报告题目：Artificial intelligence-enhanced atom probe microscopy: Local chemical ordering analysis

基于人工智能增强的原子探针显微技术：局域化学有序性分析

报告人：李跃，德国洪堡学者（独立PI），德国马普学会可持续材料研究所项目组长、博士生导师

报告时间：2025年7月25日10:00-11:30

报告地点：秀山校区冶金楼南302学术报告厅（腾讯会议:220-644-892; 会议密码:202507）

报告对象：全校感兴趣师生

主办单位：冶金工程学院

报告人简介：李跃博士，德国洪堡学者，德国马普学会可持续材料研究所项目组长，博士生导师。2019年毕业于北京科技大学，获博士学位，2019年至今于德国马普学会可持续材料研究所（原马普钢铁所）从事博士后和德国洪堡学者研究。主要从事机器学习辅助的三维原子探针表征、轻质合金的智能设计等相关研究。截至目前作为第一或通讯作者发表知名SCI论文20篇,包括Adv. Mater. （封面论文）, Nat. Commun., Acta Mater.（4篇）和Prog. Mater. Sci.等，并作10多次国际会议的邀请报告。

报告内容简介：Chemical short-range order (CSRO), describing preferential local ordering of elements within the disordered matrix, can change the mechanical and functional properties of materials. CSRO is typically characterized indirectly, using volume-averaged (e.g. X-ray/neutron scattering) or through projection microscopy techniques that fail to capture the complex, three-dimensional atomistic architectures. Quantitative assessment of CSRO and concrete structure-property relationships have remained so far unachievable. Here, we present a machine-learning enhanced approach to break the inherent resolution limits of atom probe tomography to reveal three-dimensional analytical imaging of the size and morphology of multiple CSRO. We showcase our approach by addressing a long-standing question encountered in a body-centred-cubic Fe-18Al and Fe-19Ga (at.%) alloy that sees anomalous property changes upon heat treatment, supported by electron diffraction and synchrotron X-ray scattering techniques. The proposed strategy can be generally employed to investigate short/medium/long-range ordering phenomena in a vast array of materials and help design future high-performance materials.