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). Then, it employs correlation coefficients (Pearson, Spearman, and Kendall) to investigate the AtoMF features relationship with a target property. We applied our framework to investigate three nanocluster systems, namely, PtnTM55-n, CenZr15-nO30, and (CHn + mH)/TM13. We found several interesting and consistent insights using Spearman and Kendall correlation coefficients, indicating that they are suitable for our approach; however, our results indicate that the Pearson coefficient is very sensitive to outliers and should not be used. Moreover, we highlight problems that can occur during this analysis and discuss how to handle them. Finally, we make available a new Python package that implements the proposed QC data mining framework, which can be used as is or modified to include new features.

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. Herein, we investigate the impact of choosing free-coordinate descriptors based on the Simplified Molecular Input Line Entry System (SMILES) representation, which can substantially reduce the ML predictions' computational cost. Therefore, we evaluate a feed-forward neural network (FNN) model's prediction performance over five feature selection methods and nine ground-state properties (including energetic, electronic, and thermodynamic properties) from a public data set composed of ∼130k organic molecules. Our best results reached a mean absolute error, close to chemical accuracy, of ∼0.05 eV for the atomization energies (internal energy at 0 K, internal energy at 298.15 K, enthalpy at 298.15 K, and free energy at 298.15 K). Moreover, for the atomization energies, the results obtained an out-of-sample error nine times less than the same FNN model trained with the Coulomb matrix, a traditional coordinate-based descriptor. Furthermore, our results showed how limited the model's accuracy is by employing such low computational cost representation that carries less information about the molecular structure than the most state-of-the-art methods.

Ab initio insights into the structural, energetic, electronic, and stability properties of mixed CenZr15-nO30 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.e., nearly 40% Ce), in which both the oxygen anion surroundings and cation radii play a crucial role in the stability and distribution of the chemical species. We found a strong energetic preference of Zr4+ cations to occupy larger coordination number sites, i.e., the nanocluster core region, while the Ce4+ cations are located near vacuum exposed O-rich regions. As expected, we obtained an almost linear decrease of the average bond lengths by replacing Ce4+ by Zr4+ cations in the (ZrO2)15 nanoclusters towards the formation of mixed CenZr15-nO30 nanoclusters, which resulted in a shift towards higher vibrational frequencies. Besides, we also observed that the relative stability of the mixed oxides is directly correlated with the increase (decrease) of the Zr d-state (Ce f-state) contribution to the highest occupied molecular orbital with the increase of the Zr content, hence driving the gap energy towards higher values.

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. The janusenes were used to tune both π-stacking and cation-π interactions. Three modes of substitution (facial, lateral, and facial plus lateral) were explored to gain insight into the effects of such scaffold modifications on the dual non-bonded interactions. Our findings suggest that the η2:η2 silver coordination is the most stable interaction mode between the silver ion and the janusene parallel rings. The cation-π interaction in the host structure is stabilized by electron donating groups and destabilized by electron withdrawing groups. The stabilization effect is highlighted with substitutions on the facial and facial plus lateral modes, with the latter being due to additive cooperation between the substituent groups. The bonding analysis indicates that [janusene-Ag]+ complexes containing electron withdrawing groups in the facial and facial plus lateral substitution schemes are more stabilized by orbital interactions. Complexes with electron donating groups and the complexes with substituent groups in the lateral position are mainly stabilized by electrostatic interactions, although in all cases orbital and dispersive interactions are also essential to describe the bonding situation. We envisage that these results will guide the development of new systems with increased cation-π interaction capability.

Ab initio investigation of the formation of ZrO2-like structures upon the adsorption of Zr n on the CeO2(111) surface.

The adsorption of Zr on the CeO2 surfaces can lead to the formation of ZrO2-like structures, which can play a crucial role in the catalytic properties of Ce x Zr1-x O2 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 ZrO2-CeO2 for catalysis applications. Here, we investigate the adsorption of Zr n (n = 1 - 4) atoms on CeO2(111) surfaces through density functional theory with the Hubbard model and bring new insights into the Zr-CeO2 interaction and the formation of ZrO2-like structures on ceria. We found that the Zr atoms oxidize to Zr4+ and strongly interact with the O2- anions, reducing the surface Ce4+ cations to Ce3+ (4 Ce atoms per Zr adatom), which stabilizes the system by more than 10 eV per Zr. As more Zr is adsorbed, the O2- species migrate from the sub-surface to interact with the on-surface Zr adatoms in hcp sites, producing a full ZrO2-like monolayer, which contributes to reduce the strain induced by the increased size of the Ce3+ cations compared with Ce4+. The simulated partial and full ZrO2-like structure thicknesses agree with the experimental measurements. In addition, we found an unprecedented trend for the on-surface Zr atoms: our calculations show that they are less stable than Zr replacing Ce3+ atoms from the first cation layer. Therefore, under sufficiently high temperatures, one expects the formation of a Ce2O3-like/c-ZrO2/CeO2 structure, which may completely change the reactivity of the surface.