Enthalpies of mixing of metallic systems relevant for lead-free soldering: Ag–Pd and Ag–Pd–Sn

Luef, Christoph; Paul, Aloke; Flandorfer, Hans; Kodentsov, AA Alexander; Ipser, Herbert

doi:10.1016/j.jallcom.2004.08.056

Cited by 22 publications

(4 citation statements)

References 8 publications

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“…5), although one proposed phase diagram guesses two solid-solid transitions, based on some reports of ostensibly ordered phases 58 , reliable evidence for first-order transformations in the solid state is lacking 69 . A careful review of relevant experimental literature [70][71][72][73][74][75][76] since 1961 (after ref. 58 ) suggests that, when Ag-Pd samples are annealed in the air or otherwise exposed to oxygen, the formation of oxide phases can be misinterpreted as the effects of ordering.…”

Section: Solid-solid Transitionmentioning

confidence: 99%

Machine-learned interatomic potentials for alloys and alloy phase diagrams

et al. 2021

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We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTPs) are polynomial-like functions of interatomic distances and angles. The Gaussian approximation potential (GAP) framework uses kernel regression, and we use the smooth overlap of atomic position (SOAP) representation of atomic neighborhoods that consist of a complete set of rotational and permutational invariants provided by the power spectrum of the spherical Fourier transform of the neighbor density. Both types of potentials give excellent accuracy for a wide range of compositions, competitive with the accuracy of cluster expansion, a benchmark for this system. While both models are able to describe small deformations away from the lattice positions, SOAP-GAP excels at transferability as shown by sensible transformation paths between configurations, and MTP allows, due to its lower computational cost, the calculation of compositional phase diagrams. Given the fact that both methods perform nearly as well as cluster expansion but yield off-lattice models, we expect them to open new avenues in computational materials modeling for alloys.

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Section: Solid-solid Transitionmentioning

confidence: 99%

Machine-learned interatomic potentials for alloys and alloy phase diagrams

et al. 2021

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“…In this work, we characterize the interfacial shear strength of Ag-X interfaces, where X=Pd, Cu, Ni, and Au whose heats of mixing are ᇞ ‫ܪ‬ ‫݃ܣ‬െܲ݀ ൎ ‫݈݉/ܬ݇8.4‪െ‬‬ [14], ᇞ ‫ܪ‬ ‫ݑܣ‪݃െ‬ܣ‬ ൎ ‫݈݉/ܬ݇6.4‪െ‬‬ [15], ο‫ܪ‬ ‫ݑܥ‪݃െ‬ܣ‬ ൎ ‫݈݉/ܬ݇6‬ [16], and ᇞ ‫ܪ‬ ‫݃ܣ‬െܰ݅ ൎ ‫/ܬ݇02‬ ‫. ]71[݈݉‬ The atomicmisfit strains between these materials are 5%, 0%, 11%, and 14%, respectively.…”

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confidence: 99%

Shear strengths of FCC-FCC cube-on-cube interfaces

et al. 2017

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This work employs in-situ transmission electron microscopy based nanopillar compression of physical vapor deposited multilayers to measure the interfacial shear strengths of model Ag-Pd, Ag-Au, Ag-Ni, Ag-Cu, and Cu-Ni (111) oriented cube-oncube interfaces. The experimental trends areprimarily rationalized by considering the coherency regimes.Coherent interfaces shear via slip transmission across them. Pillars containing semi-coherent interfaces with <5% atomic misfit deform rather homogeneously.Interfaces exhibitingatomic misfit >5% shear locally and their interfacial shear strength decreases with increasing misfit.

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“…Because the amount of material used in the experiments has to be limited (see above), the size of the solute samples and the number of drops that can be made is relatively small using this apparatus in comparison with others that have been employed (e.g. [8]). The initial size of the solvent bath was in the region of 0.2 g (0.0018 mol) and the solvent samples approximately 0.025 g (∼1.3 × 10 −4 mol).…”

Section: Enthalpies Of Mixingmentioning

confidence: 99%