dc.contributor.author
García, Sergio-Pablo
dc.contributor.author
Pérez-Soto, Raúl
dc.contributor.author
Sabadell-Rendón, Albert
dc.contributor.author
Garay-Ruiz, Diego
dc.contributor.author
Nosylevskyi, Vladyslav
dc.contributor.author
López, Núria
dc.date.accessioned
2024-06-28T08:51:26Z
dc.date.accessioned
2024-12-16T11:52:07Z
dc.date.available
2024-06-28T08:51:26Z
dc.date.available
2024-12-16T11:52:07Z
dc.date.issued
2024-06-19
dc.identifier.uri
http://hdl.handle.net/2072/537692
dc.description.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.
eng
dc.format.extent
13 p.
cat
dc.publisher
Royal Society of Chemistry
cat
dc.source
RECERCAT (Dipòsit de la Recerca de Catalunya)
dc.subject.other
Química
cat
dc.title
rNets: a standalone package to visualize reaction networks
cat
dc.type
info:eu-repo/semantics/article
cat
dc.type
info:eu-repo/semantics/publishedVersion
cat
dc.subject.udc
54 - Química
cat
dc.relation.projectID
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)
cat
dc.relation.projectID
A. S.-R. and N. L. thank TotalEnergies (contract reference CT00001052) and the Spanish Ministry of Science and Innovation (PID2021-122516OB-I00) for funding,
cat
dc.relation.projectID
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.
cat
dc.identifier.doi
https://doi.org/10.1039/D4DD00087K
dc.rights.accessLevel
info:eu-repo/semantics/openAccess