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- W4381551138 abstract "We present our findings of a large-scale screening for new synthesizable materials in five M-Sn binaries, M = Na, Ca, Cu, Pd, and Ag. The focus on these systems was motivated by the known richness of M-Sn properties with potential applications in energy storage, electronics packaging, and superconductivity. For the systematic exploration of the large configuration space, we relied on our recently developed MAISE-NET framework that constructs accurate neural network interatomic potentials and utilizes them to accelerate ab initio global structure searches. The scan of over two million candidate phases at a fraction of the typical ab initio calculation cost has uncovered 29 possible intermetallics thermodynamically stable at different temperatures and pressures (1 bar and 20 GPa). Notable predictions of ambient-pressure materials include a simple hP6-NaSn$_2$ phase, fcc-based Pd-rich alloys, tI36-PdSn$_2$ with a new prototype, and several high-temperature Sn-rich ground states in the Na-Sn, Cu-Sn, and Ag-Sn systems. Our modeling work also involved ab initio (re)examination of previously observed M-Sn compounds that helped explain the entropy-driven stabilization of known Cu-Sn phases. The study demonstrates the benefits of guiding structure searches with machine learning potentials and significantly expands the number of predicted thermodynamically stable crystalline intermetallics achieved with this strategy so far." @default.
- W4381551138 created "2023-06-22" @default.
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- W4381551138 date "2023-06-16" @default.
- W4381551138 modified "2023-10-13" @default.
- W4381551138 title "Machine learning search for stable binary Sn alloys with Na, Ca, Cu, Pd, and Ag" @default.
- W4381551138 doi "https://doi.org/10.1039/d3cp02817h" @default.
- W4381551138 hasPublicationYear "2023" @default.
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