Active Machine Learning for Chemical Engineers: a Bright Future Lies Ahead!

By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine learning algorithms are maturing, its applications are lacking behind. Three types of challenges faced by active machine learning are identified and ways to overcome them are discussed: the convincing of the experimental researcher, the flexibility of data creation, and the robustness of the active machine learning algorithms. A bright future lies ahead for active machine learning in chemical engineering thanks to increasing automation and more efficient algorithms to drive novel discoveries

Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead!

By combining machine learning with the design of experiments, thereby achieving so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical engineering. While active machine learning algorithms are maturing, their applications are falling behind. In this article, three types of challenges presented by active machine learning—namely, convincing the experimental researcher, the flexibility of data creation, and the robustness of active machine learning algorithms—are identified, and ways to overcome them are discussed. A bright future lies ahead for active machine learning in chemical engineering, thanks to increasing automation and more efficient algorithms that can drive novel discoveries

Periodic DFT Study of Benzene Adsorption on Pd(100) and Pd(110) at Medium and Saturation Coverage

Benzene adsorption on Pd(100) and Pd(110) has been investigated using periodic density functional theory (DFT) calculations. 4-Fold hollow geometries are preferentially adopted on both surfaces, and due to stronger repulsive interactions on Pd(100) a larger decrease in adsorption energy is calculated from medium to saturation coverage (∼120 kJ mol^–1) compared to Pd(110) (∼15 kJ mol^–1). On Pd(100), a slight energetic preference is calculated at saturation coverage for an adsorbate with two CC bonds parallel to the [011̅] direction. However, an adsorption geometry with alternately two types of benzene adsorbates, rotated azimuthally by 30° relative to one another, cannot be discarded since both geometries are compatible with ultraviolet photoemission spectroscopy (UPS) and high-resolution electron energy loss spectroscopy (HREELS) observations. On Pd(110), there is a slight energetic preference for the hollow(0) site relative to the hollow(15) and hollow(30) at saturation coverage, and their calculated electronic features match UPS experiments. For the hollow(30), calculated vibrational features are not compatible with HREELS experiments, indicating that benzene does not populate hollow(30) sites at saturation coverage. Calculated STM images confirm that the experimentally observed two-lobed protrusion separated by a single depression oriented with its direction some 50° from [11̅0] can only correspond to the hollow(15) adsorbate. Inclusion of van der Waals interactions (vdW-DFT) increases adsorption energies by some 50 kJ mol^–1, but the relative ordering of the various adsorption sites remains unaltered as compared to PW91

Kinetic Modeling of α‑Hydrogen Abstractions from Unsaturated and Saturated Oxygenate Compounds by Hydrogen Atoms

Hydrogen-abstraction reactions play a significant role in thermal biomass conversion processes, as well as regular gasification, pyrolysis, or combustion. In this work, a group additivity model is constructed that allows prediction of reaction rates and Arrhenius parameters of hydrogen abstractions by hydrogen atoms from alcohols, ethers, esters, peroxides, ketones, aldehydes, acids, and diketones in a broad temperature range (300–2000 K). A training set of 60 reactions was developed with rate coefficients and Arrhenius parameters calculated by the CBS-QB3 method in the high-pressure limit with tunneling corrections using Eckart tunneling coefficients. From this set of reactions, 15 group additive values were derived for the forward and the reverse reaction, 4 referring to primary and 11 to secondary contributions. The accuracy of the model is validated upon an ab initio and an experimental validation set of 19 and 21 reaction rates, respectively, showing that reaction rates can be predicted with a mean factor of deviation of 2 for the ab initio and 3 for the experimental values. Hence, this work illustrates that the developed group additive model can be reliably applied for the accurate prediction of kinetics of α-hydrogen abstractions by hydrogen atoms from a broad range of oxygenates