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.

This paper is Part II of a study of the chemical structural components of the organic matter of oil shale in the Green River formation. Three sections of a well-characterized oil shale core from the Utah Green River formation were demineralized, and the resulting kerogen was pyrolyzed at 10°C/min in nitrogen at atmospheric pressure at temperatures up to 525°C. The pyrolysis products (light gas, tar, and char) were analyzed using 13 C NMR, GC/MS, and (FTIR). Pyrolysis yields of 80% (daf basis) were achieved at these conditions, with 60% daf tar yield at the highest temperature. The solid-state NMR results indicate that the aromaticity of the kerogen char increased from 20% (at RT) to 80% during pyrolysis, with a corresponding decrease in the average aliphatic carbon chain length from 12 to less than 1. The average number of aromatic carbons per cluster increased from 12 to 20 in a narrow temperature window between 425 and 525°C, with an increase in the number of attachments per cluster from 6 to 8 in that same temperature window. Liquid-state NMR results of the condensed tars showed predominance of n-alkyl chains, with similar carbon aromaticities at each temperature. The alkyl chains were also observed in the GC/MS data. The light gases determined by FTIR were primarily CH 4 , CO, and CO 2 . The combination of gas, tar, and char yields and chemical structure analyses are valuable for modeling of oil shale processes based on chemical structure rather than based on empiricism.

We present a new strategy to estimate the temperature-dependent vapor-liquid equilibria and solvation free energies of dilute neutral molecules based on only their estimated solvation energy and enthalpy at 298 K. These two pieces of information coupled with matching conditions between the functional forms developed by Japas and Levelt Sengers for near critical conditions and by Harvey for low and moderate temperature conditions allow the fitting of a piecewise function that predicts the temperature-dependent solvation energy for dilute solutes up to the critical temperature of the solvent. If the Abraham and Mintz parameters for the solvent and solute are available or can be estimated from group contributions, this method requires no experimental data and can still provide accurate estimates with an error of about 1.6 kJ/mol. This strategy, which requires minimal computational resources, is shown to compare well with other methods of temperature-dependent solvation free energy prediction. K E Y W O R D Schemical property estimation, gas solubility, phase equilibrium, solvation free energy

The open-source statistical mechanics software described here, Arkane-Automated Reaction Kinetics and Network Exploration-facilitates computations of thermodynamic properties of chemical species, high-pressure limit reaction rate coefficients, and pressure-dependent rate coefficient over multi-well molecular potential energy surfaces (PES) including the effects of collisional energy transfer on phenomenological kinetics. Arkane can use estimates to fill in information for molecules or reactions where quantum chemistry information is missing. The software solves the internal energy master equation for complex unimolecular reaction systems. Inputs to the software include converged electronic structure computations performed by the user using a variety of supported software packages (Gaussian, Molpro, Orca, TeraChem, Q-Chem, Psi4). The software outputs high-pressure limit rate coefficients and pressure-dependentThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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