Invited Speaker：Prof. Turab Lookman

Instroduction：T. Lookman教授是美国洛斯阿拉莫斯国家实验室 (LANL) 的Research Fellow。其研究领域主要集中在材料信息学，利用理论模拟的手段来探索铁性智能材料相变行为的物理机制。共发表论文250余篇，包括Nature(2), Nature Communication (4)， PNAS(1) , Review of Modern Physics (1) , PRL(20)，Acta Materialia (18)等，论文被引5000多次，h因子为33。2009年获得了LANL Fellows Prize，2012年当选美国物理学会的会士。近几年来，T. Lookman教授主要专注于材料基因组技术领域的研究，主持了美国能源部资助的首个材料基因组研究专项课题，并首次提出了利用机器学习加速开发具有目标性能材料的新方法。研究成果被MRS Bulletin, News & Analysis栏目以“Adaptive design loop ushers exploration of new materials”为题专题推介（Liu, Y. MRS Bulletin, 41, 424，2016）。美国国家标准技术局材料基因计划负责人James Warren 教授指出“这一方法为新材料的设计开发提供了最好的统计学方法”，“这一方法，特别是它的成功应用，会实现从新材料到实际应用的整个材料研发过程的加速。”

【Title】Data and Discovery: Accelerated search for materials with targeted properties

Time:  10:00-12:00 am, May 18th, 2018

Location:  新材料大楼材料学院第一会议室

Abstract：Finding new materials with targeted properties with as few experiments as possible has been a key goal of the materials genome initiative, now expanding in several countries. The enormous complexity due to the interplay of structural, chemical and microstructural degrees of freedom in materials makes the rational design of new materials rather difficult. Machine learning and optimization, used in industry for solving complex problems, are increasingly being adapted for the design of new materials by rapidly learning from past data and making smart decisions. However, the number of well characterized samples available as sources of data to learn from is often typically small. I will review some of the examples we have examined, including guiding experiments for more desirable alloys and ceramics and the use of high throughput electronic structure calculations to find new perovskites. I will end by showing how coherent diffraction data from large scale facilities are allowing us to image 3D vortices in ceramics.