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The empirical aspect of descriptor design in catalyst informatics, particularly when confronted with limited data, necessitates adequate prior knowledge for delving into unknown territories, thus presenting a logical contradiction. This study introduces a technique for automatic feature engineering (AFE) that works on small catalyst datasets, without reliance on specific assumptions or pre-existing knowledge about the target catalysis when designing descriptors and building machine-learning models. This technique generates numerous features through mathematical operations on general physicochemical features of catalytic components and extracts relevant features for the desired catalysis, essentially screening numerous hypotheses on a machine. AFE yields reasonable regression results for three types of heterogeneous catalysis: oxidative coupling of methane (OCM), conversion of ethanol to butadiene, and three-way catalysis, where only the training set is swapped. Moreover, through the application of active learning that combines AFE and high-throughput experimentation for OCM, we successfully visualize the machine's process of acquiring precise recognition of the catalyst design. Thus, AFE is a versatile technique for data-driven catalysis research and a key step towards fully automated catalyst discoveries. Descriptor design in catalyst informatics necessitates adequate prior knowledge for delving into unknown territories, particularly when confronted with limited data, thus presenting a logical contradiction. Here, the authors report a technique for automatic feature engineering that works on small catalyst datasets without reliance on pre-existing knowledge about the target catalysis. [ABSTRACT FROM AUTHOR]

Niobium-basedoxide nanoparticles have proven to be catalyticallyeffective toward hydrogenation of Mg where H2dissociationover the niobium-oxides is considered to be a crucial reaction step.However, the role of niobium oxides toward H2dissociationstill remains unclear as to what atomic configurations are responsiblefor the catalytic activity. H2dissociation over differentsurface planes of Nb, NbO, and Nb2O5as wellas small NbO clusters is performed by using a density functional theory.The calculations reveal that H2dissociation, adsorptionenergy, and the bond type between H and surfaces (clusters) dependon the atomic configurations of Nb and O. In particular, H2adsorption on NbO(111) is enhanced by O atoms without forming O–Hbond where the bond type of H and surface is found to be an electronpairing. Thus, NbO(111) could not only be a effective catalyst butalso potentially prevent the formation of MgO during the hydrogenationof Mg. The results should be helpful in developing and tailoring theefficient catalyst toward H2dissociation and hydrogenationof Mg. [ABSTRACT FROM AUTHOR]

The article presents chemical equations related to the article "Pd(II)-Catalyzed Ligand Controlled Synthesis of Methyl 1-Benzyl-1H-indole-3-carboxylates and Bis(1-benzyl-1H-indol-3-yl)methanones" by R. Shen and others, published in the periodical "Organic and Biomolecular Chemistry" in 2014.

The article presents chemical equations to a article related to cyclization, carbonylation, and cyclization coupling reaction of sulfides with the Palladium(II)" by Y. Jiang and others, published in the periodical "Organic & Biomolecular Chemistry" in 2014.

This novel ring closure reaction involves tandem OH insertion/Conia-ene cyclization under cooperative Rh(II)/Zn(II) catalysis, providing access to substituted tetrahydrofurans with complete (E)-selection in the case of nonterminal alkynes (XII). [ABSTRACT FROM AUTHOR]

The article presents chemical equations for the article "Direct Use of Allylic Alcohols for Palladium-Catalyzed Synthesis of 3-Allylbenzo[b]thiophenes, Benzofurans and Indoles in Aqueous Media' by Y.D. Dhage and others, and published in the journal "RSC Advances" in 2015.