2026-10-14T07:25:01Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33789853 2026-09-15T11:45:04Z portal_1197 item_type_3 month_year_09_2026

Data for Fast screening of homogeneous catalysis mechanisms using graph-driven searches and approximate quantum chemistry Christopher Robertson (6095033) Scott Habershon (1263891) Reaction mechanisms (Chemistry) -- Computer programs Homogeneous catalysis Organometallic compounds Nanoparticles Migrated from ePrints Computational methods for predicting multi-step reaction mechanisms, such as those found in homogeneous catalysis by organometallic complexes, are rapidly emerging as powerful tools to support experimental mechanistic insight. We have recently shown how a graph-driven sampling scheme can be successfully used to propose a series of candidate reaction mechanisms for nanoparticle catalysis; however, identifying the most-likely reaction mechanism amongst this candidate set in an efficient scheme remains a challenge. Here, we show how simple descriptors for each reaction path, calculated using quick semi-empirical quantum chemistry, enable identification of the mechanism, but only if one considers both thermodynamic and kinetic parameters of proposed reaction mechanisms. Successful application to cobalt-catalysed alkene hydroformylation is used to benchmark this strategy, and provides insight into remaining algorithmic challenges.<br><br>ASCII data files containing energetic data from Figures 3, 5, 6 and 7 of related publication 2019-12-18T00:00:00Z Dataset Dataset 10.82444/warw.33789853.v1 https://figshare.com/articles/dataset/Data_for_Fast_screening_of_homogeneous_catalysis_mechanisms_using_graph-driven_searches_and_approximate_quantum_chemistry/33789853 CC BY 4.0