Zr-based bulk metallic glasses (BMGs) are amorphous alloys that can exhibit excellent mechanical properties, including high yield strength and fracture toughness. These properties are linked to local microstructural heterogeneities. Whether via microscopy-based techniques, synchrotron techniques, or calorimetric approaches, the amorphous structure of BMGs makes the characterisation of the details of these local structural and chemical heterogeneities extremely challenging. Our focus here is on atom probe tomography (APT), where considerable uncertainty remains in terms of how and when to apply this otherwise powerful technique to amorphous materials. This work reports a systematic evaluation of the experimental parameter space. We report results of BMG composition acquired against various APT operating parameters for Zr63.96Cu13.36Ni10.29Al11.04Nb1.25 (at. %). We demonstrate that a customised peak-based ranging approach yields satisfactory compositional accuracy with absolute errors of <1 at. %. Beyond composition, we have discussed the data quality in terms of attributes of the mass spectra: mass resolution, signal-to-thermal tail ratio, and overlapped peak ratio. We also assess the composition of the well-known clustered evaporation effects, common in APT data of BMGs. We conclude that these regions have negligible differences in composition from the surrounding “matrix” or bulk in these alloys.

Atom probe tomography, and related methods, probe the composition and the three-dimensional architecture of materials. The software tools which microscopists use, and how these tools are connected into workflows, make a substantial contribution to the accuracy and precision of such material characterization experiments. Typically, we adapt methods from other communities like mathematics, data science, computational geometry, artificial intelligence, or scientific computing. We also realize that improving on research data management is a challenge when it comes to align with the FAIR data stewardship principles. Faced with this global challenge, we are convinced it is useful to join forces. Here, we report the results and challenges with an inter-laboratory call for developing test cases for several types of atom probe microscopy software tools. The results support why defining detailed recipes of software workflows and sharing these recipes is necessary and rewarding: Open source tools and (meta)data exchange can help to make our day-to-day data processing tasks become more efficient, the training of new users and knowledge transfer become easier, and assist us with automated quantification of uncertainties to gain access to substantiated results.

Current reconstruction methodologies for atom probe tomography (APT) contain serious geometric artifacts that are difficult to address due to their reliance on empirical factors to generate a reconstructed volume. To overcome this limitation, a reconstruction technique is demonstrated where the analyzed volume is instead defined by the specimen geometry and crystal structure as determined by transmission electron microscopy (TEM) and diffraction acquired before and after APT analysis. APT data are reconstructed using a bottom-up approach, where the post-APT TEM image is used to define the substrate upon which APT detection events are placed. Transmission electron diffraction enables the quantification of the relationship between atomic positions and the evaporated specimen volume. Using an example dataset of ZnMgO:Ga grown epitaxially on c-plane sapphire, a volume is reconstructed that has the correct geometry and atomic spacings in 3D. APT data are thus reconstructed in 3D without using empirical parameters for the reverse projection reconstruction algorithm.

We define a measure for the accuracy of tomographic reconstruction in atom probe tomography, named here the spatial error index. We demonstrate that this index can be used to compare rigorously the spatial accuracy of various different approaches to the calculation of tomographic reconstruction. This is useful, for example, to evaluate the performance of alternate tomographic reconstruction approaches, and ensures that the comparisons are independent of individual data quality or other instrumental parameters. We then introduce a new “adaptive reconstruction” formalism that uses a progression of reconstruction parameters based on a per-atom correction from the cube root of the inverse of the voltage, along with linear correction factors linked to the evaporation sequence. We apply the measure for spatial accuracy to this new reconstruction protocol.

Current approaches to reconstruction in atom probe tomography produce results that exhibit substantial distortions throughout the analysis depth. This is largely because of the need to apply a multitude of assumptions when estimating the evolution of the tip shape, and other pseudo-empirical reconstruction factors, which vary both across the face of the tip and throughout the analysis depth. We introduce a new crystallography-mediated reconstruction to improve the spatial accuracy and dramatically reduce these in-depth variations. To achieve this, we developed a barycentric transform to directly relate atomic positions in detector space to real space. This is mediated by novel crystallographic analysis techniques, including: (1) calculating the orientation of a crystal directly from the field evaporation map, (2) tracking pole locations throughout the evaporation sequence, and (3) accounting for the evolving tip radius in a manner that removes the dependence on the geometric field factor. By improving the in-depth spatial accuracy of the atom probe reconstruction, a greater accuracy of the atomic neighborhood relationships is available. This is critical in modern materials science and engineering, where an understanding of the solid solution architecture, precipitate dispersions, and descriptions of the interfaces between phases or grains are key inputs to microstructure–property relationships.

We analyze phosphorus (P)- and boron (B)-doped silicon nanocrystals (Si NCs) with various compositions of silicon-rich oxide using atom probe tomography. By creating Si iso-concentration surfaces, it is confirmed that there are two types of Si NC networks depending on the amount of excess Si. A proximity histogram shows that P prefers to locate inside the Si NCs, whereas B is more likely to reside outside the Si NCs. We discuss the difference in a preferential location between P and B by a segregation coefficient.

Atom probe tomography (APT) represents a significant step toward atomic resolution microscopy, analytically imaging individual atoms with highly accurate, though imperfect, chemical identity and three-dimensional (3D) positional information. Here, a technique to retrieve crystallographic information from raw APT data and restore the lattice-specific atomic configuration of the original specimen is presented. This lattice rectification technique has been applied to a pure metal, W, and then to the analysis of a multicomponent Al alloy. Significantly, the atoms are located to their true lattice sites not by an averaging, but by triangulation of each particular atom detected in the 3D atom-by-atom reconstruction. Lattice rectification of raw APT reconstruction provides unprecedented detail as to the fundamental solute hierarchy of the solid solution. Atomic clustering has been recognized as important in affecting alloy behavior, such as for the Al-1.1Cu-1.7Mg (at. %) investigated here, which exhibits a remarkable rapid hardening reaction during the early stages of aging, linked to clustering of solutes. The technique has enabled lattice-site and species-specific radial distribution functions, nearest-neighbor analyses, and short-range order parameters, and we demonstrate a characterization of solute-clustering with unmatched sensitivity and precision.