Due to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure-activity relations of catalysts. Herein, we unveil an anomalous entropic effect on catalysis via surface pre-melting of nanoclusters through machine learning accelerated molecular dynamics and free energy calculation. We find that due to the pre-melting of shell atoms, there exists a non-linear variation in the catalytic activity of the nanoclusters with temperature. Consequently, two notable changes in catalyst activity occur at the respective temperatures of melting for the shell and core atoms. We further study the nanoclusters with surface point defects, i.e. vacancy and ad-atom, and observe significant decrease in the surface melting temperatures of the nanoclusters, enabling the reaction to take place under more favorable and milder conditions. These findings not only provide novel insights into dynamic catalysis of nanoclusters but also offer new understanding of the role of point defects in catalytic processes.

中文翻译：

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机器学习分子动力学显示纳米团簇表面预熔化对催化的反常熵效应

由于优异的催化活性和贵金属的高效利用，纳米催化剂被广泛应用于现代化学品工业生产中。这些材料的表面结构受到反应性吸附物的显着影响，导致实验条件下的动态行为。动态性质对研究催化剂的结构-活性关系提出了重大挑战。在此，我们通过机器学习加速分子动力学和自由能计算，揭示了纳米团簇表面预熔化对催化的反常熵效应。我们发现，由于壳原子的预熔化，纳米团簇的催化活性随温度存在非线性变化。因此，在壳原子和核原子各自的熔化温度下，催化剂活性发生了两个显着的变化。我们进一步研究了具有表面点缺陷（即空位和ad原子）的纳米团簇，并观察到纳米团簇的表面熔化温度显着降低，使得反应能够在更有利和更温和的条件下进行。这些发现不仅为纳米团簇的动态催化提供了新的见解，而且还为点缺陷在催化过程中的作用提供了新的理解。