Extensive High-Accuracy Thermochemistry and Group Additivity Values for Halocarbon Combustion Modeling

Farina, David; Sirumalla, Sai Krishna; Mazeau, Emily; West, Richard H.

doi:10.33774/chemrxiv-2021-1wjw8

Cited by 4 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For halogen chemistry, the CHOX G4 (X=F,Cl,Br,FCl,ClBr,FClBr) libraries are recommended as they cover a significant portion of their respective chemical spaces with up to 4 carbons and oxygen atoms. 33 Catalyst adsorbates are available in Sur-faceThermoPt111 and SurfaceThermoNi111. 39,40 Many other specialized thermo libraries are also available within the RMG-database.…”

Section: Librariesmentioning

confidence: 99%

“…45 An additional neural network-based thermochemistry estimator specific for halogenated species was trained off of G4 data from Farina et al 2021 recently. 17,36,46 While not yet added to the RMG database, this neural network will be available in the near future.…”

Section: ■ Introductionmentioning

confidence: 99%

“…[24][25][26][27][28][29] Theory Thermo Group Additivity of group values has been derived from quantum chemical calculations. [30][31][32][33] RMG's scheme also integrates several advanced corrections to the ordinary Benson groups:…”

Section: Introductionmentioning

confidence: 99%

“…• long distance interaction corrections for halogenated molecules. 33 • ortho, para, and meta cyclic non-nearest neighbor interactions using values from Ince et al 2015 and Ince et al 2017. 37,38 • ketene corrections.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

RMG Database for Chemical Property Prediction

Johnson

Dong

Dana

et al. 2022

J. Chem. Inf. Model.

View full text Add to dashboard Cite

The Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical kinetic mechanisms. These datasets and estimators are mostly published and enable prediction of thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG database contains 45 libraries of thermochemical parameters with a combination of 4564 entries and a group additivity scheme with 9 types of corrections including radical, polycyclic, and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural network trained using transfer learning from a set of >130 000 DFT calculations to 10 000 high-quality values. Correction schemes for solvent−solute effects, important for thermochemistry in the liquid phase, are available. They include tabulated values for 195 pure solvents and 152 common solutes and a group additivity scheme for predicting the properties of arbitrary solutes. For kinetics estimation, the database contains 92 libraries of kinetic parameters containing a combined 21 000 reactions and contains rate rule schemes for 87 reaction classes trained on 8655 curated training reactions. Additional libraries and estimators are available for transport properties. All of this information is easily accessible through the graphical user interface at https://rmg.mit.edu. Bulk or on-the-fly use can be facilitated by interfacing directly with the RMG Python package which can be installed from Anaconda. The RMG database provides kineticists with easy access to estimates of the many parameters they need to model and analyze kinetic systems. This helps to speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction, and by providing checks on kinetic parameters from other sources.

show abstract

Section: Librariesmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

RMG Database for Chemical Property Prediction

Johnson

Dong

Dana

et al. 2022

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…42 An additional neural network based thermochemistry estimator specific for halogenated species was trained off of G4 data from Farina et al 2021 recently. 15,33,43 While not yet added to the RMG-database this neural network will be available in the near future.…”

Section: Thermodynamic Property Neural Network Estimatormentioning

confidence: 99%

The RMG Database for Chemical Property Prediction

Johnson

Dong

Dana

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The RMG-database for chemical property prediction is presented. The RMG-database consists of curated datasets and estimators for accurately predicting parameters necessary for constructing a wide variety of chemical kinetic mechanisms, including thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG-database contains 45 libraries of thermochemical parameters with a combined 4564 entries, a group additivity scheme with nine types of corrections including radical, polycyclic and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural net trained using transfer learning from a set of >130,000 DFT calculations to 10,000 high-quality values. Correction schemes for solvent-solute effects, important for thermochemistry in the liquid phase, are available. They include tabled values for 195 pure solvents and 152 common solutes and a group additivity scheme for predicting the properties of arbitrary solutes. For kinetics estimation the database contains 92 libraries of kinetic parameters containing a combined 21,000 reactions and contains rate rule schemes for 87 reaction classes trained on 8655 curated training reactions. Additional libraries and estimators are available for transport properties. All of this information is easily accessible through the graphical user interface at https://rmg.mit.edu. Bulk or on-the-fly use can be facilitated by interfacing directly with the RMG Python package which can be installed from Anaconda. The RMG-database provides kineticists with easy access to estimates of the many parameters they need to model and analyze kinetic systems. This helps speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction and by providing information to check other kinetic parameters against.

show abstract

Implementing the Blowers-Masel Approximation to Scale Activation Energy Based on Reaction Enthalpy in Mean-field Micro-kinetic Modeling for Catalytic Methane Partial Oxidation

Xu,

Mazeau,

West

2023

Preprint

View full text Add to dashboard Cite

Mean-field micro-kinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a micro-kinetic model often need to be adjusted when changing species’ binding energies to model differ- ent catalysts, when performing thermodynamic sensitivity analyses, and when fitting experimental data. When altering reaction enthalpies, the activation energies should also be reasonably altered, to ensure realistic reaction rates. The Blowers-Masel ap- proximation (BMA) relates the reaction barrier to the reaction enthalpy. Unlike the Brønsted-Evans-Polani (BEP) relationship, the BMA requires less data because only one parameter, the intrinsic activation energy, needs to be determined. We validate this novel application of BMA relations to model surface reactions by comparing against density functional theory (DFT) data taken from literature. By incorporating the BMA rate description into the open-source Cantera software we enable a new work- flow, demonstrated herein, allowing rapid screening of catalysts using linear scaling relationships (LSRs) and BMA kinetics within the process simulation software. For demonstration purposes, a catalyst screening for catalytic methane partial oxidation (CMPO) on 81 hypothetical metals is conducted. We compare the results with and without BMA-corrected rates. The heat maps of various descriptors (e.g. CH4 conver- sion, syngas yield) show that using BMA rates instead of Arrhenius rates (with constant activation energies) changes which metals are most active. Heat maps of sensitivity analyses can help identify which reactions or species are most influential in shaping the descriptor map patterns. Our findings indicate that while using BMA-adjusted rates didn't markedly affect the most sensitive reactions, it did change the most influential species.

show abstract

Extensive High-Accuracy Thermochemistry and Group Additivity Values for Halocarbon Combustion Modeling

Cited by 4 publications

References 28 publications

RMG Database for Chemical Property Prediction

RMG Database for Chemical Property Prediction

The RMG Database for Chemical Property Prediction

Implementing the Blowers-Masel Approximation to Scale Activation Energy Based on Reaction Enthalpy in Mean-field Micro-kinetic Modeling for Catalytic Methane Partial Oxidation

Contact Info

Product

Resources

About