%VBur index and steric maps: from predictive catalysis to machine learning
dc.contributor.author
dc.date.accessioned
2024-03-20T13:55:40Z
dc.date.available
2024-03-20T13:55:40Z
dc.date.issued
2014-01-21
dc.identifier.issn
0306-0012
dc.identifier.uri
dc.description.abstract
Steric indices are parameters used in chemistry to describe the spatial arrangement of atoms or groups of atoms in molecules. They are important in determining the reactivity, stability, and physical properties of chemical compounds. One commonly used steric index is the steric hindrance, which refers to the obstruction or hindrance of movement in a molecule caused by bulky substituents or functional groups. Steric hindrance can affect the reactivity of a molecule by altering the accessibility of its reactive sites and influencing the geometry of its transition states. Notably, the Tolman cone angle and %VBur are prominent among these indices. Actually, steric effects can also be described using the concept of steric bulk, which refers to the space occupied by a molecule or functional group. Steric bulk can affect the solubility, melting point, boiling point, and viscosity of a substance. Even though electronic indices are more widely used, they have certain drawbacks that might shift preferences towards others. They present a higher computational cost, and often, the weight of electronics in correlation with chemical properties, e.g. binding energies, falls short in comparison to %VBur. However, it is worth noting that this may be because the steric index inherently captures part of the electronic content. Overall, steric indices play an important role in understanding the behaviour of chemical compounds and can be used to predict their reactivity, stability, and physical properties. Predictive chemistry is an approach to chemical research that uses computational methods to anticipate the properties and behaviour of these compounds and reactions, facilitating the design of new compounds and reactivities. Within this domain, predictive catalysis specifically targets the prediction of the performance and behaviour of catalysts. Ultimately, the goal is to identify new catalysts with optimal properties, leading to chemical processes that are both more efficient and sustainable. In this framework, %VBur can be a key metric for deepening our understanding of catalysis, emphasizing predictive catalysis and sustainability. Those latter concepts are needed to direct our efforts toward identifying the optimal catalyst for any reaction, minimizing waste, and reducing experimental efforts while maximizing the efficacy of the computational methods
dc.description.sponsorship
We thank the Spanish Ministerio de Ciencia e Innovación for project PID2021-127423NB-I00 and the Generalitat de Catalunya for project 2021SGR623. A. P. is a Serra Húnter Fellow and ICREA Academia Prize 2019. S. E. thanks Universitat de Girona and DIPC for an IFUdG2019 PhD fellowship
Open Access funding provided thanks to the CSUC agreement with Royal Society of Chemistry (RSC)
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Royal Society of Chemistry (RSC)
dc.relation
PID2021-127423NB-I00
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1039/D3CS00725A
dc.relation.ispartof
Chemical Society Reviews, 2024, vol. 53, p. 853-882
dc.relation.ispartofseries
Articles publicats (D-Q)
dc.rights
Attribution 4.0 International
dc.rights.uri
dc.title
%VBur index and steric maps: from predictive catalysis to machine learning
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.relation.projectID
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-127423NB-I00/ES/CATALISIS PREDICTIVA PARA CAMBIAR EL ODEN SECUENCIAL ENTRE EXPERIMENTOS I CALCULOS/
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
038225
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.FundingProgramme
dc.relation.ProjectAcronym
dc.identifier.eissn
1460-4744