First-principles based group additivity values for thermochemical properties of substituted aromatic compounds

Geometric deep learning for molecular property predictions with chemical accuracy across chemical space

Chemical engineers heavily rely on precise knowledge of physicochemical properties to model chemical processes. Despite the growing popularity of deep learning, it is only rarely applied for property prediction due to data scarcity and limited accuracy for compounds in industrially-relevant areas of the chemical space. Herein, we present a geometric deep learning framework for predicting gas- and liquid-phase properties based on novel quantum chemical datasets comprising 124,000 molecules. Our findings reveal that the necessity for quantum-chemical information in deep learning models varies significantly depending on the modeled physicochemical property. Specifically, our top-performing geometric model meets the most stringent criteria for "chemically accurate" thermochemistry predictions. We also show that by carefully selecting the appropriate model featurization and evaluating prediction uncertainties, the reliability of the predictions can be strongly enhanced. These insights represent a crucial step towards establishing deep learning as the standard property prediction workflow in both industry and academia.Scientific contributionWe propose a flexible property prediction tool that can handle two-dimensional and three-dimensional molecular information. A thermochemistry prediction methodology that achieves high-level quantum chemistry accuracy for a broad application range is presented. Trained deep learning models and large novel molecular databases of real-world molecules are provided to offer a directly usable and fast property prediction solution to practitioners.

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.

Isolating key reaction energetics and thermodynamic properties during hardwood model lignin pyrolysis

Computational studies on the pyrolysis of lignin using electronic structure methods have been largely limited to dimeric or trimeric models. In the current work we have modeled a lignin oligomer consisting of 10 syringyl units linked through 9 β-O-4' bonds. A lignin model of this size is potentially more representative of the polymer in angiosperms; therefore, we used this representative model to examine the behavior of hardwood lignin during the initial steps of pyrolysis. Using this oligomer, the present work aims to determine if and how the reaction enthalpies of bond cleavage vary with positions within the chain. To accomplish this, we utilized a composite method using molecular mechanics based conformational sampling and quantum mechanically based density functional theory (DFT) calculations. Our key results show marked differences in bond dissociation enthalpies (BDE) with the position. In addition, we calculated standard thermodynamic properties, including enthalpy of formation, heat capacity, entropy, and Gibbs free energy for a wide range of temperatures from 25 K to 1000 K. The prediction of these thermodynamic properties and the reaction enthalpies will benefit further computational studies and cross-validation with pyrolysis experiments. Overall, the results demonstrate the utility of a better understanding of lignin pyrolysis for its effective valorization.

Learning Molecular Representations for Thermochemistry Prediction of Cyclic Hydrocarbons and Oxygenates

Accurate thermochemistry estimation of polycyclic molecules is crucial for kinetic modeling of chemical processes that use renewable and alternative feedstocks. In kinetic model generators, molecular properties are estimated rapidly with group additivity, but this method is known to have limitations for polycyclic structures. This issue has been resolved in our work by combining a geometry-based molecular representation with a deep neural network trained on ab initio data. Each molecule is transformed into a probabilistic vector from its interatomic distances, bond angles, and dihedral angles. The model is tested on a small experimental dataset (200 molecules) from the literature, a new medium-sized set (4000 molecules) with both open-shell and closed-shell species, calculated at the CBS-QB3 level with empirical corrections, and a large G4MP2-level QM9-based dataset (40 000 molecules). Heat capacities between 298.15 and 2500 K are calculated in the medium set with an average deviation of about 1.5 J mol^-1 K^-1 and the standard entropy at 298.15 K is predicted with an average error below 4 J mol^-1 K^-1. The standard enthalpy of formation at 298.15 K has an average out-of-sample error below 4 kJ mol^-1 on a QM9 training set size of around 15 000 molecules. By fitting NASA polynomials, the enthalpy of formation at higher temperatures can be calculated with the same accuracy as the standard enthalpy of formation. Uncertainty quantification by means of the ensemble standard deviation is included to indicate when molecules that are on the edge or outside of the application range of the model are evaluated.

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation

In chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since the previously published description of RMG v1.0. Most notably, RMG can now generate heterogeneous catalysis models in addition to the previously available gas- and liquid-phase capabilities. For model analysis, new methods for local and global uncertainty analysis have been implemented to supplement first-order sensitivity analysis. The RMG database of thermochemical and kinetic parameters has been significantly expanded to cover more types of chemistry. The present release includes parallelization for faster model generation and a new molecule isomorphism approach to improve computational performance. RMG has also been updated to use Python 3, ensuring compatibility with the latest cheminformatics and machine learning packages. Overall, RMG v3.0 includes many changes which improve the accuracy of the generated chemical mechanisms and allow for exploration of a wider range of chemical systems.

Transferable Ring Corrections for Predicting Enthalpy of Formation of Cyclic Compounds

Computational predictions of the thermodynamic properties of molecules and materials play a central role in contemporary reaction prediction and kinetic modeling. Due to the lack of experimental data and computational cost of high-level quantum chemistry methods, approximate methods based on additivity schemes and more recently machine learning are currently the only approaches capable of supplying the chemical coverage and throughput necessary for such applications. For both approaches, ring-containing molecules pose a challenge to transferability due to the nonlocal interactions associated with conjugation and strain that significantly impact thermodynamic properties. Here, we report the development of a self-consistent approach for parameterizing transferable ring corrections based on high-level quantum chemistry. The method is benchmarked against both the Pedley-Naylor-Kline experimental dataset for C-, H-, O-, N-, S-, and halogen-containing cyclic molecules and a dataset of Gaussian-4 quantum chemistry calculations. The prescribed approach is demonstrated to be superior to existing ring corrections while maintaining extensibility to arbitrary chemistries. We have also compared this ring-correction scheme against a novel machine learning approach and demonstrate that the latter is capable of exceeding the performance of physics-based ring corrections.

Electronic structure-based rate rules for ipso addition-elimination reactions on mono-aromatic hydrocarbons with single and double OH/CH3/OCH3/CHO/C2H5 substituents: a systematic theoretical investigation

The recent interest in bio-oils combustion and the key role of mono-aromatic hydrocarbons (MAHs) in existing kinetic frameworks, both in terms of poly-aromatic hydrocarbons growth and surrogate fuels formulation, motivates the current systematic theoretical investigation of one of the relevant reaction classes in MAHs pyrolysis and oxidation: ipso substitution by hydrogen. State-of-the-art theoretical methods and protocols implemented in automatized computational routines allowed to investigate 14 different potential energy surfaces involving MAHs with hydroxy and methyl single (phenol and toluene) and double (o-,m-,p-C6H4(OH)2, o-,m-,p-CH3C6H4OH, and o-,m-,p-C6H4(CH3)2) substituents, providing rate constants for direct implementation in existing kinetic models. The accuracy of the adopted theoretical method was validated by comparison of the computed rate constants with the available literature data. Systematic trends in energy barriers, pre-exponential factors, and temperature dependence of the Arrhenius parameters were found, encouraging the formulation of rate rules for ipso substitutions on MAHs. The rules here proposed allow to extrapolate from a reference system the necessary activation energy and pre-exponential factor corrections for a large number of reactions from a limited set of electronic structure calculations. We were able to estimate rate constants for other 63 ipso addition-elimination reactions on di-substituted MAHs, reporting in total 75 rate constants for ipso substitution reactions o-,m-,p-R'C6H4R + → C6H5R + ', with R,R' = OH/CH3/OCH3/CHO/C2H5, in the 300-2000 K range. Additional calculations performed for validation showed that the proposed rate rules are in excellent agreement with the rate constants calculated using the full computational protocol in the 500-2000 K range, generally with errors below 20%, increasing up to 40% in a few cases. The main results of this work are the successful application of automatized electronic structure calculations for the derivation of accurate rate constants for ipso substitution reactions on MAHs, and an efficient and innovative approach for rate rules formulation for this reaction class.

Modeling study of the anti-knock tendency of substituted phenols as additives: an application of the reaction mechanism generator (RMG)

This work presents kinetic modeling efforts to evaluate the anti-knock tendency of several substituted phenols if used as gasoline additives. They are p-cresol, m-cresol, o-cresol, 2,4-xylenol, 2-ethylphenol, and guaiacol. A detailed kinetic model was constructed to predict the ignition of blends of the phenols in n-butane with the help of reaction mechanism generator (RMG), an open-source software package. The resulting model, which has 1465 species and 27 428 reactions, was validated against literature n-butane ignition data in the low-to-intermediate temperature range. To rank the anti-knock tendency of the additives, engine-like simulations were performed in a closed adiabatic homogenous batch reactor with a volume history derived from the pressure profile of a real research octane number (RON) engine test. The ignition timings of the additive blends were compared to that of primary reference fuels (PRFs) to quantitatively predict the anti-knock ability. The model predictions agree well with experimental determinations of the changes in RON induced by the additives. This study explains the chemical mechanism by which methyl-substituted phenols increase RON, and demonstrates how fundamental chemical kinetics can be used to evaluate practical fuel additive performance.

Thermochemistry of gas-phase and surface species via LASSO-assisted subgraph selection

Graph theory-based regression techniques, such as group additivity, have widely been implemented for fast estimation of thermochemistry of large molecules.