A nexus between 3D atomistic data hybrids derived from atom probe microscopy and computational materials science: a new analysis of solute clustering in Al-alloys

Solute clusters affect the physical properties of alloys. Knowledge of the atomic structure of solute clusters is a prerequisite for material optimisation. In this study, solute clusters in a rapid-hardening Al-Cu-Mg alloy were characterised by a combination of atom probe tomography and density functional theory, making use of a hybrid data type that combines lattice rectification and data completion to directly input experimental data into atomistic simulations. The clusters input to the atomistic simulations are thus observed experimentally, reducing the number of possible configurations. Our results show that spheroidal, compact clusters are more energetically favourable and more abundant

The effect of high yttrium solute concentration on the twinning behaviour of magnesium alloys

The deformation behaviour of two single phase binary alloys, Mg-5Y and Mg-10Y, have been examined. In compression, two twin types were observed, the common {101¯2} twin as well as the less common {112¯1} extension twin. It is shown that the {112¯1} twin is much less sensitive to solute concentration than the {101¯2} twin, and it is suggested that the simple atomic shuffle of the {112¯1} twin reduces the solute strengthening imparted by Y additions. The common {101¯2} twin showed significant hardening as a result of alloying with Y. An analysis of solute behaviour has indicated that of the four chemical parameters investigated, i.e. atomic size, shear modulus, electronegativity and solute distribution, it appears to be the larger atomic radius of Y compared to Mg that increases the stress required to activate the {101¯2} twin. It is suggested that the large atomic radius inhibits the atomic shuffling process which accompanies the twinning shear in this twin type

A three-dimensional Markov field approach for the analysis of atomic clustering in atom probe data

Solute clustering is increasingly recognised as a significant characteristic within certain material systems that can be tailored to the optimization of bulk properties and performance. Atom probe tomography (APT) is emerging as a powerful tool for the detection of these nanoscale features; however, complementary to experiment, precise and efficient characterization algorithms are required to identify and characterise these nanoclusters within the potentially massive three-dimensional atomistic APT datasets. In this study, a new three-dimensional Markov field (3DMF) cluster identification algorithm is proposed. The algorithm is based upon an analysis of the direct atomic neighbourhood surrounding each atom, and the only input parameter required utilises known crystallographic properties of the system. Further, an array of statistical approaches has been developed and applied with respect to the results generated by the 3DMF algorithm including: an SN statistic, a two-tailed z-test, a difference measure, the ξ2 test, and a direct evaluation of the Warren-Cowley parameter for short-range ordering. Finally, the methodologies have been applied to the characterization of the nanostructural evolution of an Al-1.1Cu-0.5Mg (at.%) alloy subjected to a variety of heat treatments

Grain boundary segregation in Fe-Mn-C twinning-induced plasticity steels studied by correlative electron backscatter diffraction and atom probe tomography

We report on the characterization of grain boundary (GB) segregation in an Fe-28Mn-0.3C (wt.%) twinning-induced plasticity (TWIP) steel. After recrystallization of this steel for 24 h at 700 °C, ∼50% general grain boundaries (GBs) and ∼35% Σ3 annealing twin boundaries were observed (others were high-order Σ and low-angle GBs). The segregation of B, C and P and traces of Si and Cu were detected at the general GB by atom probe tomography (APT) and quantified using ladder diagrams. In the case of the Σ3 coherent annealing twin, it was necessary to first locate the position of the boundary by density analysis of the atom probe data, then small amounts of B, Si and P segregation and, surprisingly, depletion of C were detected. The concentration of Mn was constant across the interface for both boundary types. The depletion of C at the annealing twin is explained by a local change in the stacking sequence at the boundary, creating a local hexagonal close-packed structure with low C solubility. This finding raises the question of whether segregation/depletion also occurs at Σ3 deformation twin boundaries in high-Mn TWIP steels. Consequently, a previously published APT dataset of the Fe-22Mn-0.6C alloy system, containing a high density of deformation twins due to 30% tensile deformation at room temperature, was reinvestigated using the same analysis routine as for the annealing twin. Although crystallographically identical to the annealing twin, no evidence of segregation or depletion was found at the deformation twins, owing to the lack of mobility of solutes during twin formation at room temperature

Mining information from atom probe data

Whilst atom probe tomography (APT) is a powerful technique with the capacity to gather information containing hundreds of millions of atoms from a single specimen, the ability to effectively use this information creates significant challenges. The main technological bottleneck lies in handling the extremely large amounts of data on spatial-chemical correlations, as well as developing new quantitative computational foundations for image reconstruction that target critical and transformative problems in materials science. The power to explore materials at the atomic scale with the extraordinary level of sensitivity of detection offered by atom probe tomography has not been not fully harnessed due to the challenges of dealing with missing, sparse and often noisy data. Hence there is a profound need to couple the analytical tools to deal with the data challenges with the experimental issues associated with this instrument. In this paper we provide a summary of some key issues associated with the challenges, and solutions to extract or "mine" fundamental materials science information from that data