rNets: a standalone package to visualize reaction networks†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-06-19 DOI:10.1039/D4DD00087K
Sergio Pablo-García, Raúl Pérez-Soto, Albert Sabadell-Rendón, Diego Garay-Ruiz, Vladyslav Nosylevskyi and Núria López
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Abstract

In the study of chemical processes, visualizing reaction networks is pivotal for identifying crucial compounds and transformations. Traditional methods, such as network schematics and reaction path linear plots, often struggle to effectively represent complex reaction networks due to their size and intricate connectivity. Alternatives capable of leading with complexity include graph methods, but they are not user-friendly, lacking simplicity and modularity, which hinders their integration with widely-used research software. This work introduces rNets an innovative tool designed for the efficient visualization of reaction networks with a user-friendly interface, modularity, and seamless integration with existing software packages. The effectiveness of rNets is demonstrated through its application in analyzing three catalytic reactions, showcasing its potential to significantly enhance research both in homogeneous and heterogeneous catalysis fields. This tool not only simplifies the visualization process but also opens new avenues for exploring complex reaction networks in diverse research contexts.

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rNets:可视化反应网络的独立软件包
在化学过程研究中,反应网络的可视化对于识别关键化合物和转化至关重要。传统的方法,如网络示意图和反应路径线性图,由于体积庞大、连接错综复杂,往往难以有效地表现复杂的反应网络。能够应对复杂性的替代方法包括图方法,但这些方法对用户不友好,缺乏简洁性和模块化,这阻碍了它们与广泛使用的研究软件的整合。本文介绍的 rNets 是一种创新工具,设计用于高效可视化反应网络,具有用户友好界面、模块化和与现有软件包无缝集成的特点。rNets 在分析三个催化反应中的应用证明了它的有效性,展示了它在显著提高均相和异相催化领域研究水平方面的潜力。该工具不仅简化了可视化过程,还为在不同研究背景下探索复杂反应网络开辟了新途径。
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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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