Nanoparticles have a wide range of applications as catalysts. Their catalytic and electronic properties differ from those of materials with flat surfaces and bulk materials. First-principles calculations of real system nanoparticles, which use nanoparticle models based on real shapes extracted from experimental observations, are essential for studying these properties to facilitate the computational design of new catalysts. In this article, we review first-principles studies of models of real systems of monometallic, bimetallic, and supported nanoparticles. The stability, electronic structure, hydrogen absorption behavior, and small molecule adsorption behavior are reviewed, and advances in first-principles calculations of real system nanoparticles are presented. Further, a combination of machine learning and first-principles studies is also considered. Future perspectives are discussed on the basis of these examples.
Models for predicting properties/activities of materials based on machine learning can lead to the discovery of new mechanisms underlying properties/activities of materials. However, methods for constructing models that exhibit both high prediction accuracy and interpretability remain a work in progress because the prediction accuracy and interpretability exhibit a trade-off relationship. In this study, we propose a new model-construction method that combines decision tree (DT) with random forests (RF); which we therefore call DT-RF. In DT-RF, the datasets to be analyzed are divided by a DT model, and RF models are constructed for each subdataset. This enables global interpretation of the data based on the DT model, while the RT models improve the prediction accuracy and enable local interpretations. Case studies were performed using three datasets, namely, those containing data on the boiling point of compounds, their water solubility, and the transition temperature of inorganic superconductors. We examined the proposed method in terms of its validity, prediction accuracy, and interpretability.