Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography

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.

Composition analysis of AU–AG and AU–CU solid solution alloys by Friedel’s periodic spherical oscillation model

In this work, a two-nearest-neighbor structure model, named the 3-1 model, of the face-centered cubic (FCC) solid solution alloy is found based on the Cowley short-range order parameter and the Friedel’s periodic spherical oscillated (FPSO) model. The proposed 3-1 model has high symmetry, high density, and large separation. The model error between the 3-1 model and the standard spherical periodic model is only 0.004 nm. Besides, the chemical composition formula of the present model is applied to analyze the common grade compositions of various alloys. This work shows that the 3-1 model has universality in mature industrial grades of Au–Ag and Au–Cu alloys, and provides a simplified method to design the composition of alloys.