Cai Huabing, Ren Qinghua, Gao Yi
Department of Chemistry, Shanghai University 99 Shangda Road Shanghai 200444 China
Shanghai Institute of Applied Physics, Chinese Academy of Sciences Shanghai 201800 China.
Nanoscale Adv. 2024 Apr 3;6(10):2623-2628. doi: 10.1039/d3na01119d. eCollection 2024 May 14.
Cerium clusters have been extensively applied in industry owing to their extraordinary properties for oxygen storage and redox catalytic activities. However, their atomically precise structures have not been studied because of the lack of a reliable method to efficiently sample their complex structures. Herein, we combined a neural network algorithm with density functional theory calculations to establish a high-dimensional potential to search for the global minimums of cerium oxide clusters. Using CeO as well as its reduced state CeO and oxidized state CeO with ultra-small dimensions of ∼1.0 nm as examples, we found that these three clusters adopt pyramid-like structures with the lowest energies, which was obtained by exploring 100 000 configurations in large feasible spaces. Further the neural network potential-enhanced molecular dynamics calculations indicated that these cluster structures are stable at high temperature. The electronic structure analysis suggested that these clusters are highly active and easily lose oxygen. This work demonstrated that neural network potentials can be useful for exploring the stable structures of metal oxide nanoclusters in practical applications.
铈簇因其在储氧和氧化还原催化活性方面的非凡特性而在工业中得到广泛应用。然而，由于缺乏一种可靠的方法来有效采样其复杂结构，它们的原子精确结构尚未得到研究。在此，我们将神经网络算法与密度泛函理论计算相结合，建立了一个高维势能来寻找氧化铈簇的全局最小值。以尺寸约为1.0纳米的超小尺寸的CeO及其还原态CeO和氧化态CeO为例，我们发现这三个簇采用能量最低的金字塔状结构，这是通过在大的可行空间中探索100000种构型获得的。此外，神经网络势能增强的分子动力学计算表明，这些簇结构在高温下是稳定的。电子结构分析表明，这些簇具有高活性且容易失去氧。这项工作表明，神经网络势能在实际应用中可用于探索金属氧化物纳米簇的稳定结构。
