Mechanistic Inference from Statistical Models at Different Data-Size Regimes,ACS Catalysis

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Mechanistic Inference from Statistical Models at Different Data-Size Regimes
ACS Catalysis ( IF 11.3 ) Pub Date : 2022-06-17 , DOI: 10.1021/acscatal.2c01741
Danilo M. Lustosa ₁ , Anat Milo ₁

Affiliation

The chemical sciences are witnessing an influx of statistics into the catalysis literature. These developments are propelled by modern technological advancements that are leading to fast and reliable data production, mining, and management. In organic chemistry, models encoded with information-rich parameters have facilitated the formulation of mechanistic hypotheses across different data-size regimes. Herein, we aim to demonstrate through selected examples that the integration of statistical principles into homogeneous catalysis can streamline not only reaction optimization protocols but also mechanistic investigation procedures. Namely, we highlight how different aspects of molecular modeling, data set design, data visualization, and nuanced data restructuring can contribute to improving chemical reactivity and selectivity, while furthering our understanding of reaction mechanisms. By mapping out these techniques at different data set sizes, we hope to encourage the broad application of data-driven approaches for mechanistic studies regardless of the accessible amount of data.

中文翻译：

来自不同数据大小机制的统计模型的机制推断

化学科学正在见证大量统计数据涌入催化文献。这些发展受到现代技术进步的推动，这些进步导致了快速可靠的数据生产、挖掘和管理。在有机化学中，使用信息丰富的参数编码的模型有助于在不同的数据大小范围内制定机械假设。在这里，我们旨在通过选定的例子证明，将统计原理整合到均相催化中不仅可以简化反应优化方案，还可以简化机械研究程序。也就是说，我们强调分子建模、数据集设计、数据可视化和细微数据重组的不同方面如何有助于提高化学反应性和选择性，同时加深我们对反应机理的理解。通过在不同的数据集大小上绘制这些技术，我们希望鼓励将数据驱动的方法广泛应用于机械研究，而不管可访问的数据量如何。

更新日期：2022-06-17

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