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The Blueprint of Exotic Perovskite in High-Throughput Screening

Yifeng Han  Mei-Huan Zhao  Martha Greenblatt  Man-Rong Li  
【摘要】:The machine-learning prediction of perovskite ABX_3 and A_2 BB'X_6 based on the geometric predictor(tolerance factor t) can distinguish an average of 74% of the classification accuracy(even worse for halide perovskite), which can be improved over 80% if considering the octahedral factor(μ = r_B/r_X) and atomic packing fraction(η = V_(atoms)/V_(cell)).~([1, 2]) The recent developed one-dimensional tolerance factor(τ) can τ=r_X/r_B-n_A(n_A-rA/rB/In(rA/rB))render 92% percent of the classification accuracy of perovskite phase, where n A presents the oxidation state of the A-cation and r_A is larger than r_B. Perovskite structure is likely to be the ground state in τ 4.18, in good agreement with the first-principles calculations.~([3]) However, as for exotic perovskite related phases with small A-cations, either t or τ is very similar giving the rather comparable ionic size of r_A and r_B, and thus new approaches are required to predict and guide the goal-orientated exploration, especially for the stringent preparation under high-pressure and temperature. Essentially, the polymorphs of exotic perovskites are determined by the ground-state energy from the synergetic effect of microscopically chemical(ionic size and valence, electron configuration, spin structure) and macroscopically thermodynamic(pressure and temperature) factors,~([4]) which can be well established by the firstprinciples calculations in line with Murnaghan equation of state for solids as manifested in LiSbO_3 and related materials.~([5]) Here we propose the crystal structure prediction of exotic perovskite oxides via high-throughput screening based on the perovskite-elemental table confined by charge-balance to guide the exploration of new multifunctional materials.

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