H·(H2O)n Clusters: Microsolvation of the Hydrogen Atom via Molecular ab Initio Gradient Embedded Genetic Algorithm (GEGA)

How the addition of atomic hydrogen to a multiple bond can be catalyzed by water molecules

Observational data show complex organic molecules in the interstellar medium (ISM). Hydrogenation of small unsaturated carbon double bond could be one way for molecular complexification. It is important to understand how such reactivity occurs in the very cold and low-pressure ISM. Yet, there is water ice in the ISM, either as grain or as mantle around grains. Therefore, the addition of atomic hydrogen on double-bonded carbon in a series of seven molecules have been studied and it was found that water catalyzes this reaction. The origin of the catalysis is a weak charge transfer between the π MO of the unsaturated molecule and H atom, allowing a stabilizing interaction with H2O. This mechanism is rationalized using the non-covalent interaction and the quantum theory of atoms in molecules approaches.

Tuning Reinforcement Learning Parameters for Cluster Selection to Enhance Evolutionary Algorithms

The ability to find optimal molecular structures with desired properties is a popular challenge, with applications in areas such as drug discovery. Genetic algorithms are a common approach to global minima molecular searches due to their ability to search large regions of the energy landscape and decrease computational time via parallelization. In order to decrease the amount of unstable intermediate structures being produced and increase the overall efficiency of an evolutionary algorithm, clustering was introduced in multiple instances. However, there is little literature detailing the effects of differentiating the selection frequencies between clusters. In order to find a balance between exploration and exploitation in our genetic algorithm, we propose a system of clustering the starting population and choosing clusters for an evolutionary algorithm run via a dynamic probability that is dependent on the fitness of molecules generated by each cluster. We define four parameters, MFavOvrAll-A, MFavClus-B, NoNewFavClus-C, and Select-D, that correspond to a reward for producing the best structure overall, a reward for producing the best structure in its own cluster, a penalty for not producing the best structure, and a penalty based on the selection ratio of the cluster, respectively. A reward increases the probability of a cluster's future selection, while a penalty decreases it. In order to optimize these four parameters, we used a Gaussian distribution to approximate the evolutionary algorithm performance of each cluster and performed a grid search for different parameter combinations. Results show parameter MFavOvrAll-A (rewarding clusters for producing the best structure overall) and parameter Select-D (appearance penalty) have a significantly larger effect than parameters MFavClus-B and NoNewFavClus-C. In order to produce the most successful models, a balance between MFavOvrAll-A and Select-D must be made that reflects the exploitation vs exploration trade-off often seen in reinforcement learning algorithms. Results show that our reinforcement-learning-based method for selecting clusters outperforms an unclustered evolutionary algorithm for quinoline-like structure searches.

Exploration of Free Energy Surface of the Au10 Nanocluster at Finite Temperature

The first step in comprehending the properties of Au10 clusters is understanding the lowest energy structure at low and high temperatures. Functional materials operate at finite temperatures; however, energy computations employing density functional theory (DFT) methodology are typically carried out at zero temperature, leaving many properties unexplored. This study explored the potential and free energy surface of the neutral Au10 nanocluster at a finite temperature, employing a genetic algorithm coupled with DFT and nanothermodynamics. Furthermore, we computed the thermal population and infrared Boltzmann spectrum at a finite temperature and compared it with the validated experimental data. Moreover, we performed the chemical bonding analysis using the quantum theory of atoms in molecules (QTAIM) approach and the adaptive natural density partitioning method (AdNDP) to shed light on the bonding of Au atoms in the low-energy structures. In the calculations, we take into consideration the relativistic effects through the zero-order regular approximation (ZORA), the dispersion through Grimme's dispersion with Becke-Johnson damping (D3BJ), and we employed nanothermodynamics to consider temperature contributions. Small Au clusters prefer the planar shape, and the transition from 2D to 3D could take place at atomic clusters consisting of ten atoms, which could be affected by temperature, relativistic effects, and dispersion. We analyzed the energetic ordering of structures calculated using DFT with ZORA and single-point energy calculation employing the DLPNO-CCSD(T) methodology. Our findings indicate that the planar lowest energy structure computed with DFT is not the lowest energy structure computed at the DLPN0-CCSD(T) level of theory. The computed thermal population indicates that the 2D elongated hexagon configuration strongly dominates at a temperature range of 50-800 K. Based on the thermal population, at a temperature of 100 K, the computed IR Boltzmann spectrum agrees with the experimental IR spectrum. The chemical bonding analysis on the lowest energy structure indicates that the cluster bond is due only to the electrons of the 6 s orbital, and the Au d orbitals do not participate in the bonding of this system.

Role of H3O· Radical in the Degradation of Fuel Cell Proton-Exchange Membranes

Membrane durability in proton-exchange membrane fuel cells (PEMFCs) is one of the major obstacles limiting its applications, especially in heavy-duty vehicles. Membrane degradation reactions are thought to be attacks by radicals such as hydroxyl (HO^•) or hydrogen atom (H^•) generated during fuel cell operation. For the H^• case, computational modeling results have suggested that the reaction between H^• and the sulfonic group should be the dominant degradation pathway. However, experimental work implies that the tertiary fluorine (t-F) attack is the dominant H^• reaction pathway, apparently contradicting the theoretical prediction. Based on previous experimental evidence on isotopic substitution, we postulate that the hydronium radical (H₃O^•) might be present in PEMFCs. Our ab initio modeling indicates that this radical can be stabilized by the sulfonic anion on the polymer side chain. With the assistance of explicit water, the polymer side chain can undergo a conformational change, leading to a greatly reduced barrier for the t-F degradation reaction. Thus, our H₃O^• hypothesis is able to explain not only the previous isotopic substitution experiment but also why the t-F degradation reaction is a highly plausible H^• degradation mechanism for proton-exchange membranes. To our knowledge, this is the first suggestion that H₃O^• radicals could be present in electrochemical devices with both experimental and theoretical support.

Molecular design of redox carriers for electrochemical CO2 capture and concentration

Developing improved methods for CO₂ capture and concentration (CCC) is essential to mitigating the impact of our current emissions and can lead to carbon net negative technologies. Electrochemical approaches for CCC can achieve much higher theoretical efficiencies compared to the thermal methods that have been more commonly pursued. The use of redox carriers, or molecular species that can bind and release CO₂ depending on their oxidation state, is an increasingly popular approach as carrier properties can be tailored for different applications. The key requirements for stable and efficient redox carriers are discussed in the context of chemical scaling relationships and operational conditions. Computational and experimental approaches towards developing redox carriers with optimal properties are also described.

Ensemble representation of catalytic interfaces: soloists, orchestras, and everything in-between

Catalytic systems are complex and dynamic, exploring vast chemical spaces on multiple timescales. In this perspective, we discuss the dynamic behavior of fluxional, heterogeneous thermal and electrocatalysts and the ensembles of many isomers which govern their behavior. We develop a new paradigm in catalysis theory in which highly fluxional systems, namely sub-nano clusters, isomerize on a much shorter timescale than that of the catalyzed reaction, so macroscopic properties arise from the thermal ensemble of isomers, not just the ground state. Accurate chemical predictions can only be reached through a many-structure picture of the catalyst, and we explain the breakdown of conventional methods such as linear scaling relations and size-selected prevention of sintering. We capitalize on the forward-looking discussion of the means of controlling the size of these dynamic ensembles. This control, such that the most effective or selective isomers can dominate the system, is essential for the fluxional catalyst to be practicable, and their targeted synthesis to be possible. It will also provide a fundamental lever of catalyst design. Finally, we discuss computational tools and experimental methods for probing ensembles and the role of specific isomers. We hope that catalyst optimization using chemically informed descriptors of ensemble nature and size will become a new norm in the field of catalysis and have broad impacts in sustainable energy, efficient chemical production, and more.

Inverse molecular design of alkoxides and phenoxides for aqueous direct air capture of CO2

Aqueous direct air capture (DAC) is a key technology toward a carbon negative infrastructure. Developing sorbent molecules with water and oxygen tolerance and high CO₂ binding capacity is therefore highly desired. We analyze the CO₂ absorption chemistries on amines, alkoxides, and phenoxides with density functional theory calculations, and perform inverse molecular design of the optimal sorbent. The alkoxides and phenoxides are found to be more suitable for aqueous DAC than amines thanks to their water tolerance (lower pK_a prevents protonation by water) and capture stoichiometry of 1:1 (2:1 for amines). All three molecular systems are found to generally obey the same linear scaling relationship (LSR) between [Formula: see text] and [Formula: see text], since both CO₂ and proton are bonded to the nucleophilic (alkoxy or amine) binding site through a majorly [Formula: see text] bonding orbital. Several high-performance alkoxides are proposed from the computational screening. Phenoxides have comparatively poorer correlation between [Formula: see text] and [Formula: see text], showing promise for optimization. We apply a genetic algorithm to search the chemical space of substituted phenoxides for the optimal sorbent. Several promising off-LSR candidates are discovered. The most promising one features bulky ortho substituents forcing the CO₂ adduct into a perpendicular configuration with respect to the aromatic ring. In this configuration, the phenoxide binds CO₂ and a proton using different molecular orbitals, thereby decoupling the [Formula: see text] and [Formula: see text]. The [Formula: see text] trend and off-LSR behaviors are then confirmed by experiments, validating the inverse molecular design framework. This work not only extensively studies the chemistry of the aqueous DAC, but also presents a transferrable computational workflow for understanding and optimization of other functional molecules.

Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions-so-called operando modeling-is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for example, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3-55 atoms with mean absolute errors in the range of 0.11-0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure-property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal-metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3-55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

Modeling microsolvation clusters with electronic-structure calculations guided by analytical potentials and predictive machine learning techniques

We propose a new methodology to study, at the density functional theory (DFT) level, the clusters resulting from the microsolvation of alkali-metal ions with rare-gas atoms. The workflow begins with a global optimization search to generate a pool of low-energy minimum structures for different cluster sizes. This is achieved by employing an analytical potential energy surface (PES) and an evolutionary algorithm (EA). The next main stage of the methodology is devoted to establish an adequate DFT approach to treat the microsolvation system, through a systematic benchmark study involving several combinations of functionals and basis sets, in order to characterize the global minimum structures of the smaller clusters. In the next stage, we apply machine learning (ML) classification algorithms to predict how the low-energy minima of the analytical PES map to the DFT ones. An early and accurate detection of likely DFT local minima is extremely important to guide the choice of the most promising low-energy minima of large clusters to be re-optimized at the DFT level of theory. In this work, the methodology was applied to the Li+Krn (n = 2-14 and 16) microsolvation clusters for which the most competitive DFT approach was found to be the B3LYP-D3/aug-pcseg-1. Additionally, the ML classifier was able to accurately predict most of the solutions to be re-optimized at the DFT level of theory, thereby greatly enhancing the efficiency of the process and allowing its applicability to larger clusters.

Exploration of Free Energy Surface and Thermal Effects on Relative Population and Infrared Spectrum of the Be6B11- Flux-Ional Cluster

The starting point to understanding cluster properties is the putative global minimum and all the nearby local energy minima; however, locating them is computationally expensive and difficult. The relative populations and spectroscopic properties that are a function of temperature can be approximately computed by employing statistical thermodynamics. Here, we investigate entropy-driven isomers distribution on Be₆B₁₁⁻ clusters and the effect of temperature on their infrared spectroscopy and relative populations. We identify the vibration modes possessed by the cluster that significantly contribute to the zero-point energy. A couple of steps are considered for computing the temperature-dependent relative population: First, using a genetic algorithm coupled to density functional theory, we performed an extensive and systematic exploration of the potential/free energy surface of Be₆B₁₁⁻ clusters to locate the putative global minimum and elucidate the low-energy structures. Second, the relative populations’ temperature effects are determined by considering the thermodynamic properties and Boltzmann factors. The temperature-dependent relative populations show that the entropies and temperature are essential for determining the global minimum. We compute the temperature-dependent total infrared spectra employing the Boltzmann factor weighted sums of each isomer’s infrared spectrum and find that at finite temperature, the total infrared spectrum is composed of an admixture of infrared spectra that corresponds to the spectra of the lowest-energy structure and its isomers located at higher energies. The methodology and results describe the thermal effects in the relative population and the infrared spectra.

Ensembles of Metastable States Govern Heterogeneous Catalysis on Dynamic Interfaces

Heterogeneous catalysis is at the heart of the chemical industry. Being able to tune and design efficient catalysts for processes of interest is of the utmost importance, and for this, a molecular-level understanding of heterogeneous catalysts is the first step and indeed a prime focus of modern catalysis research. For a long time, the single most thermodynamically stable structure of the catalytic interface attained under the reaction conditions had been envisioned as the reactive phase. However, some catalytic interfaces continue to undergo structural dynamics in the steady state, triggered by high temperatures and pressures and binding and changing reagents. Among particularly dynamic interfaces are such widely used catalysts as crystalline and amorphous surfaced supporting (sub)nanometallic clusters. Recently, it became clear that this dynamic fluxionality causes the supported clusters to populate many distinct structural and stoichiometric states under catalytic conditions. Hence, the catalytic interface should be viewed as an evolving statistical ensemble of many structures (rather than one structure). Every member in the ensemble contributes to the properties of the catalyst differently, in proportion to its probability of being populated. This new notion flips the established paradigm and calls for a new theory, new modeling approaches, operando measurements, and updated design strategies. The statistical ensemble nature of surface-supported subnanocluster catalysts can be exemplified by oxide-supported and adsorbate-covered Pt, Pd, Cu, and CuPd clusters, which are catalytic toward oxidative and nonoxidative dehydrogenation. They have access to a variety of 3D and quasi-2D shapes. The compositions of their thermal ensembles are dependent on the cluster size, leading to size-specific catalytic activities and the famous "every atom counts" phenomenon. The support and adsorbates affect catalyst structures, and the state of the reacting species causes the ensemble to change in every reaction intermediate. The most stable member of the ensemble dominates the thermodynamic properties of the corresponding intermediate, whereas the kinetics can be determined by more active but less populated metastable catalyst states, and that suggests that many earlier studies might have overlooked the actual active sites. Both effects depend on the relative time scales of catalyst restructuring and reaction dynamics. The catalyst may routinely operate off-equilibrium. Ensemble phenomena lead to surprising exceptions from established rules of catalysis, such as scaling relations and Arrhenius behavior. Catalyst deactivation is also an ensemble property, and its extent of mitigation can be predicted through the new paradigm. These findings were enabled by advances in theory, such as global optimization and subsequent utilization of multiple local minima and pathways sampling as well as operando catalyst characterization. The fact that the per-site and per-species resolution is needed for the description and prediction of catalyst properties gives theory the central role in catalysis research, as most experiments provide ensemble-average information and cannot detect the crucial minority species that may be responsible for the catalytic activity.

Carbon rings decorated with group 14 elements: new aromatic clusters containing planar tetracoordinate carbon

A simple and chemically intuitive approach is used to design ptC-containing E–C clusters (E = Si–Pb).

Metastable Structures in Cluster Catalysis from First-Principles: Structural Ensemble in Reaction Conditions and Metastability Triggered Reactivity

Reactivity studies on catalytic transition metal clusters are usually performed on a single global minimum structure. With the example of a Pt₁₃ cluster under a pressure of hydrogen, we show from first-principle calculations that low energy metastable structures of the cluster can play a major role for catalytic reactivity and that hence consideration of the global minimum structure alone can severely underestimate the activity. The catalyst is fluxional with an ensemble of metastable structures energetically accessible at reaction conditions. A modified genetic algorithm is proposed to comprehensively search for the low energy metastable ensemble (LEME) structures instead of merely the global minimum structure. In order to reduce the computational cost of density functional calculations, a high dimensional neural network potential is employed to accelerate the exploration. The presence and influence of LEME structures during catalysis is discussed by the example of H covered Pt₁₃ clusters for two reactions of major importance: hydrogen evolution reaction and methane activation. The results demonstrate that although the number of accessible metastable structures is reduced under reaction condition for Pt₁₃ clusters, these metastable structures can exhibit high activity and dominate the observed activity due to their unique electronic or structural properties. This underlines the necessity of thoroughly exploring the LEME structures in catalysis simulations. The approach enables one to systematically address the impact of isomers in catalysis studies, taking into account the high adsorbate coverage induced by reaction conditions.

Exploring the potential energy surface of small lead clusters using the gradient embedded genetic algorithm and an adequate treatment of relativistic effects

Theoretical inclusion of relativistic effects (scalar and spin–orbit) play a crucial role to assure an adequate structural assignment on lead clusters.

Theoretical design of stable hydride clusters: isoelectronic transformation in the EnAl4−nH7+n− series

Design of stable hydrogen-rich metallic hydrides through substitutions of one aluminum atom by one E–H unit in the Al₄H₇⁻ cluster (E = Be, Mg, Ca, Sr and Ba atoms).

Boron avoids cycloalkane-like structures in the LinBnH2n series

The stability of cycloalkane-like structures in the series Li_nB_nH_2n is analyzed using ab initio calculations. Neither of these cyclic species is energetically favored, contrary to what happens with aromatic lithium boron hydride rings.

Trapped Hydronium Radical Produced by Ultraviolet Excitation of Substituted Aromatic Molecule

The gas phase structure and excited state dynamics of o-aminophenol-H2O complex have been investigated using REMPI, IR-UV hole-burning spectroscopy, and pump-probe experiments with picoseconds laser pulses. The IR-UV spectroscopy indicates that the isomer responsible for the excitation spectrum corresponds to an orientation of the OH bond away from the NH2 group. The water molecule acts as H-bond acceptor of the OH group of the chromophore. The complexation of o-aminophenol with one water molecule induced an enhancement in the excited state lifetime on the band origin. The variation of the excited state lifetime of the complex with the excess energy from 1.4 ± 0.1 ns for the 0-0 band to 0.24 ± 0.3 ns for the band at 0-0 + 120 cm(-1) is very similar to the variation observed in the phenol-NH3 system. This experimental result suggests that the excited state hydrogen transfer reaction is the dominant channel for the non radiative pathway. Indeed, excited state ab initio calculations demonstrate that H transfer leading to the formation of the H3O(•) radical within the complex is the main reactive pathway.

How Anion Chaotrope Changes the Local Structure of Water: Insights from Photoelectron Spectroscopy and Theoretical Modeling of SCN(-) Water Clusters

The behavior of charged solute molecules in aqueous solutions is often classified using the concept of kosmotropes ("structure makers") and chaotropes ("structure breakers"). There is a growing consensus that the key to kosmotropic/chaotropic behaviors lies in the local solvent region, but the exact microscopic basis for such differentiation is not well-understood. This issue is examined in this work by analyzing size selective solvation of a well-known chaotrope, a negatively charged SCN(-) molecule. Combining experimental photoelectron spectroscopy measurements with theoretical modeling, we examine evolution of solvation structure up to eight waters. We observe that SCN(-) indeed fits the description of weakly hydrated ion, and its solvation is heavily driven by stabilization of water-water interaction network. However, the impact on water structure is more subtle than that associated with "structure breaker". In particular, we observe that the solvation structure of SCN(-) preserves the "packing" structure of the water network but changes local directionality of hydrogen bonds in the local solvent region. The resulting effect is closer to that of "structure weakener", where solute can be readily accommodated into the native water network, at the cost of compromising its stability due to constraints on hydrogen bonding directionality.

Structural Determination of (Al2O3)(n) (n = 1-15) Clusters Based on Graphic Processing Unit

Global optimization algorithms have been widely used in the field of chemistry to search the global minimum structures of molecular and atomic clusters, which is a nondeterministic polynomial problem with the increasing sizes of clusters. Considering that the computational ability of a graphic processing unit (GPU) is much better than that of a central processing unit (CPU), we developed a GPU-based genetic algorithm for structural prediction of clusters and achieved a high acceleration ratio compared to a CPU. On the one-dimensional (1D) operation of a GPU, taking (Al2O3)n clusters as test cases, the peak acceleration ratio in the GPU is about 220 times that in a CPU in single precision and the value is 103 for double precision in calculation of the analytical interatomic potential. The peak acceleration ratio is about 240 and 107 on the block operation, and it is about 77 and 35 on the 2D operation compared to a CPU in single precision and double precision, respectively. And the peak acceleration ratio of the whole genetic algorithm program is about 35 compared to CPU at double precision. Structures of (Al2O3)n clusters at n = 1-10 reported in previous works are successfully located, and their low-lying structures at n = 11-15 are predicted.

Li7(BH)5+: a new thermodynamically favored star-shaped molecule

The feasibility of stabilizing a new star-shaped molecule, the Li₇(BH)₅⁺, is demonstrated by anin silicoinvestigation, combining DFT andAb initiomethods.

Pool-BCGA: a parallelised generation-free genetic algorithm for the ab initio global optimisation of nanoalloy clusters

The Birmingham cluster genetic algorithm is a package that performs global optimisations for homo- and bimetallic clusters based on either first principles methods or empirical potentials. Here, we present a new parallel implementation of the code which employs a pool strategy in order to eliminate sequential steps and significantly improve performance. The new approach meets all requirements of an evolutionary algorithm and contains the main features of the previous implementation. The performance of the pool genetic algorithm is tested using the Gupta potential for the global optimisation of the Au10Pd10 cluster, which demonstrates the high efficiency of the method. The new implementation is also used for the global optimisation of the Au10 and Au20 clusters directly at the density functional theory level.

PtZnH5(-), A σ-Aromatic Cluster

We report a joint photoelectron spectroscopic and theoretical study of the PtZnH5(-) cluster anion. This cluster exhibited an unprecedented planar pentagonal coordination for Pt and an unusual stability and high intensity in the mass spectrum. Both are due to the σ-aromaticity found in the H5-cycle supported by the 5d orbitals on the Pt atom. σ-Aromaticity in all-H systems has been predicted in the past but never found in experimentally observed species. Besides fundamental importance, mixed transition-metal hydrides can be found as intermediates in catalytic processes, and thus, the unexpected stability facilitated by σ-aromaticity can be appreciated also in practical applications.

Hydrogen atom in water from ambient to high temperatures

The aqueous hydrogen atom is studied with molecular dynamics simulations from ambient temperature to near the critical point. The radial distribution functions find a hydrogen atom coordination number of about 13 water molecules at 300 K to about 4 water molecules at 646 K. The radial and angular distribution functions indicate that first-shell water molecules tend to orient to maximize hydrogen bonding interactions with other water molecules. These orientational tendencies diminish with temperature. The calculated diffusion coefficient agrees very well with experimental results known near ambient temperatures. It fits a simple activation model to about 575 K, above which the diffusion becomes much faster than predicted by the fit. To temperatures of at least 500 K there is evidence for caging on a time scale of about 1 ps, but the evidence disappears at very high temperatures. Values of the aqueous hydrogen hyperfine coupling constant are obtained by averaging the results of density functional calculations on clusters extracted from the simulations. The hyperfine coupling calculations do not agree well with experiment for reasons that are not understood now, pointing to the need for further research on this problem.

Selected AB4(2-/-) (A = C, Si, Ge; B = Al, Ga, In) ions: a battle between covalency and aromaticity, and prediction of square planar Si in SiIn4(2-/-)

CAl(4)(2-/-) (D(4h), (1)A(1g)) is a cluster ion that has been established to be planar, aromatic, and contain a tetracoordinate planar C atom. Valence isoelectronic substitution of C with Si and Ge in this cluster leads to a radical change of structure toward distorted pentagonal species. We find that this structural change goes together with the cluster acquiring partial covalency of bonding between Si/Ge and Al(4), facilitated by hybridization of the atomic orbitals (AOs). Counter intuitively, for the AAl(4)(2-/-) (A = C, Si, Ge) clusters, hybridization in the dopant atom is strengthened from C, to Si, and to Ge, even though typically AOs are more likely to hybridize if they are closer in energy (i.e. in earlier elements in the Periodic Table). The trend is explained by the better overlap of the hybrids of the heavier dopants with the orbitals of Al(4). From the thus understood trend, it is inferred that covalency in such clusters can be switched off, by varying the relative sizes of the AOs of the main element and the dopant. Using this mechanism, we then successfully killed covalency in Si, and predicted a new aromatic cluster ion containing a tetracoordinate square planar Si, SiIn(4)(2-/-).

Heavy periodane

The potential energy surface of the hypothetical NaMgAlSiPSCl system (heavy periodane) is exhaustively analyzed via the gradient embedded genetic algorithm (GEGA) in combination with density functional theory (DFT) computations. The electronegativity differences among the elements in both the second and third rows of the periodic table indicate that low-energy heavy periodane structures are obtained when highly electronegative and electropositive elements are bound together, but the global minimum of the heavy periodane system is completely different to its second-row analog (LiBeBCNOF).