We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are based on conventional atom-centered symmetry functions (ACSFs) but overcome the undesirable scaling of the latter with an increasing number of different elements in a chemical system. The performance of these two descriptors is compared using them as inputs in high-dimensional neural network potentials (HDNNPs), employing the molecular structures and associated enthalpies of the 133 855 molecules containing up to five different elements reported in the QM9 database as reference data. A substantially smaller number of wACSFs than ACSFs is needed to obtain a comparable spatial resolution of the molecular structures. At the same time, this smaller set of wACSFs leads to a significantly better generalization performance in the machine learning potential than the large set of conventional ACSFs. Furthermore, we show that the intrinsic parameters of the descriptors can in principle be optimized with a genetic algorithm in a highly automated manner. For the wACSFs employed here, we find however that using a simple empirical parametrization scheme is sufficient in order to obtain HDNNPs with high accuracy.

Metal−organic frameworks (MOFs) have garnered interest as potential solid sorbent materials for postcombustion CO 2 capture. With a seemingly infinite design space, high-throughput computational screening of MOFs has developed into an effective tool for the development of new materials. In this work, machine learning (ML) has been used to develop accurate quantitative structure−property relationship (QSPR) models to rapidly predict the CO 2 working capacity and CO 2 /N 2 selectivity at the low-pressure conditions relevant to postcombustion carbon capture (0.15 bar CO 2 , 0.85 bar N 2 ). A database of over 340 000 MOFs constructed from hundreds of types of building units arranged in over 1000 net topologies was used to train and test the models. Neural network ML models were optimized using six geometric descriptors along with three so-called chemical descriptors, namely, the atomic property-weighted radial distribution function (AP-RDF) and some variants thereof, the bag-of-atoms, and the chemical motif density descriptors. The ML models built using geometric descriptors alone resulted in test set correlation R 2 values of only 0.71 and 0.75 for CO 2 working capacity and CO 2 /N 2 selectivity, respectively. ML models built with a single type of chemical descriptor all outperformed the geometry-only models giving R 2 values ranging from 0.83 to 0.94 with the AP-RDF model being the most accurate. Overall, the best model was built using a combination of AP-RDF, chemical motif, and geometric descriptors (R 2 = 0.96 when predicting the CO 2 working capacity and R 2 = 0.95 for the selectivity). To date, these are the most accurate ML models for predicting low-pressure gas uptake of MOFs. The combined model was able to capture 994 of the true top 1000 MOFs (from a test set of ∼70 000) within the top 5000 MOFs as predicted by the model with CO 2 working capacity as the target. Thus, if the ML model were used to prescreen materials for more compute intensive GCMC simulations, then it would result in a greater than 10 times speed up while still capturing >99% of high-performing materials. These results highlight the importance of chemical descriptors in predicting low-pressure gas adsorption properties in nanoporous materials.

This review describes major advances in the use of functionalized molecular metal oxides (polyoxometalates, POMs) as water oxidation catalysts under electrochemical conditions. The fundamentals of POM-based water oxidation are described, together with a brief overview of general approaches to designing POM water oxidation catalysts. Next, the use of POMs for homogeneous, solution-phase water oxidation is described together with a summary of theoretical studies shedding light on the POM-WOC mechanism. This is followed by a discussion of heterogenization of POMs on electrically conductive substrates for technologically more relevant application studies. The stability of POM water oxidation catalysts is discussed, using select examples where detailed data is already available. The review finishes with an outlook on future perspectives and emerging themes in electrocatalytic polyoxometalate-based water oxidation research.

Despite their technological importance for water splitting, the reaction mechanisms of most water oxidation catalysts (WOCs) are poorly understood. This paper combines theoretical and experimental methods to reveal mechanistic insights...