Wei Yao, Santana-Bonilla Alejandro, Kantorovich Lev
Theory and Simulation of Condensed Matter (TSCM), King's College London, Strand, London WC2R 2LS, U.K.
ACS Appl Mater Interfaces. 2024 Nov 20;16(46):64177-64189. doi: 10.1021/acsami.4c13102. Epub 2024 Nov 6.
The development of novel subnanometer clusters (SNCs) catalysts with superior catalytic performance depends on the precise control of clusters' atomistic sizes, shapes, and accurate deposition onto surfaces. The intrinsic complexity of the adsorption process complicates the ability to achieve an atomistic understanding of the most relevant structure-reactivity relationships hampering the rational design of novel catalytic materials. In most cases, existing computational approaches rely on just a few structures to draw conclusions on clusters' reactivity thereby neglecting the complexity of the existing energy landscapes thus leading to insufficient sampling and, most likely, unreliable predictions. Moreover, modeling of the actual experimental procedure that is responsible for the deposition of SNCs on surfaces is often not done even though in some cases this procedure may enhance the significance of certain (e.g., metastable) adsorption geometries. This study proposes a novel systematic approach that utilizes global search techniques, specifically, the particle swarm optimization (PSO) method, in conjunction with ab initio calculations, to simulate all stages in the beam experiments, from predicting the most relevant SNCs structures in the beam and on a surface, to their reactivity. To illustrate the main steps of our approach, we consider the deposition of Molybdenum SNC of 6 Mo atoms on a free-standing graphene surface, as well as their catalytic properties with respect to the CO molecule dissociation reaction. Even though our calculations are not exhaustive and serve only to produce an illustration of the method, they are still able to provide insight into the complicated energy landscape of Mo SNCs on graphene demonstrating the catalytic activity of Mo SNCs and the importance of performing statistical sampling of available configurations. This study establishes a reliable procedure for performing theoretical rational design predictions.
开发具有卓越催化性能的新型亚纳米团簇（SNCs）催化剂，依赖于对团簇原子尺寸、形状的精确控制，以及精确沉积到表面上。吸附过程固有的复杂性，使得难以从原子层面理解最相关的结构 - 反应性关系，从而阻碍了新型催化材料的合理设计。在大多数情况下，现有的计算方法仅依靠少数结构来推断团簇的反应性，从而忽略了现有能量景观的复杂性，导致采样不足，很可能做出不可靠的预测。此外，即使在某些情况下，实际的将SNCs沉积在表面的实验过程可能会增强某些（例如亚稳态）吸附几何结构的重要性，但通常也没有对其进行建模。本研究提出了一种新颖的系统方法，该方法利用全局搜索技术，特别是粒子群优化（PSO）方法，并结合从头算计算，来模拟束流实验中的所有阶段，从预测束流中和表面上最相关的SNCs结构，到它们的反应性。为了说明我们方法的主要步骤，我们考虑了由6个钼原子组成的钼SNC在独立石墨烯表面上的沉积，以及它们对CO分子解离反应的催化性能。尽管我们的计算并不详尽，仅用于举例说明该方法，但它们仍然能够深入了解钼SNCs在石墨烯上复杂的能量景观，证明钼SNCs的催化活性以及对可用构型进行统计采样的重要性。本研究建立了一个用于进行理论合理设计预测的可靠程序。
