SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA.
Nanoscale. 2019 Mar 7;11(10):4438-4452. doi: 10.1039/c9nr00959k.
We present a simple approach for predicting the relative energies of bimetallic nanoparticles spanning a wide-ranging combinatorial space, using only the identity and nearest-neighbor coordination number of individual metal atoms as independent parameters. By performing straightforward metal atom adsorption calculations on surface slab models, we parameterize expressions for the energy of metal atoms as a function of their coordination number in 21 bimetallic pairings of fcc metals. We rigorously establish the transferability of our model by predicting relative energies of a series of nanoparticles across a large number of morphologies, sizes, atomic compositions, and arrangements. The model is particularly accurate in predicting atomic rearrangements at or near the metal surfaces, which is essential for its potential applications when studying segregation phenomena or dynamic processes in heterogeneous catalysis. By rapidly forecasting site stabilities with atomic specificity across generic structural and compositional features, our model is able to reverse engineer thermodynamically feasible motifs of active sites in bimetallic nanoparticles through robust property ⇔ structure relations.
我们提出了一种简单的方法，可以预测横跨广泛组合空间的双金属纳米粒子的相对能量，仅使用单个金属原子的身份和最近邻配位数作为独立参数。通过在表面平板模型上执行简单的金属原子吸附计算，我们将金属原子能量的表达式参数化为 21 种 fcc 金属双金属对中配位数的函数。我们通过预测大量形貌、尺寸、原子组成和排列的一系列纳米粒子的相对能量，严格确立了我们模型的可转移性。该模型在预测金属表面或附近的原子重排方面特别准确，这对于研究异相催化中的分凝现象或动态过程至关重要。通过快速预测具有原子特异性的站点稳定性，跨越通用结构和组成特征，我们的模型能够通过稳健的属性 ⇔ 结构关系反向设计双金属纳米粒子中活性位点的热力学可行基序。
