Atomic Partial Charges as Descriptors for Barrier Heights

Prediction of Activation Energies of Organic Molecules With at Most Seven Non-Hydrogen Atoms Using Quantum-Chemically Assisted ML

In this study, a hybrid machine learning (ML) approach is presented for accurately predicting activation energies (Ea) of gas-phase elementary reactions involving organic compounds with up to seven non-hydrogen atoms. Given the importance of activation energies in reaction studies and modeling, ML composite models were created that effectively integrate molecular descriptors with semi-empirical and single energy density functional theory (DFT) calculations. The dataset, containing 300 randomly selected elementary gas-phase reactions, was assembled using accurate DFT (ωB97X-D3/def2-TZVP) values for activation energies Ea from a database alongside semi-empirical computations. For accurate predictions, this approach required the inclusion of both physical organic and geometric/empirical descriptors in the training procedure. The best two ML models demonstrated efficient Ea prediction capability, achieving a mean absolute error (MAE) of 1.314 kcal mol-1 and R2 of 0.992 (Model 3) and (MAE) of 1.949 kcal mol-1 and R2 of 0.979 (Model 2) in validation tests. Notably, this performance approaches the threshold of "chemical accuracy" of 1 kcal mol-1. Model's 3 robustness was tested across the reaction types present in the dataset, demonstrating its ability in properly predicting activation energies, which is critical for the study and optimization of chemical processes.

Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability

Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor.

Machine Learning Models for Predicting Zirconocene Properties and Barriers

Zr metallocenes have significant potential to be highly tunable polyethylene catalysts through modification of the aromatic ligand framework. Here we report the development of multiple machine learning models using a large library (>700 systems) of DFT-calculated zirconocene properties and barriers for ethylene polymerization. We show that very accurate machine learning models are possible for HOMO-LUMO gaps of precatalysts but the performance significantly depends on the machine learning algorithm and type of featurization, such as fingerprints, Coulomb matrices, smooth overlap of atomic positions, or persistence images. Surprisingly, the description of the bonding hapticity, the number of direct connections between Zr and the ligand aromatic carbons, only has a moderate influence on the performance of most models. Despite robust models for HOMO-LUMO gaps, these types of machine learning models based on structure connectivity type features perform poorly in predicting ethylene migratory insertion barrier heights. Therefore, we developed several relatively robust and accurate machine learning models for barrier heights that are based on quantum-chemical descriptors (QCDs). The quantitative accuracy of these models depends on which potential energy surface structure QCDs were harvested from. This revealed a Hammett-type principle to naturally emerge showing that QCDs from the π-coordination complexes provide much better descriptions of the transition states than other potential-energy structures. Feature importance analysis of the QCDs provides several fundamental principles that influence zirconocene catalyst reactivity.

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 [Formula: see text] 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.

Insight into the Ozone-Assisted Low-Temperature Combustion of Dimethyl Ether by Means of Stabilized Cool Flames

The low-temperature combustion kinetics of dimethyl ether (DME) were studied by means of stabilized cool flames in a heated stagnation plate burner configuration using ozone-seeded premixed flows of DME/O₂. Direct imaging of CH₂O* chemiluminescence and laser-induced fluorescence of CH₂O were used to determine the flame front positions in a wide range of lean and ultra-lean equivalence ratios and ozone concentrations for two strain rates. The temperature and species mole fraction profiles along the flame were measured by coupling thermocouples, gas chromatography, micro-chromatography, and quadrupole mass spectrometry analysis. A new kinetic model was built on the basis of the Aramco 1.3 model, coupled with a validated submechanism of O₃ chemistry, and was updated to improve the agreement with the obtained experimental results and experimental data available in the literature. The main results show the efficiency of the tested model to predict the flame front position and temperature in every tested condition, as well as the importance of reactions typical of atmospheric chemistry in the prediction of cool flame occurrence. The agreement on the fuel and major products is overall good, except for methanol, highlighting some missing kinetic pathways for the DME/O₂/O₃ system, possibly linked to the direct addition of atomic oxygen on the fuel radical, modifying the product distribution after the cool flame.