The NIST Chemical Kinetics Database includes essentially all reported kinetics results for thermal gas-phase chemical reactions. The database is designed to be searched for kinetics data based on the specific reactants involved, for reactions resulting in specified products, for all the reactions of a particular species, or for various combinations of these. In addition, the bibliography can be searched by author name or combination of names. The database contains in excess of 38,000 separate reaction records for over 11,700 distinct reactant pairs. These data have been abstracted from over 12,000 papers with literature coverage through early 2000. Rate constant records for a specified reaction are found by searching the Reaction Database. All rate constant records for that reaction are returned, with a link to 'Details' on that record. Each rate constant record contains the following information (as available): a) Reactants and, if defined, reaction products; b) Rate parameters: A, n, Ea/R, where k = A (T/298)*n exp[-(Ea/R)/T], where T is the temperature in Kelvins; c) Uncertainty in A, n, and Ea/R, if reported; d) Temperature range of experiment or temperature range of validity of a review or theoretical paper; e) Pressure range and bulk gas of the experiment; f) Data type of the record (i.e., experimental, relative rate measurement, theoretical calculation, modeling result, etc.). If the result is a relative rate measurement, then the reaction to which the rate is relative is also given; g) Experimental procedure, including separate fields for the description of the apparatus, the time resolution of the experiment, and the excitation technique. A majority of contemporary chemical kinetics methods are represented. The Kinetics Database is being expanded to include other resources for the convenience of the users. Presently this includes direct links to the corresponding NIST WebBook page for all substances for which such a link is possible. This is indicated by underling and highlighting the species. The WebBook provides thermodynamic, spectral, and other data on the species. Note that the link to the WebBook is opened as a new frame in your browser.

The NDRL/NIST Solution Kinetics Database contains data on rate constants for solution-phase chemical reactions. The database is designed to be searched by reactants, products, solvents, or any combination of these. In addition, the bibliography may be searched by author name, title words, journal, page(s), and/or year. This is not the same database as the one at Notre Dame, although both databases share a common data source.

This dataset consists of rate constants, half-lives and activation energy values for hydrolysis of organic chemicals compiled from journal publications and regulatory reports. The dataset was used to develop a ranked library of transformation reaction schemes for abiotic hydrolysis of organic chemicals under environmentally relevant conditions. The spreadsheet file HydrolysisRateConstants_est6b05412.xlsx contains a compilation of 187 literature-reported hydrolysis half-lives adjusted to pH 5, 7 and 9 and temperature of 25°C. The spreadsheet file HydrolysisActivationEnergy_est6b05412.xlsx contains a compilation of 58 literature-reported activation energies for hydrolysis reaction schemes. This dataset is associated with the following publication: Stevens, C., J. Patel, J. Jones, and E. Weber. Prediction of Hydrolysis Products of Organic Chemicals under Environmental pH Conditions. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 51(9): 5008–5016, (2017).

This dataset contains information on each of the 14 reactions used in the paper, the geometries for these reactions, the product of the quantum reaction rate constant and canonical reactant partition function and the flux-flux correlation function time series values for each reaction-temperature combination.

reaction_details.csv

This is a .csv file containing additional details on the reactions used in this paper. Each row contains one reaction/temperature combination, of which there are 55.

Column descriptions:

reaction_number: Reaction identifier number used in this work

reaction: The chemical reaction equation

metal_surface: atomic symbol of metal surface

facet_number: Miller indices of surface

reactants: Python dictionary object of reactants and their quantities

products: Python dictionary object of products and their quantities

reaction_energy [eV]: reaction energy in electron-volts

activation_energy [eV]: activation energy of reaction in electron-volts

temperature [K]: The randomly assigned temperature a calculation was run for

kQ_Cff [1/au]: The calculated integrated reaction rate product at corresponding temperature {1,2,3,4} in units 1/(au time).

reaction_split: Train/test placement of that reaction/temperature combination for reaction split

temperature_split: Trian/test placement of that reaction/temperature combination for temperature split

catalysishub_reactionID: Catalysis Hub reaction ID identifier for referencing catalysis hub database

doi: digital object identifier of original publication for which DFT calculations were performed

Flux_flux_correlation_functions:

Directory containing flux-flux correlation function time series values for each reaction temperature combination. Values are organized in subdirectories, one for each of the 14 reaction. In each subdirectory .csv files are labeled by reaction number and temperature in Kelvin. Each csv file contains a column with time points [au of time] and the corresponding flux-flux correlation function value in units [1/(au of time)2].

Geometries:

Directory containing geometry files for each reaction. Geometries of reactants on the surface were shifted respect to those supplied by catalysis hub to create continuous reaction pathways where necessary. Geometry files are organized in subdirectories for each reaction. When complete nudged elastic band (NEB) minimum energy paths (MEP) were not available ,subdirectories contain a products.xyz, reactants.xyz, and TSstar.xyz file (reactions 1 to 11) otherwise the complete set of NEB MEP images labeled neb{n}.xyz is given (reactions 12, 13, 14).

DescriptionThis dataset accompanies the publication "Are pseudo first-order kinetic constants properly calculated for catalytic membranes?" and includes the database containing all the analysed literature, the kinetic rate constants, statistics and the code used to complete all the analysis and generate the figures for the publication. The study compares different methods of calculating pseudo first-order (PFO) kinetic rate constants for degrading water contaminants using catalytic membranes.This dataset includes:database.xlsx: Excel file containing all details about surveyed literature and calculated PFO kinetic results.catalytic_membrane_meta-analysis-0.1.3.zip: zip folder containing figures from the publication and python codes and instructions for generating PFO results and individual figures for every research article listed in database.xlsxData Contentsdatabase.xlsxPorous Catalytic Membranes (Tab 1):Index - number associated with analysis and figures generated by python codes in catalytic_membrane_meta-analysis-0.1.3.zipAuthor full namesTitleYearSource titleDOIAbstractKeywordsData Table (Tab 2):Index - number associated with analysis and figures generated by python codes in catalytic_membrane_meta-analysis-0.1.3.zipTitleComments on C/C0 Graph - description on what data was extracted, the format of the data and any modification required for analysis (if any).Data Location - the location within the research articles or supplimentary informations files where the C/C0 graphs can be found.Removal Mechanism - The removal mechanism specified by the research articles that are associated with the C/C0 graphs.All the PFO kinetic contant results and the statistics associated with different methods of calculating PFO kinectic constantscatalytic_membrane_meta-analysis-0.1.3.zipcase_studies:Directory containing the data, figures and code to generate the figures for the four case studies presented in the article.concentration_graphs:Directory containing subdirectories jpg and csv where concentration graphs (C/C0) are generated to by "data-analysis.py" script examples:Directory containing figures and code relating to the comparison of calculation methodologies for PFO kinetic rate constants in the article.exp_equil_graphs:Directory containing subdirectories jpg and csv where the graphs related to the exponential model with equilibrium constant are generated to by "data-analysis.py" script exponential_graphs:Directory containing subdirectories jpg and csv where the graphs related to the exponential model are generated to by "data-analysis.py" script first_order_kinetic_graphs:Directory containing subdirectories jpg and csv where the graphs related to the linearized from of C/C0 are generated to by "data-analysis.py" scriptindividual_mechanisms:Directory containing the meta-analysis figures for each separate mechanism (found in supplementary information) and the code to generate these figures.meta-analysis:Directory containing the meta-analysis figure found in the article, histograms subdirectory containing the histogram figures and "database.xlsx" from which the data use used to generate the figures is drawnmodel_parameters:Directory containing "model_parameters.csv" which is the output file for the "data-analysis.py" script where all the calculated PSO kinetic contant values and statistics are recorded.processed_data:Directory containing .csv files associated with each analyzed journal article labelled by the index defined in "database.xlsx". The data is the C/C0 data from each paper after modifications were made as described in the journal article including normalization and converting time scale to minutes etc. These files are used by "data-analysis.py" to generate PSO kinetic constants and the figures associated with each analysis method.data-analysis.py:The python script which should be run to analyze the data and generate figures. Licence:The licence agreement for use of the codes and data.meta-analysis:The python script for generating the meta-analysis figures and histogram figures.README.md:The details and instructions for using the provided codes.Usage NotesPlease cite the original publication when using this dataset.LicensingThis dataset is licensed under CC BY 4.0. See the publication for further details.

This Zenodo repository contains the data presented in Spiekermann, K. A.; Pattanaik, L.; Green, W. H.* High Accuracy Barrier Heights, Enthalpies, and Rate Coefficients for Chemical Reactions, Sci. Data 9, 417, (2022). We recommend people refer to this dataset as RDB7 i.e. a diverse reaction database whose transition states contain up to 7 heavy atoms.

Atom-mapped SMILES, barrier heights, reaction enthalpies, and Reaction Mechanism Generator (RMG) reaction family for each reaction are listed in the comma-separated values files b97d3.csv, wb97xd3.csv, ccsdtf12_dz.csv, and ccsdtf12_tz.csv. Q-Chem output files from the reoptimized products are provided for 16,302 reactions at B97-D3/def2-mSVP and for 11,926 reactions at ωB97X-D3/def2-TZVP level of theory. For convenience, these also include the original log files for the reactant, transition state, and non-reoptimized products from Grambow et al. (10.5281/zenodo.3715478) since they were used to calculate barrier heights, enthalpies, and rate constants in this work. The numbering of reaction indices matches that from the originally published dataset to facilitate easy comparison. MOLPRO output files from the single point calculations are provided for 11,926 reactions at the CCSD(T)-F12/cc-pVDZ-F12 level of theory as well as for the 15 validation reactions run at CCSD(T)-F12/cc-pVTZ-F12. The raw log files for all calculations are stored in b97d3.tar.gz, wb97xd3.tar.gz, ccsdtf12_dz.tar.gz, and ccsdtf12_tz.tar.gz. Each archive contains a separate folder for each reaction, which contains log files for the reactant, transition state, and product/s. The Q-Chem log files contain the output from a geometry optimization and harmonic vibrational analysis while the MOLPRO log files contain output from an energy calculation. Transition state theory rate constants, fitted Arrhenius parameters, and average percentage error between the calculated and fitted rate constants can be found for the rigid reactions in ccsdtf12_dz_rigid.csv. The list of 50 temperatures (K) used during Arrhenius fitting is provided in arkane_temperatures.csv, and the raw Arkane outputs are provided in ccsdtf12_dz_rigid.tar.gz.

The improvement from fitting bond additivity corrections at B97-D3/def2-mSVP, ωB97X-D3/def2-TZVP, CCSD(T)-F12/cc-pVDZ-F12//ωB97X-D3/def2-TZVP, and CCSD(T)-F12/cc-pVTZ-F12//ωB97X-D3/def2-TZVP is shown in b97d3_def2msvp_BAC.csv, wb97xd3_def2tzvp_BAC.csv, ccsdtf12_ccpvdzf12_wb97xd3_def2tzvp_BAC.csv, and ccsdtf12_ccpvtzf12_wb97xd3_def2tzvp_BAC.csv respectively. The files contain the experimental and calculated enthalpies for the reference species from the RMG-database used for fitting. The correction values are publicly stored on the RMG-database GitHub on the AEC_BAC branch, though they are also provided in fitted_corrections.pkl for convenience. Further validation of the BACs at the double zeta level was done by comparing to experimental values from the Pedley set since over half of these molecules were not in the RMG-database training set used for fitting. The comparison is shown in ccsdtf12_dz_vs_Pedley_experimental.csv.

Many industrially and environmentally relevant reactions occur in the liquid phase. An accurate prediction of the rate constants is needed to analyze the intricate kinetic mechanisms of condensed phase systems. Quantum chemistry and continuum solvation models are commonly used to compute liquid phase rate constants; yet, their exact computational errors remain largely unknown, and a consistent computational workflow has not been well established. In this study, the accuracies of various quantum chemical and COSMO-RS levels of theory are assessed for the predictions of liquid phase rate constants and kinetic solvent effects. The prediction is made by first obtaining gas phase rate constants and subsequently applying solvation corrections. The calculation errors are evaluated using the experimental data of 191 rate constants that comprise 15 neutral closed-shell or free radical reactions and 49 solvents. The ωB97XD/def2-TZVP level of theory combined with the COSMO-RS method at the BP-TZVP level is shown to achieve the best performance with a mean absolute error of 0.90 in log10(kliq). Relative rate constants are additionally compared to determine the errors associated with the solvation calculations alone. Very accurate predictions of relative rate constants are achieved at nearly all levels of theory with a mean absolute error of 0.27 in log10(ksolvent1/ksolvent2).

Peru Depository Corporations: Liquidity: Exchange Rate: Constant data was reported at 315,071.897 PEN mn in Jun 2019. This records an increase from the previous number of 313,511.893 PEN mn for May 2019. Peru Depository Corporations: Liquidity: Exchange Rate: Constant data is updated monthly, averaging 55,325.295 PEN mn from Jan 1992 (Median) to Jun 2019, with 330 observations. The data reached an all-time high of 315,071.897 PEN mn in Jun 2019 and a record low of 10,452.933 PEN mn in Apr 1992. Peru Depository Corporations: Liquidity: Exchange Rate: Constant data remains active status in CEIC and is reported by Central Reserve Bank of Peru. The data is categorized under Global Database’s Peru – Table PE.KA005: Monetary Survey: Depository Corporations.

The Database of Quantitative Cellular Signaling is a repository of models of signaling pathways. It includes reaction schemes, concentrations, rate constants, as well as annotations on the models. The database provides a range of search, navigation, and comparison functions. This datatype provides access to specific models.

Czech Republic CZ: Real Effective Exchange Rate: Constant Trade Weights data was reported at 1.189 Index, 2015 in 2025. This records a decrease from the previous number of 1.197 Index, 2015 for 2024. Czech Republic CZ: Real Effective Exchange Rate: Constant Trade Weights data is updated yearly, averaging 1.108 Index, 2015 from Dec 2007 (Median) to 2025, with 19 observations. The data reached an all-time high of 1.274 Index, 2015 in 2023 and a record low of 1.000 Index, 2015 in 2015. Czech Republic CZ: Real Effective Exchange Rate: Constant Trade Weights data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.EO: Effective Exchange Rate: Forecast: OECD Member: Annual. EXCHER-Real effective exchange rate, constant trade weightsIndex, OECD reference year OECD calculation, see OECD Economic Outlook database documentation

Evaluated site-specific rate constants for the reactions of isobutane with CH3 and H were determined in a combined analysis of new shock tube experiments and existing literature data. In our shock tube experiments, CH3 radicals, produced from the pyrolysis of di-tert-butylperoxide, and H atoms, produced from the pyrolysis of C2H5I, were reacted with dilute mixtures of isobutane in argon at 870–1130 K and 140–360 kPa, usually with a radical chain inhibitor. Propene and isobutene, measured with GC/FID and MS, were quantified as characteristic of H-abstraction from the primary and tertiary carbons, respectively. Using the method of uncertainty minimization using polynomial chaos expansions (MUM-PCE), a comprehensive Cantera kinetics model based on JetSurF 2.0 was optimized to our experiments and available literature data spanning ambient temperatures to 1327 K. Based on Bayes’ theorem, MUM-PCE constrains the kinetics model to the experimental data. The isobutane literature data used for optimization included both raw experimental data and reported branching and total rate measurements. Data for ethane were also included to better define the absolute rate constant for abstraction of H from primary carbons. For both H and CH3, the optimization increased the relative rate of tertiary to primary H-abstraction compared with existing estimates, especially at higher temperatures. We combine the present data for primary and tertiary sites with previous results from our group on 1-butane to derive site-specific rate constants for the reaction of H and CH3 with generic primary, secondary, and tertiary carbons suitable for a wide range of temperatures.

Anharmonicity can greatly affect rate constants. One or even several orders of magnitude of deviation are found for obtaining rate constants using the standard rigid-rotor harmonic-oscillator model. In turn, reactive molecular dynamics (MD) simulations are a powerful way to explore chemical reaction networks and calculate rate constants from the fully anharmonic potential energy surface. However, the classical nature of the dynamics and the required numerical efficiency of the force field limit the accuracy of the resulting kinetics. We combine the best of both worlds by presenting an approximation that pairs anharmonic information intrinsic to classical MD with high-accuracy energies and frequencies from quantum-mechanical electronic structure calculations. The proposed scheme is applied to hydrogen abstractions in the methane system, which allows for the benchmarking of rate constants corrected by our approach against experimental rate constants. This comparison reveals a standard deviation of factor 2.6. Two archetypes of possible failure are identified in the course of a detailed investigation of the CH3• + H• → CH22• + H2 reaction. From this follows the application range of the method, within which the method shows a standard deviation of factor 2.1. The computational efficiency and beneficial scaling of the method allow for application to larger systems, as shown for hydrogen abstraction from 2-butanone by HO2•.

Integrated rate equations are straightforward to fit to experimental data to verify a proposed mechanism and to extract kinetic parameters. Such equations are derived for reversible disproportionation/comproportionation reactions with any set of initial concentrations. Extraction of forward and reverse rate constants from experimental data by fitting the rate law to the data is demonstrated for the disproportionation of 2,2,6,6-tetramethyl-1-piperidinyl-N-oxyl (TEMPO) under acidic conditions where the approach to equilibrium is observed.

The datasets and final machine learning model files for the manuscript "Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates". Citation should refer directly to the manuscript:

• Chung, Y.; Green, W. H. Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates. Chemical Science 2024, doi: 10.1039/D3SC05353A

To use the machine learning models, please refer to the sample files and instructions on https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML.

Detailed information can be found in README.md file.

Details on the files

In the pretraining and finetuning set csv files, each column represents:

1. rxn_smiles: atom-mapped reaction SMILES
2. solvent_smiles: solvent SMILES
3. ddGsolv: solvation free energy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)
4. ddHsolv: solvation enthalpy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)
5. dGsolv_reactant: solvation free energy of reactant(s) at 298K in kcal/mol (additional feature)
6. dGsolv_product: solvation free energy of product(s) at 298K in kcal/mol (additional feature)
7. dHsolv_reactant: solvation enthalpy of reactant(s) at 298K in kcal/mol (additional feature)
8. dHsolv_product: solvation enthalpy of product(s) at 298K in kcal/mol (additional feature)

Data sets under 'RxnSolvKSE_dataset_v1.1.zip'

• pretraining_set: contains the dataset used for pre-training
  • all_data: contains all calculated data
    • pretraining_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: contains both main prediction targets and additional feature for reaction-solvent pairs
    • pretraining_solvent_info.csv: list of all solvents
    • pretraining_unique_rxn.csv: list of all reactions, both forward and reverse directions
  • chosen_500k_data: contains the chosen 500k data
    • pretraining_rxn_solvent_ddGsolv_ddHsolv_500k.csv: contains main prediction targets for reaction-solvent pairs
    • pretraining_features_react_prod_dGsolv_dHsolv_500k.csv: contains additional features for reaction-solvent pairs
    • train_test_split: contains the 5-fold random split training and test sets.
• finetuning_set: contains the dataset used for fine-tuning
  • all_data: contains all calculated data
    • finetuning_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: constains both main prediction targets and additional features for reaction-solvent pairs. The rxn_key column indicates whether the reaction is bimolecular hydrogen abstraction (bihabs), unimolecular hydrogen migration (intrahabs), or radical addition to a multiple bond (raddition). The 'fwd' and 'rev' each indicate forward and reverse reactions.
    • finetuning_solvent_info.csv: list of all solvents
    • finetuning_unique_rxn.csv: list of all reactions, both forward and reverse directions
  • chosen_data: contains chosen data
    • finetuning_rxn_solvent_ddGsolv_ddHsolv_chosen.csv: contains main prediction targets for reaction-solvent pairs
    • finetuning_features_react_prod_dGsolv_dHsolv_chosen.csv: contains additional features for reaction-solvent pairs
• experimental_set: contains the experimental rate constant data used to test the model. The original experimental data can be found at https://zenodo.org/record/7747557.
  • expt_rxn_atom_mapped_smiles.csv: contains the atom-mapped reaction SMILES used for the experimental data.
  • expt_data_collected.xlsx: contains all experimental data and detailed information
  • expt_rxn_solv_smiles_with_features_all.csv: contains the computed additional features for the experimental reaction-solvent pairs.

Machine learning model files under 'RxnSolvKSE_ML_model_files.zip'

• Contains the Chemprop machine learning model files for predicting ddGsolv and ddHsolv for a reaction-solvent pair. It takes atom-mapped reaction SMILES and solvent SMILES as inputs.
• To use these ML models, please refer to the sample files and instructions on https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML

Specification of affiliations:

Hassan Ali - Centre of Polymer Systems

Muhammad Yasir - Centre of Polymer Systems

Hamza Ul Haq - Laboratory of Alternative Fuel and Sustainability, School of Chemical and Materials Engineering,

Ali Can Guler - Centre of Polymer Systems

Milan Masar - Centre of Polymer Systems

Muhammad Nouman Aslam Khan - Laboratory of Alternative Fuel and Sustainability, School of Chemical and Materials Engineering,

Michal Machovsky - Centre of Polymer Systems

Vladimir Sedlarik - Centre of Polymer Systems

Ivo Kuritka - Centre of Polymer Systems

Raw data for the research paper. Information on the data collection are described in the manuscript.

Machine learning models were built based on published data for a nucleophilic substitution chemical reaction in ionic liquid-acetonitrile solvents. Models were built relating the rate constant of the reaction to the chemical structure of the ionic liquids.

In this dataset, we provide analytical data to Kinetic oxygen isotope fractionation between water and aqueous OH- during hydroxylation of CO2 by Bajnai and Herwartz (2021). Also deposited here is the R code that was used to generate the figures in the manuscript.

Original (raw) data files to accompany the manuscript submitted to Atmospheric Chemistry and Physics Discussions (ACPD), titled "Measurements of Henry's law and liquid-phase loss rate constants of peroxypropionic nitric anhydride (PPN) in deionized water and in n-octanol", Atmos. Chem. Phys. Discuss, in review (2022) https://doi.org/10.5194/acp-2022-587.

We report a new method for determination of rate constants of processes in solution using electrospray ionization mass spectrometry (ESI-MS). The investigated reaction is C–H activation of acetanilides by palladium(II)trifluoroacetate leading to stable organopalladium complexes. The rate constants can be determined from an experiment with a couple of differently substituted acetanilides being in competition for being activated by the palladium salt. The formed organopalladium complexes can be detected by ESI-MS. The time dependence is achieved by adding one of the acetanilides to the reaction mixture with a time delay. The kinetics can be then evaluated from the evolution of the ratio of the ESI-MS signals of differently substituted complexes as a function of the time delay. The Hammett analysis of the rate constants obtained for a series of meta- and para-substituted acetanilides provides a ρ value of −1.5, which is in agreement with values reported for similar C–H activations. We have also investigated the very same reaction with UV–vis spectroscopy that gave us about three times smaller rate constants but the same trend with the ρ value of −1.6. The rate constants determined by ESI-MS are directly linked to the occurrence of organopalladium complexes, whereas the UV–vis data are associated with an absorption spectra change that could involve more reaction steps. DFT calculations support the interpretation of the reaction mechanism as cyclopalladation and provide the ρ value in the same range. The rate-determining step corresponds to the agostic C–H transition structure.

Kinetics of the reactions of aryldiazomethanes (ArCHN2) with benzhydrylium ions (Ar2CH+) have been measured photometrically in dichloromethane. The resulting second-order rate constants correlate linearly with the electrophilicities E of the benzhydrylium ions which allowed us to use the correlation lg k = sN(N + E) (eq 1) for determining the nucleophile-specific parameters N and sN of the diazo compounds. UV–vis spectroscopy was analogously employed to measure the rates of the 1,3-dipolar cycloadditions of these aryldiazomethanes with acceptor-substituted ethylenes of known electrophilicities E. The measured rate constants for the reactions of the diazoalkanes with highly electrophilic Michael acceptors (E > −11, for example 2-benzylidene Meldrum’s acid or 1,1-bis(phenylsulfonyl)ethylene) agreed with those calculated by eq 1 from the one-bond nucleophilicities N and sN of the diazo compounds and the one-bond electrophilicities of the dipolarophiles, indicating that the incremental approach of eq 1 may also be applied to predict the rates of highly asynchronous cycloadditions. Weaker electrophiles, e.g., methyl acrylate, react faster than calculated from E, N, and sN, and the ratio of experimental to calculated rate constants was suggested to be a measure for the energy of concert ΔG‡concert = RT ln(k2exptl/k2calcd). Quantum chemical calculations indicated that all products isolated from the reactions of the aryldiazomethanes with acceptor substituted ethylenes (Δ2-pyrazolines, cyclopropanes, and substituted ethylenes) arise from intermediate Δ1-pyrazolines, which are formed through concerted 1,3-dipolar cycloadditions with transition states, in which the C–N bond formation lags behind the C–C bond formation. The Gibbs activation energies for these cycloadditions calculated at the PCM(UA0,CH2Cl2)/(U)B3LYP-D3/6-31+G(d,p) level of theory agree within 5 kJ mol–1 with the experimental numbers showing the suitability of the applied polarizable continuum model (PCM) for considering solvation.