Thermochemistry and Group Additivity Values for Fused Two-Ring Species and Radicals

Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation

Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of reliable kinetic models requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed-shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, currently rely on expert knowledge and manual construction, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based prepruning methods. Moreover, we compile a data set of thermochemical parameters for 2210 radicals involving C, O, N, and H based on quantum chemical calculations from recently published works. We leverage the collected data set to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending the tree estimator for thermochemistry, (2) has better accuracy and R2, (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry.

RMG Database for Chemical Property Prediction

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.

An extensive theoretical study on the thermochemistry of aromatic compounds: from electronic structure to group additivity values

An extensive and reliable database of thermodynamic properties of C₆-C₁₂ aromatic molecules is constructed by using quantum chemistry calculations. There are 101 molecules in total, which cover a variety of structures including mono-substituted, di-substituted, and bi-cyclic aromatics which can be important intermediates in the combustion of alkylbenzenes. Based on the database, a consistent set of Benson group additive values (GAV) and non-nearest neighbor interactions (NNI) is developed to extend the applicability of Benson's group additivity method for aromatic molecules. Meanwhile, GAVs of existing groups are also updated to improve their accuracy. Geometry optimizations, and vibrational frequency calculations are conducted at the M06-2X/6-311++G(d,p) level of theory. Internal rotor potentials for lower-frequency modes are obtained at the M06-2X/6-31G level of theory. G3 and G4 compound methods are used to derive the 0 K enthalpies of formation via the atomization approach. The entropy and temperature-dependent heat capacity values of all species are calculated via the Master Equation System Solver (MESS) code. This work also provides an extensive literature comparison to validate the calculated results, and good agreement is observed with literature data. The correction terms beyond a group range are explored. The NNIs of di-substituted aromatics with substituents including OH, CHO, and CH₃ groups are reported. Entropy reduction is observed in the molecules with two substituents in the ortho position, which mainly derives from the hindered internal rotations. In addition, ring strain corrections (RSC) of dicyclic aromatics are evaluated. The strain energies of molecules with a four-membered side ring are prominently large, as the bond length and bond angle distortions are severely restricted. Ring strain also plays a key role in the C-H bond strength associated with the benzylic carbons in dicyclic aromatics. The loss of a hydrogen atom can destroy the high ring-strain geometry leading to a large C-H bond energy.

High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions

Quantitative chemical reaction data, including activation energies and reaction rates, are crucial for developing detailed kinetic mechanisms and accurately predicting reaction outcomes. However, such data are often difficult to find, and high-quality datasets are especially rare. Here, we use CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP to obtain high-quality single point calculations for nearly 22,000 unique stable species and transition states. We report the results from these quantum chemistry calculations and extract the barrier heights and reaction enthalpies to create a kinetics dataset of nearly 12,000 gas-phase reactions. These reactions involve H, C, N, and O, contain up to seven heavy atoms, and have cleaned atom-mapped SMILES. Our higher-accuracy coupled-cluster barrier heights differ significantly (RMSE of ∼5 kcal mol⁻¹) relative to those calculated at ωB97X-D3/def2-TZVP. We also report accurate transition state theory rate coefficients \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${k}_{\infty }(T)$$\end{document}k∞(T) between 300 K and 2000 K and the corresponding Arrhenius parameters for a subset of rigid reactions. We believe this data will accelerate development of automated and reliable methods for quantitative reaction prediction.

Measurement(s)	Barrier Heights • Enthalpies • Rate Coefficients
Technology Type(s)	ab initio quantum chemistry computational method