主讲人：刘惠军，武汉大学教授
时 间：2024年11月1日16:00
地 点：物电学院A栋112
联系人：程龙
讲座摘要：Benefited from recent advances in big-data analytics, the machine learning method was proposed to accelerate discovery of materials with desired properties. In this talk, we apply several data-driven algorithms/strategies to establish high-throughput models that allows ready and accurate prediction on the Seebeck coefficient, the lattice thermal conductivity, and the ZT values of thermoelectric materials. Without any input from first-principles calculations, the models only require the information of crystal structures or fundamental properties of the constituent atoms, and can be readily generalized to systems drastically beyond the training data. Our work not only provides a large space for exploring high-performance thermoelectric materials, but also attests to the increasing importance of artificial intelligence-based approaches in modern materials discovery.
主讲人简介：刘惠军，武汉大学教授、博导。1995年及1998年在武汉大学分别获学士和硕士学位，2003年在香港科技大学获博士学位，2008年入选教育部“新世纪优秀人才支持计划”，2012年在美国University of Pittsburgh进行访问研究。长期从事计算凝聚态物理、计算材料科学的研究工作，特别是从第一性原理出发对材料的性质进行计算和设计新材料。现任中国材料研究学会计算材料学分会副秘书长、国际学术期刊Scientific Reports编委。先后主持了多项国家自然科学基金项目，并作为主要学术骨干参与了两项国家973计划项目。研究成果在Physical Review Letters、Physical Review B、Advanced Energy Materials、Applied Physics Letters、Materials Today Physics等期刊上发表论文130余篇。
