2024-06-19
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.
Article
Published version
English
13 p.
Royal Society of Chemistry
U.S. Department of Energy, Office of Science, Subaward by University of Minnesota, Project title: Development of Machine Learning and Molecular Simulation Approaches to Accelerate the Discovery of Porous Materials for Energy-Relevant Applications under Award Number DE-SC0023454 (UMN Subaward A010026303)
A. S.-R. and N. L. thank TotalEnergies (contract reference CT00001052) and the Spanish Ministry of Science and Innovation (PID2021-122516OB-I00) for funding,
D. G.-R. thanks the Spanish Ministry of Science and Innovation (reference PID2020-112806RB-I00) and European Union NextGenerationEU/PRTR (reference TED2021-132850B-I00) for funding.
CC BY 3.0
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