Data mining for isotope discrimination in atom probe tomography

Broderick, Scott; Bryden, Aaron; Suram, Santosh K.; Rajan, Krishna

doi:10.1016/j.ultramic.2013.02.001

Cited by 18 publications

(17 citation statements)

References 16 publications

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“…PCA operates by defining a linear combination of the descriptors that captures the most independent information in the dataset. [59][60][61] In this way, by ordering the new axes in terms of the corresponding information, the data may be described in fewer dimensions with a minimum loss of information. The relationships uncovered and the corresponding visualizations become more robust and interpretable.…”

Section: Trends Of Phase Stabilitymentioning

confidence: 99%

Computational discovery of stableM2AXphases

et al. 2016

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The family of layered Mn+1AXn compounds provides a large class of materials with applications ranging from magnets, to high-temperature coatings, to nuclear cladding. In this work, we employ a density-functional theory based discovery approach to identify a large number of thermodynamically stable Mn+1AXn compounds, where n = 1, M = Sc, Ti, V, Cr, Zr, Nb, Mo, Hf, Ta; A = Al, Si, P, S, Ga, Ge, As, Cd, In, Sn, Tl, Pb; and X = C, N. We calculate the formation energy for 216 pure M2AX compounds and 10,314 solid solutions, (MM )2(AA )(XX ), relative to their competing phases. We find that the 49 experimentally known M2AX phases exhibit formation energies less than 30 meV/atom. Among the 10,530 compositions considered, 3,140 exhibit formation energies below 30 meV/atom, most of which have yet to be experimentally synthesized. A significant subset of 301 compositions exhibits strong exothermic stability in excess of 100 meV/atom, indicating favorable synthesis conditions. We identify empirical design rules for stable M2AX compounds. Among the metastable M2AX compounds are two Cr-based compounds with ferromagnetic ordering and expected Curie temperatures around 75K. These results can serve as a map for the experimental design and synthesis of previously unknown M2AX compounds.

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Section: Trends Of Phase Stabilitymentioning

confidence: 99%

Computational discovery of stableM2AXphases

et al. 2016

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“…Other analyses of this material for different objectives are provided in the literature. [35][36][37] …”

Section: B Experimental Detailsmentioning

confidence: 99%

Impact of extreme electrical fields on charge density distributions in Al3Sc alloy

Loyola

Peralta

Broderick

et al. 2016

Journal of Vacuum Science &Amp; Technology A: Vacuum, Surfaces, and Films

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In this study, the authors investigated how extreme electrical fields affect charge distribution of metallic surfaces and bond character at the moment of evaporation. The surface structure and neighborhood chemistry were also studied as a function of various field evaporation pathways. Density functional theory (DFT) was used to model the surface bonding and charge distribution and then correlate the DFT results with experimental results by comparing the calculated evaporation fields with atom probe tomography measurements. The evaporation fields of different surface neighborhood chemistries in L12-Al3Sc were calculated, with the Sc atoms occupying the corners of a cubic unit cell and the Al atoms occupying the face centers. Al-Al surface atoms are found via DFT to be more likely to evaporate as dimers because of the Al-Al shared charge density. In contrast, Al-Sc evaporates as single ions due to the increased density localized around the Sc atom. This difference in evaporation behavior correlates with the resistance to degradation under extreme fields. This work allows better interpretation of the atom probe data by clarifying the relationship between different evaporation events and the role of surface and subsurface chemistry.

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“…In this "reduced" dimensional space, we can then seek to find patterns and associations of data that permit us to unravel the complexity and truly "mine" the rich information embedded in atom probe images and spectra. The process of reducing the dimensionality of data mathematically is one that has to be done carefully, to avoid losing or distorting true correlations between characteristics in the original data set [73,[131][132][133][134]. There are numerous techniques to accomplish this and in the following discussion we shall provide a couple of brief examples of the value of using such methods.…”

Section: Data Dimensionalitymentioning

confidence: 99%

Mining information from atom probe data

et al. 2015

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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.

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Data mining for isotope discrimination in atom probe tomography

Cited by 18 publications

References 16 publications

Computational discovery of stableM2AXphases

Computational discovery of stableM2AXphases

Impact of extreme electrical fields on charge density distributions in Al3Sc alloy

Mining information from atom probe data

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