环境治理中的应用等理论研究工作。已经发表论文240余篇，其中在Chem. Rev.、J. Am. Chem. Soc.、 Angew. Chem. Int. Ed.、 Phys. Rev. Lett. 发表论文30篇以上。 论文被引用超过10400次，个人h指数为54。