Effect of Hydrogen Addition on Coke Formation and Product Distribution in Catalytic Coupling of Methane

The effect of hydrogen addition on catalytic nonoxidative coupling of methane at 1000 °C was investigated. Experiments were performed at varying ratios between the catalyst and the postcatalytic volume to discern the effect of hydrogen on the catalytic reaction as well as on the gas-phase reaction. Adding 10% H2 decreases the methane conversion by a factor of 2, almost independent of the ratio between the catalyst and the postcatalytic residence time. The effect on the conversion is mostly determined by gas-phase chemistry. Hydrogen addition has no influence on the C2 hydrocarbon yield, whereas aromatic selectivity is significantly reduced. Changes in selectivity are attributed to changes in methane conversion. Quantitative determination of the amount of coke deposited on the catalyst reveals a decrease by 1 order of magnitude when dosing up to 10% H2, while carbon deposits-downstream of the catalyst bed are suppressed to a much lower extent. These results suggest a process in which the produced hydrogen is partly recycled, maximizing the carbon selectivity to C2 hydrocarbons while minimizing both aromatics and, most crucially, formation of coke on the catalyst as well as further deposits-downstream.

Prediction of the chemical context for Buchwald‐Hartwig coupling reactions

We present machine learning models for predicting the chemical context for Buchwald‐Hartwig coupling reactions, i. e., what chemicals to add to the reactants to give a productive reaction. Using reaction data from in‐house electronic lab notebooks, we train two models: one based on single‐label data and one based on multi‐label data. Both models show excellent top‐3 accuracy of approximately 90 %, which suggests strong predictivity. Furthermore, there seems to be an advantage of including multi‐label data because the multi‐label model shows higher accuracy and better sensitivity for the individual contexts than the single‐label model. Although the models are performant, we also show that such models need to be re‐trained periodically as there is a strong temporal characteristic to the usage of different contexts. Therefore, a model trained on historical data will decrease in usefulness with time as newer and better contexts emerge and replace older ones. We hypothesize that such significant transitions in the context‐usage will likely affect any model predicting chemical contexts trained on historical data. Consequently, training context prediction models warrants careful planning of what data is used for training and how often the model needs to be re‐trained.

A recurrent neural network model for biomass gasification chemistry

A recurrent neural network model is built to predict the temporal evolution of chemical species during biomass gasification.

Machine learning enabling prediction of the bond dissociation enthalpy of hypervalent iodine from SMILES

Machine learning to create models on the basis of big data enables predictions from new input data. Many tasks formerly performed by humans can now be achieved by machine learning algorithms in various fields, including scientific areas. Hypervalent iodine compounds (HVIs) have long been applied as useful reactive molecules. The bond dissociation enthalpy (BDE) value is an important indicator of reactivity and stability. Experimentally measuring the BDE value of HVIs is difficult, however, and the value has been estimated by quantum calculations, especially density functional theory (DFT) calculations. Although DFT calculations can access the BDE value with high accuracy, the process is highly time-consuming. Thus, we aimed to reduce the time for predicting the BDE by applying machine learning. We calculated the BDE of more than 1000 HVIs using DFT calculations, and performed machine learning. Converting SMILES strings to Avalon fingerprints and learning using a traditional Elastic Net made it possible to predict the BDE value with high accuracy. Furthermore, an applicability domain search revealed that the learning model could accurately predict the BDE even for uncovered inputs that were not completely included in the training data.