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Tailoring the composition and morphology of electrocatalysts has been regarded as an effective alternative to improve their catalytic performance by forming and exposing more active sites. Herein a rapid thermal annealing (RTA) strategy is proposed to mass produce 2D cobalt-fluoride-oxide phase electrocatalysts in air at 400 °C (CFO-RH400) from crystalline fluoride hydrates, with critical steps including the thermal expansion of the water molecule, the exfoliation of CoF2 nanosheets, and the subsequent oxidation. The regulated electronic structure of the active cobalt oxide phase by sufficient fluoride anions leads to a closer O p-band center relative to the Fermi level based on our theoretical simulations. Therefore, the as-prepared 2D CFO-RH400 exhibits superior electrocatalytic activity and durability toward the oxygen evolution reaction.

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The tight-binding (TB) method is an ideal candidate for determining electronic and transport properties for a large-scale system. It describes the system as real-space Hamiltonian matrices expressed on a manageable number of parameters, leading to substantially lower computational costs than the ab-initio methods. Since the whole system is defined by the parameterization scheme, the choice of the TB parameters decides the reliability of the TB calculations. The typical empirical TB method uses the TB parameters directly from the existing parameter sets, which hardly reproduces the desired electronic structures quantitatively without specific optimizations. It is thus not suitable for quantitative studies like the transport property calculations. The ab-initio TB method derives the TB parameters from the ab-initio results through the transformation of basis functions, which achieves much higher numerical accuracy. However, it assumes prior knowledge of the basis and may encompass truncation error. Here, a machine learning method for TB Hamiltonian parameterization is proposed, within which a neural network (NN) is introduced with its neurons acting as the TB matrix elements. This method can construct the empirical TB model that reproduces the given ab-initio energy bands with predefined accuracy, which provides a fast and convenient way for TB model construction and gives insights into machine learning applications in physical problems.

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