The process employed to discover new materials for specific applications typically utilizes screening of large compound libraries. In this approach, the performance of a compound is correlated to the properties of elements referred to as descriptors. In the effort described below, we developed a simple and efficient machine learning (ML) model for predicting adsorption energies of CH 4 related species, namely, CH 3 , CH 2 , CH, C, and H on the Cubased alloys. The developed ML model predicted the DFT-calculated adsorption energies with 12 descriptors, which are readily available values for the selected elements. The predictive accuracy of four regression methods (ordinary linear regression by least-squares (OLR), random forest regression (RFR), gradient boosting regression (GBR), and extra tree regression (ETR)) with different numbers of descriptors and different test-set/training-set ratios was quantitatively evaluated using statistical cross validations. Among four types of regression methods, we have found that ETR gave the best performance in predicting the adsorption energies with the average root mean squared errors (RMSEs) below 0.3 eV. Strikingly, despite its simplicity and low computational cost, this model can predict the adsorption energies on a range of Cu-based alloy models (46 in total number) as calculated by using DFT. In addition, we show the ML prediction for the differences in the adsorption energies of CH 3 and CH 2 on the same surface. This would be of great importance especially when designing the selective catalytic reaction processes to suppress the undesired overreactions. The accuracy and simplicity of the developed system suggest that adsorption energies can be readily predicted without time-consuming DFT calculations, and eventually, this would allow us to predict the catalytic performances of the solid catalysts.

Thiolate-bridged diruthenium complexes bearing pendent ethers have been found to work as effective catalysts toward the oxidation of molecular dihydrogen into protons and electrons in water. The pendent ether moiety in the complex plays an important role to facilitate the proton transfer between the metal center and the external proton acceptor.

Molecular graphs are one of the established representations for small molecules, and even steric or electronic information can be encoded as node and edge features. Naturally, graph neural networks have been intensively investigated to solve various chemical problems at molecular levels. However, it remains unclear how to encode relevant chemical information into graphs. We investigate this problem by proposing three models of graph neural networks with self-attention mechanisms at different levels to adaptively select relevant chemical information for each input. Using neural graph fingerprint (NFP) as a baseline, we introduce three types of attention mechanisms on the top of NFPs. Our experimental evaluations suggest that introducing these self-attention mechanisms contributes to not only improving the prediction accuracy but also providing quantitative interpretation using obtained attention coefficients.

Thiolate‐bridged diruthenium complexes bearing pendent ethers have been found to work as effective catalysts toward the oxidation of molecular dihydrogen into protons/electrons in water. The pendent ether moiety plays an important role to facilitate the proton transfer between the metal center and the external proton acceptor. A theoretical study on the proposed reaction pathway is also described using DFT calculations. More information can be found in the Communication by K. Sakata and Y. Nishibayashi et al. on page 1007 ff.