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Large-scale structure prediction of near-stoichiometric magnesium oxide based on a machine-learned interatomic potential: Crystalline phases and oxygen-vacancy ordering
Physical Review Materials ( IF 3.4 ) Pub Date : 2021-08-30 , DOI: 10.1103/physrevmaterials.5.083806
Hossein Tahmasbi 1 , Stefan Goedecker 2 , S. Alireza Ghasemi 1
Affiliation  

Using a fast and accurate neural network potential, we are able to systematically explore the energy landscape of large unit cells of bulk magnesium oxide with the minima hopping method. The potential is trained with a focus on the near-stoichiometric compositions, in particular on suboxides, i.e., MgxO1x with 0.50<x<0.60. Our extensive exploration demonstrates that for bulk stoichiometric compounds, there are several new low-energy rock-salt-like structures in which Mg atoms are octahedrally six-coordinated and form trigonal prismatic motifs with different stacking sequences. Furthermore, we find a dense spectrum of novel nonstoichiometric crystal phases of MgxO1x for each composition of x. These structures are mostly similar to the rock-salt structure with octahedral coordination and five-coordinated Mg atoms. Due to the removal of one oxygen atom, the energy landscape becomes more glasslike with oxygen-vacancy type structures that all lie very close to each other energetically. For the same number of magnesium and oxygen atoms, our oxygen-deficient structures are lower in energy if the vacancies are aligned along lines or planes than rock-salt structures with randomly distributed oxygen vacancies. We also found the putative global minima configurations for each composition of the nonstoichiometric suboxide structures. These structures are predominantly composed of MgO(111) layers of the rock-salt structure which are terminated with Mg atoms at the top and bottom and are stacked in different sequences along the z direction. Like for other materials, these Magnéli-type phases have properties that differ considerably from their stoichiometric counterparts such as high electrical conductivity.

中文翻译:

基于机器学习原子间势的近化学计量氧化镁的大规模结构预测:结晶相和氧空位排序

使用快速准确的神经网络势,我们能够通过最小值跳跃方法系统地探索块状氧化镁大晶胞的能量格局。潜力的训练重点是接近化学计量的成分,特别是低价氧化物,即,X1-X0.50<X<0.60. 我们的广泛探索表明,对于本体化学计量化合物,存在几种新的低能岩盐状结构,其中镁原子呈八面体六配位,并形成具有不同堆叠序列的三角棱柱基序。此外,我们发现了一系列新的非化学计量晶相X1-X 对于每个组合 X. 这些结构大多类似于具有八面体配位和五配位镁原子的岩盐结构。由于去除了一个氧原子,能量景观变得更像玻璃,氧空位型结构在能量上彼此非常接近。对于相同数量的镁和氧原子,如果空位沿线或平面排列,我们的缺氧结构的能量低于具有随机分布的氧空位的岩盐结构。我们还发现了非化学计量低氧化物结构的每种组成的假定全局最小值配置。这些结构主要由氧化镁(111) 岩盐结构层在顶部和底部终止于 Mg 原子,并沿不同的顺序堆叠 z方向。与其他材料一样,这些 Magnéli 型相具有与其化学计量对应物显着不同的特性,例如高导电性。
更新日期:2021-08-30
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