用机器学习加速二氧化碳加氢能垒计算，发现反催化剂活性新规律。
Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $\mathrm{CO_2}$ hydrogenation energy barriers
- 构建神经网络势函数，高效模拟逆催化剂过渡态结构。
- 揭示纳米簇边缘与内部不同构型的催化活性趋势。
- 打破线性关联规律，解释实验中优异催化性能成因。
将CO₂转化为甲醇等有用化学品是缓解气候变化和减少化石燃料依赖的关键策略。开发新型催化剂成本高、耗时长，可通过计算方法探索潜在活性位点来加速进程。然而，材料复杂性和反应网络多样性使这一过程充满挑战。本文提出一种基于神经网络机器学习原子间势函数的工作流程，用于研究逆催化剂上关键甲酸中间体形成步骤的过渡态。以铟氧化物负载在Cu(111)上的纳米团簇为例，相比纯密度泛函理论方法，该方法实现显著提速，可广泛探测不同尺寸和化学计量比纳米团簇的活性位点。对获得的过渡态几何结构分析显示，纳米团簇边缘与内部存在不同的构效关系。此外，识别出的结构突破了传统线性缩放关系，这可能是实验中观察到的逆催化剂优异催化性能的关键原因。
原文摘要 · Abstract (English)
The conversion of $\mathrm{CO_2}$ into useful products such as methanol is a key strategy for abating climate change and our dependence on fossil fuels. Developing new catalysts for this process is costly and time-consuming and can thus benefit from computational exploration of possible active sites. However, this is complicated by the complexity of the materials and reaction networks. Here, we present a workflow for exploring transition states of elementary reaction steps at inverse catalysts, which is based on the training of a neural network-based machine learning interatomic potential. We focus on the crucial formate intermediate and its formation over nanoclusters of indium oxide supported on Cu(111). The speedup compared to an approach purely based on density functional theory allows us to probe a wide variety of active sites found at nanoclusters of different sizes and stoichiometries. Analysis of the obtained set of transition state geometries reveals different structure--activity trends at the edge or interior of the nanoclusters. Furthermore, the identified geometries allow for the breaking of linear scaling relations, which could be a key underlying reason for the excellent catalytic performance of inverse catalysts observed in experiments.
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