A public data-analytics competition was organized by the Novel Materials Discovery (NOMAD) Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000 (Al x Ga y In 1-x-y) 2 O 3 compounds. Its aim was to identify the best machinelearning (ML) model for the prediction of two key physical properties that are relevant for optoelectronic applications: the electronic bandgap energy and the crystalline formation energy. Here, we present a summary of the top-three ranked ML approaches. The first-place solution was based on a crystal-graph representation that is novel for the ML of properties of materials. The second-place model combined many candidate descriptors from a set of compositional, atomic-environment-based, and average structural properties with the light gradient-boosting machine regression model. The third-place model employed the smooth overlap of atomic position representation with a neural network. The Pearson correlation among the prediction errors of nine ML models (obtained by combining the top-three ranked representations with all three employed regression models) was examined by using the Pearson correlation to gain insight into whether the representation or the regression model determines the overall model performance. Ensembling relatively decorrelated models (based on the Pearson correlation) leads to an even higher prediction accuracy.

Metal–organic frameworks (MOFs) featuring multiple catalytic units are excellent platforms for heterogeneous catalysis. However, the synergism between multiple catalytic units for catalysis is far from being well understood. Herein, we reported the synthesis of a robust 2,2,6,6-tetramethylpiperidinyloxy (TEMPO) radical-functionalized Zr-MOF (UiO-68-TEMPO) in the form of single-crystalline and microsized crystals with varied missing linker defects. Detailed catalytic studies and theoretical calculations reveal that the synergistic effect between the TEMPO radicals and hydrophilic and defective Zr-nodes endows UiO-68-TEMPO with superior catalytic activity toward aerobic oxidation of alcohols. Our work not only offers a new route to design and synthesize highly effective MOF catalysts but also provides insights into the synergism between multiple catalytic sites.

Figure 19. (a) Crystal structure of Al 2 (OH) 2 TCPP-Co and schematic diagram of CO 2 RR by 2D MOF films. Reproduced with permission from ref. [148].

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