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.

Dr Yue Li, Alexander von Humboldt Fellow (2021-2024), Postdoc (2019-2021) at the Max Planck Institute for Sustainable Materials, Germany. Li received his doctorate degree in materials science and engineering from University of Science and Technology Beijing in 2019. His research interest is on machine learning enhanced atom probe microscopy, smart design of aluminum-rich alloys. Li has published 18 papers in well-known SCI journals as the first/corresponding author (such as Adv. Mater., Nat. Commun., Acta Mater., Prog. Mater. Sci.), and delivered more than 10 (invited) presentations in international conferences.

李跃博士，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）从事博士后研究，德国洪堡学者。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18篇, 包括Adv. Mater., Nat. Commun., Acta Mater.和Prog. Mater. Sci.等，并作10多次国际会议的邀请报告。