Correlation-Based Framework for Extraction of Insights from Quantum Chemistry Databases: Applications for Nanoclusters.

The amount of quantum chemistry (QC) data is increasing year by year due to the continuous increase of computational power and development of new algorithms. However, in most cases, our atom-level knowledge of molecular systems has been obtained by manual data analyses based on selected descriptors. In this work, we introduce a data mining framework to accelerate the extraction of insights from QC datasets, which starts with a featurization process that converts atomic features into molecular properties (AtoMF).

Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset.

Machine learning (ML) models can potentially accelerate the discovery of tailored materials by learning a function that maps chemical compounds into their respective target properties. In this realm, a crucial step is encoding the molecular systems into the ML model, in which the molecular representation plays a crucial role. Most of the representations are based on the use of atomic coordinates (structure); however, it can increase ML training and predictions' computational cost.

Ab initio insights into the structural, energetic, electronic, and stability properties of mixed CeZrO nanoclusters.

Mixed CeO2-ZrO2 nanoclusters have the potential to play a crucial role in nanocatalysis, however, the atomistic understanding of those nanoclusters is far from satisfactory. In this work, we report a density functional theory investigation combined with Spearman rank correlation analysis of the energetic, structural and electronic properties of mixed CenZr15-nO30 nanoclusters as a function of the composition (n = 0, 1,…,14, 15). For instance, we found a negative excess energy for all putative global minimum CenZr15-nO30 configurations with a minimum at about n = 6 (i.

Understanding the interplay between π-π and cation-π interactions in [janusene-Ag] host-guest systems: a computational approach.

Janusene is a symmetrical molecule that contains four benzene rings, with two of them forced to be in a vertical quasi-parallel face-to-face alignment. The unique physical nature of the transannular interactions and the electronic features of the region between the enforced parallel rings was tested with the complexation of Ag ion as a probe to evaluate the interplay between π-stacking and cation-π non-bonded interactions. The janusene framework and the [janusene-Ag] host-guest (H-G) system were analyzed through the introduction of substituent groups with different chemical natures and in different parts of the host framework.

investigation of the formation of ZrO-like structures upon the adsorption of Zr on the CeO(111) surface.

The adsorption of Zr on the CeO surfaces can lead to the formation of ZrO-like structures, which can play a crucial role in the catalytic properties of Ce Zr O as support for transition-metal catalysts; however, our atomistic understanding is far from satisfactory, and hence, it affects our capacity to engineer the combination of ZrO-CeO for catalysis applications. Here, we investigate the adsorption of Zr ( = 1 - 4) atoms on CeO(111) surfaces through density functional theory with the Hubbard model and bring new insights into the Zr-CeO interaction and the formation of ZrO-like structures on ceria. We found that the Zr atoms oxidize to Zr and strongly interact with the O anions, reducing the surface Ce cations to Ce (4 Ce atoms per Zr adatom), which stabilizes the system by more than 10 eV per Zr.