Abstract
Chemical short-range order (CSRO) refers to atoms of specific elements self-organising within a disordered crystalline matrix. These particular atomic neighbourhoods can modify the mechanical and functional performances of materials 1-6. CSRO is typically characterized indirectly, using volume-averaged (e.g. X-ray/neutron scattering) 2,7,8 or through projection (i.e. two-dimensional) microscopy techniques 5,6,9,10 that fail to capture the complex, three-dimensional atomistic architectures. Quantitative assessment of CSRO and concrete structure-property relationships remain 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 (at.%) solid solution alloy that sees anomalous property changes upon heat treatment 2. After validating our method against artificial data for ground truth, we unearth non-statistical B2-CSRO (FeAl) instead of the generally-expected D03-CSRO (Fe3Al) 11,12. We propose quantitative correlations among annealing temperature, CSRO, and the nano-hardness and electrical resistivity, supported by atomistic simulations. 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.
Evaluation on Sports Facility Resource in Primary School Using a Combined Approach of Unsupervised Machine Learning: a Case Study of Shanghai, China
