By taking advantage of a nanospace-confined nanocrystal conversion protocol via high-temperature solid-state reaction within the SiO2 nanosphere, an in-depth study was conducted into the unique transformation behavior of the Au-CuO heterostructured nanocrystals (HNCs), which was discovered during the oxidative annealing of the embedded AuCu alloy nanocrystal (NC). The type of heterojuction structure of the oxidized AuCu NCs, between core@shell and heterodimer, could be determined by modulating either the annealing temperature (T ann) or Cu contents (P cu) in AuCu NCs; Au@CuO was generated only either at low temperature (T ann ≤ 250 °C) or with very low Cu contents (P cu = 2.1), whereas the Au/CuO heterodimer was obtained as a major product in most of the cases at relatively high heat treatment (>250 °C). The systematic investigation of the conversion between HNCs could elucidate the distinct evolution pathway of the Au/CuO heterodimer via the kinetically accessed Au@CuO, revealing the escaping motion of the encapsulated Au core, which is more facilitated through a thicker CuO shell. This also demonstrated the high thermal stability of the Au@CuO with a very thin shell thickness due to the insufficient compressive lattice stain on the CuO shell to drive the morphological transformation into the heterodimer. Moreover, the higher operational stability could be detected for the Au@CuO with the lowest Cu content during catalytic CO oxidation, which correlates with its resistance against the thermal deformation.

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Developing easy and customizable strategies for the directional structure modulation of multicomponent nanosystems to influence and optimize their properties are a paramount but challenging task in nanoscience. Here, we demonstrate highly controlled eccentric off-center positioning of metal−core in metal@silica core−shells by utilizing an in situ generated biphasic silica-based intraparticle solid− solid interface. In the synthetic strategy, by including Ca 2+ -ions in silica−shell and successive oxidative and reductive annealing at high temperature, a unique hairline−biphasic interface is evolved via the heat-induced concentric radial segregation of calcium silicate phase at the interior and normal silica phase at the exterior of core−shell, which can effectively arrest the outwardly migrating metal−core within rubbery calcium silicate phase, affording various eccentric core−shells, where core-positions are flexibly controlled by the annealing time and amounts of initially added Ca 2+ -ions. In the structure−property correlation study, the strategy allows fine-tuning of dipolar interaction-based blocking temperatures and magnetic anisotropies of different eccentric core−shells as the function of variable off-center distance of magnetic core without changing the overall size of nanoparticles. This work demonstrates the discovery and potential application of biphasic solid−solid media interface in controlling the heat-induced migration of metal nanocrystals and opens the avenues for exploiting the rarely studied high-temperature solid-state nanocrystal conversion chemistry and migratory behavior for directional nanostructure engineering.

Properties of solid-state materials depend on their crystal structures. In solid solution high entropy alloy (HEA), its mechanical properties such as strength and ductility depend on its phase. Therefore, the crystal structure prediction should be preceded to find new functional materials. Recently, the machine learning-based approach has been successfully applied to the prediction of structural phases. However, since about 80% of the data set is used as a training set in machine learning, it is well known that it requires vast cost for preparing a dataset of multi-element alloy as training. In this work, we develop an efficient approach to predicting the multi-element alloys' structural phases without preparing a large scale of the training dataset. We demonstrate that our method trained from binary alloy dataset can be applied to the multi-element alloys' crystal structure prediction by designing a transformation module from raw features to expandable form. Surprisingly, without involving the multi-element alloys in the training process, we obtain an accuracy, 80.56% for the phase of the multi-element alloy and 84.20% accuracy for the phase of HEA. It is comparable with the previous machine learning results. Besides, our approach saves at least three orders of magnitude computational cost for HEA by employing expandable features. We suggest that this accelerated approach can be applied to predicting various structural properties of multi-elements alloys that do not exist in the current structural database.

High-throughput virtual screening for crystals aims to discover new materials by evaluating the property of every virtual candidate in the database exhaustively. During this process, the major computational bottleneck is the costly structural relaxation of each hypothetical material on the large-scale dataset using density functional theory (DFT) calculations. Here, we present a generative domain translation framework that maps the unrelaxed structural domains to the relaxed domains, enabling data-driven structural translations. The model predicts the materials formation energy with a small mean absolute error without DFT relaxations, and furthermore can produce the atomic coordinates consistent with the DFT relaxed structures. The utility of the proposed concept is not restricted to the structural domains, and we expect that it can be extended to translate the domain of easy-to-compute properties into the domain of more difficult properties.