A human-machine interface for automatic exploration of chemical reaction networks

Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.

Photoelectron Photoion Coincidence Spectroscopy of Biradicals

The electronic structure of biradicals is characterized by the presence of two unpaired electrons in degenerate or near-degenerate molecular orbitals. In particular, some of the most relevant species are highly reactive, difficult to generate cleanly and can only be studied in the gas phase or in matrices. Unveiling their electronic structure is, however, of paramount interest to understand their chemistry. Photoelectron photoion coincidence (PEPICO) spectroscopy is an excellent approach to explore the electronic states of biradicals, because it enables a direct correlation between the detected ions and electrons. This permits to extract unique vibrationally resolved photoion mass-selected threshold photoelectron spectra (ms-TPES) to obtain insight in the electronic structure of both the neutral and the cation. In this review we highlight most recent advances on the spectroscopy of biradicals and biradicaloids, utilizing PEPICO spectroscopy and vacuum ultraviolet (VUV) synchrotron radiation.

Radical-Radical Reactions in Molecular Weight Growth: The Phenyl + Propargyl Reaction

The mechanism for hydrocarbon ring growth in sooting environments is still the subject of considerable debate. The reaction of phenyl radical (C₆H₅) with propargyl radical (H₂CCCH) provides an important prototype for radical-radical ring-growth pathways. We studied this reaction experimentally over the temperature range of 300-1000 K and pressure range of 4-10 Torr using time-resolved multiplexed photoionization mass spectrometry. We detect both the C₉H₈ and C₉H₇ + H product channels and report experimental isomer-resolved product branching fractions for the C₉H₈ product. We compare these experiments to theoretical kinetics predictions from a recently published study augmented by new calculations. These ab initio transition state theory-based master equation calculations employ high-quality potential energy surfaces, conventional transition state theory for the tight transition states, and direct CASPT2-based variable reaction coordinate transition state theory (VRC-TST) for the barrierless channels. At 300 K only the direct adducts from radical-radical addition are observed, with good agreement between experimental and theoretical branching fractions, supporting the VRC-TST calculations of the barrierless entrance channel. As the temperature is increased to 1000 K we observe two additional isomers, including indene, a two-ring polycyclic aromatic hydrocarbon, and a small amount of bimolecular products C₉H₇ + H. Our calculated branching fractions for the phenyl + propargyl reaction predict significantly less indene than observed experimentally. We present further calculations and experimental evidence that the most likely cause of this discrepancy is the contribution of H atom reactions, both H + indenyl (C₉H₇) recombination to indene and H-assisted isomerization that converts less stable C₉H₈ isomers into indene. Especially at low pressures typical of laboratory investigations, H-atom-assisted isomerization needs to be considered. Regardless, the experimental observation of indene demonstrates that the title reaction leads, either directly or indirectly, to the formation of the second ring in polycyclic aromatic hydrocarbons.

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.

Non-oxidative methane conversion by Fe single site catalysts: quantifying temperature limitations imposed by gas-phase pyrolysis

A combined heterogeneous and homogeneous model of methane conversion reveals the temperature limitations of methane to olefins, aromatics, and hydrogen (MTOAH).

Kinetic Modeling of API Oxidation: (1) The AIBN/H2O/CH3OH Radical "Soup"

Stress testing of active pharmaceutical ingredients (API) is an important tool used to gauge chemical stability and identify potential degradation products. While different flavors of API stress testing systems have been used in experimental investigations for decades, the detailed kinetics of such systems as well as the chemical composition of prominent reactive species, specifically reactive oxygen species, are unknown. As a first step toward understanding and modeling API oxidation in stress testing, we investigated a typical radical "soup" solution an API is subject to during stress testing. Here we applied ab initio electronic structure calculations to automatically generate and refine a detailed chemical kinetics model, taking a fresh look at API oxidation. We generated a detailed kinetic model for a representative azobis(isobutyronitrile) (AIBN)/H₂O/CH₃OH stress-testing system with a varied cosolvent ratio (50%/50%-99.5%/0.5% vol water/methanol) for 5.0 mM AIBN and representative pH values of 4-10 at 40 °C that was stirred and open to the atmosphere. At acidic conditions, hydroxymethyl alkoxyl is the dominant alkoxyl radical, and at basic conditions, for most studied initial methanol concentrations, cyanoisopropyl alkoxyl becomes the dominant alkoxyl radical, albeit at an overall lower concentration. At acidic conditions, the levels of cyanoisopropyl peroxyl, hydroxymethyl peroxyl, and hydroperoxyl radicals are relatively high and comparable, while, at both neutral and basic pH conditions, superoxide becomes the prominent radical in the system. The present work reveals the prominent species in a common model API stress testing system at various cosolvent and pH conditions, sets the stage for an in-depth quantitative API kinetic study, and demonstrates the usage of novel software tools for automated chemical kinetic model generation and ab initio refinement.

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.

Machine learning in chemical reaction space

Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for 'reactive' ML, we establish a first-principles database (Rad-6) containing closed and open-shell organic molecules, along with an associated database of chemical reaction energies (Rad-6-RE). We show that the special topology of reaction spaces, with central hub molecules involved in multiple reactions, requires a modification of existing compound space ML-concepts. Showcased by the application to methane combustion, we demonstrate that the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses.

Theoretical Study of PAH Growth by Phenylacetylene Addition

Although there was significant advancement on polycyclic aromatic hydrocarbon (PAH) formation, current mechanisms are still limited in providing an integrated and accurate scheme of PAH yield in combustion conditions; thus, a more detailed and comprehensive understanding is necessary. This work provides a systematic investigation of PAH growth by phenylacetylene addition. A combination of the density functional theory (DFT/B3LYP/6-311+G(d,p)) and the complete basis set method (CBS-QB3) is utilized to calculate the potential energy surfaces. The reaction system is initiated by the H elimination reaction of phenylacetylene + H → o-ethynylphenyl + H₂, and then, the addition reaction of phenylacetylene and o-ethynylphenyl can produce PAHs with one, two, three, and four rings. The temperature- and pressure-dependent reaction rate coefficients are calculated via a combination of conventional transition state theory (TST) and Rice-Ramsperger-Kassel-Marcus (RRKM) theory with solving the master equation in the temperature range of 500-2500 K and at the pressure range of 0.01-10 atm. There are 263 species and 65 reactions in this reaction system. It shows that the rate constants of this reaction system are highly temperature-dependent and slightly sensitive to the pressure at temperatures lower than 1300 K. To evaluate the yield distributions of various PAH products in the whole reaction network, a closed 0-D batch reactor model in Chemkin is used to calculate the C₆H₅C₂H-C₂H₂-H-Ar reaction system. The results showed that the prevailing products of this system are three-ring PAHs with side chain structures. Compared with the traditional HACA pathways, the investigated reaction system presents higher efficiency in large PAH formations, which could subsequently promote the formation of soot particles. The phenylacetylene and o-ethynylphenyl reaction network emphasizes the importance of species with side chains, and it enriches current PAH growth pathways aside from the addition of small species such as C₂H₂.

From benzene to naphthalene: direct measurement of reactions and intermediates of phenyl radicals and acetylene

First-time measurement of time evolution of the main products and critical intermediates on phenyl HACA pathways with a validated pressure-dependent model.