How Partial Atomic Charges and Bonding Orbitals Affect the Reactivity of Aluminum Clusters with Water?

Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors

Machine-learned computational chemistry has led to a paradoxical situation in which molecular properties can be accurately predicted, but they are difficult to interpret. Explainable AI (XAI) tools can be used to analyze complex models, but they are highly dependent on the AI technique and the origin of the reference data. Alternatively, interpretable real-space tools can be employed directly, but they are often expensive to compute. To address this dilemma between explainability and accuracy, we developed SchNet4AIM, a SchNet-based architecture capable of dealing with local one-body (atomic) and two-body (interatomic) descriptors. The performance of SchNet4AIM is tested by predicting a wide collection of real-space quantities ranging from atomic charges and delocalization indices to pairwise interaction energies. The accuracy and speed of SchNet4AIM breaks the bottleneck that has prevented the use of real-space chemical descriptors in complex systems. We show that the group delocalization indices, arising from our physically rigorous atomistic predictions, provide reliable indicators of supramolecular binding events, thus contributing to the development of Explainable Chemical Artificial Intelligence (XCAI) models.

Advances in Naked Metal Clusters for Catalysis

The properties of sub-nano metal clusters are governed by quantum confinement and their large surface-to-bulk ratios, atomically precise compositions and geometric/electronic structures. Advances in metal clusters lead to new opportunities in diverse aspects of sciences including chemo-sensing, bio-imaging, photochemistry, and catalysis. Naked metal clusters having synergic multiple active sites and coordinative unsaturation and tunable stability/activity enable researchers to design atomically precise metal catalysts with tailored catalysis for different reactions. Here we summarize the progress of ligand-free naked metal clusters for catalytic applications. It is anticipated that this review helps to better understand the chemistry of small metal clusters and facilitates the design and development of new catalysts for potential applications.

An Unsupervised Machine Learning Approach for the Automatic Construction of Local Chemical Descriptors

Condensing the many physical variables defining a chemical system into a fixed-size array poses a significant challenge in the development of chemical Machine Learning (ML). Atom Centered Symmetry Functions (ACSFs) offer an intuitive featurization approach by means of a tedious and labor-intensive selection of tunable parameters. In this work, we implement an unsupervised ML strategy relying on a Gaussian Mixture Model (GMM) to automatically optimize the ACSF parameters. GMMs effortlessly decompose the vastness of the chemical and conformational spaces into well-defined radial and angular clusters, which are then used to build tailor-made ACSFs. The unsupervised exploration of the space has demonstrated general applicability across a diverse range of systems, spanning from various unimolecular landscapes to heterogeneous databases. The impact of the sampling technique and temperature on space exploration is also addressed, highlighting the particularly advantageous role of high-temperature Molecular Dynamics (MD) simulations. The reliability of the resulting features is assessed through the estimation of the atomic charges of a prototypical capped amino acid and a heterogeneous collection of CHON molecules. The automatically constructed ACSFs serve as high-quality descriptors, consistently yielding typical prediction errors below 0.010 electrons bound for the reported atomic charges. Altering the spatial distribution of the functions with respect to the cluster highlights the critical role of symmetry rupture in achieving significantly improved features. More specifically, using two separate functions to describe the lower and upper tails of the cluster results in the best performing models with errors as low as 0.006 electrons. Finally, the effectiveness of finely tuned features was checked across different architectures, unveiling the superior performance of Gaussian Process (GP) models over Feed Forward Neural Networks (FFNNs), particularly in low-data regimes, with nearly a 2-fold increase in prediction quality. Altogether, this approach paves the way toward an easier construction of local chemical descriptors, while providing valuable insights into how radial and angular spaces should be mapped. Finally, this work opens the possibility of encoding many-body information beyond angular terms into upcoming ML features.

On the nature of Con±/0 clusters reacting with water and oxygen

Bulk cobalt does not react with water at room temperature, but cobalt nanometals could yield corrosion at ambient conditions. Insights into the cobalt cluster reactions with water and oxygen enable us to better understand the interface reactivity of such nanometals. Here we report a comprehensive study on the gas-phase reactions of Con±/0 clusters with water and oxygen. All these Con±/0 clusters were found to react with oxygen, but only anionic cobalt clusters give rise to water dissociation whereas the cationic and neutral ones are limited to water adsorption. We elucidate the influences of charge states, bonding modes and dehydrogenation mechanism of water on typical cobalt clusters. It is unveiled that the additional electron of anionic Con- clusters is not beneficial to H2O adsorption, but allows for thermodynamics- and kinetics-favourable H atom transfer and dehydrogenation reactions. Apart from the charge effect, size effect and spin effect play a subtle role in the reaction process. The synergy of multiple metal sites in Con- clusters reduces the energy barrier of the rate-limiting step enabling hydrogen release. This finding of water dissociation on cobalt clusters put forward new connotations on the activity series of metals, providing new insights into the corrosion mechanism of cobalt nanometals.

Developing a User-Friendly Code for the Fast Estimation of Well-Behaved Real-Space Partial Charges

The Quantum Theory of Atoms in Molecules (QTAIM) provides an intuitive, yet physically sound, strategy to determine the partial charges of any chemical system relying on the topology induced by the electron density ρ(r) . In a previous work [J. Chem. Phys. 2022, 156, 014112], we introduced a machine learning (ML) model for the computation of QTAIM charges of C, H, O, and N atoms at a fraction of the conventional computational cost. Unfortunately, the independent nature of the atomistic predictions implies that the raw atomic charges may not necessarily reconstruct the exact molecular charge, limiting the applicability of the latter in the chemistry realm. Trying to solve such an inconvenience, we introduce NNAIMGUI, a user-friendly code which combines the inferring abilities of ML with an equilibration strategy to afford adequately behaved partial charges. The performance of this approach is put to the test in a variety of scenarios including interpolation and extrapolation regimes (e.g chemical reactions) as well as large systems. The results of this work prove that the equilibrated charges retain the chemically accurate behavior reproduced by the ML models. Furthermore, NNAIMGUI is a fully flexible architecture allowing users to train and use tailor-made models targeted at any atomic property of choice. In this way, the GUI-interfaced code, equipped with visualization utilities, makes the computation of real-space atomic properties much more appealing and intuitive, paving the way toward the extension of QTAIM related descriptors beyond the theoretical chemistry community.

NNAIMQ: A neural network model for predicting QTAIM charges

Atomic charges provide crucial information about the electronic structure of a molecular system. Among the different definitions of these descriptors, the one proposed by the Quantum Theory of Atoms in Molecules (QTAIM) is particularly attractive given its invariance against orbital transformations although the computational cost associated with their calculation limits its applicability. Given that Machine Learning (ML) techniques have been shown to accelerate orders of magnitude the computation of a number of quantum mechanical observables, in this work, we take advantage of ML knowledge to develop an intuitive and fast neural network model (NNAIMQ) for the computation of QTAIM charges for C, H, O, and N atoms with high accuracy. Our model has been trained and tested using data from quantum chemical calculations in more than 45 000 molecular environments of the near-equilibrium CHON chemical space. The reliability and performance of NNAIMQ have been analyzed in a variety of scenarios, from equilibrium geometries to molecular dynamics simulations. Altogether, NNAIMQ yields remarkably small prediction errors, well below the 0.03 electron limit in the general case, while accelerating the calculation of QTAIM charges by several orders of magnitude.

Hydrogen release from a single water molecule on Vn+ (3 ≤ n ≤ 30)

Water and its interactions with metals are closely bound up with human life, and the reactivity of metal clusters with water is of fundamental importance for the understanding of hydrogen generation. Here a prominent hydrogen evolution reaction (HER) of single water molecule on vanadium clusters V_n⁺ (3 ≤ n ≤ 30) is observed in the reaction of cationic vanadium clusters with water at room temperature. The combined experimental and theoretical studies reveal that the wagging vibrations of a V-OH group give rise to readily formed V-O-V intermediate states on V_n⁺ (n ≥ 3) clusters and allow the terminal hydrogen to interact with an adsorbed hydrogen atom, enabling hydrogen release. The presence of three metal atoms reduces the energy barrier of the rate-determining step, giving rise to an effective production of hydrogen from single water molecules. This mechanism differs from dissociative chemisorption of multiple water molecules on aluminium cluster anions, which usually proceeds by dissociative chemisorption of at least two water molecules at multiple surface sites followed by a recombination of the adsorbed hydrogen atoms.

Reactivity of Cobalt Clusters Con±/0 with Ammonia: Co3+ Cluster Catalysis for NH3 Dehydrogenation

A customized reflection time-of-flight (Re-TOF) mass spectrometer combined with a 177 nm deep-ultraviolet laser has enabled us to observe well-resolved cobalt clusters Co_n^±/0 and perform a comprehensive study of their reactivity with ammonia (NH₃). The anions Co_n^- are found to be inert, the neutrals allow the adsorption of multiple NH₃ molecules, while the cationic Co_n⁺ clusters readily react with NH₃ giving rise to dehydrogenation. However, incidental dehydrogenation of NH₃ on Co_n⁺ is only observed for n ≥ 3. The dramatic charge- and size-dependent reactivities of Co_n^±/0 clusters with NH₃ are studied by the density functional theory (DFT)-calculation results of energetics, density of states, orbital interactions, and reaction dynamics. We illustrate the dehydrogenation from two NH₃ molecules, where a significantly reduced transition-state energy barrier is found pertaining to the dimolecular co-catalysis effect. The reactivity of Co₃⁺ with NH₃ is illustrated showing effective catalysis for N-H dissociation to produce hydrogen applicable for designing ammonia fuel cells.