Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches. Accurate and efficient interatomic potentials are the key enabler, but their development remains a challenge for complex materials and/or complex phenomena. Machine learning potentials, such as the Deep Potential (DP) approach, provide robust means to produce general purpose interatomic potentials. Here, we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena, where general potentials do not suffice. As an example, we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium (in addition to other defect, thermodynamic and structural properties). The resulting DP correctly captures the structures, energies, elastic constants and γ-lines of Ti in both the HCP and BCC structures, as well as properties such as dislocation core structures, vacancy formation energies, phase transition temperatures, and thermal expansion. The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti. The approach to specialising DP interatomic potential, DPspecX, for accurate reproduction of properties of interest “X”, is general and extensible to other systems and properties.

Fe3O4 porous spheres are anchored onto carbon nanotubes (CNTs) to form three-dimension Fe3O4/CNTs nanocomposites, which exhibit remarkable EM microwave absorption performances with RL value of −51 dB at 5.52 GHz and effective bandwidth of 3.9 GHz.

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Novel uniform CoFe@C core–shell composite nanoparticles with good distribution have been fabricated through the combined self-assembly and controlled thermal decomposition of Co-based Prussian blue (PB) nanocubes..

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