NWPEsSe: An Adaptive-Learning Global Optimization Algorithm for Nanosized Cluster Systems

Electrochemical Imaging of Precisely-Defined Redox and Reactive Interfaces

Understanding the diverse electrochemical reactions occurring at electrode-electrolyte interfaces (EEIs) is a critical challenge to developing more efficient energy conversion and storage technologies. Establishing a predictive molecular-level understanding of solid electrolyte interphases (SEIs) is challenging due to the presence of multiple intertwined chemical and electrochemical processes occurring at battery electrodes. Similarly, chemical conversions in reactive electrochemical systems are often influenced by the heterogeneous distribution of active sites, surface defects, and catalyst particle sizes. In this mini review, we highlight an emerging field of interfacial science that isolates the impact of specific chemical species by preparing precisely-defined EEIs and visualizing the reactivity of their individual components using single-entity characterization techniques. We highlight the broad applicability and versatility of these methods, along with current state-of-the-art instrumentation and future opportunities for these approaches to address key scientific challenges related to batteries, chemical separations, and fuel cells. We establish that controlled preparation of well-defined electrodes combined with single entity characterization will be crucial to filling key knowledge gaps and advancing the theories used to describe and predict chemical and physical processes occurring at EEIs and accelerating new materials discovery for energy applications.

Evolution of the ionisation energy with the stepwise growth of chiral clusters of [4]helicene

Polycyclic aromatic hydrocarbons (PAHs) are widely established as ubiquitous in the interstellar medium (ISM), but considering their prevalence in harsh vacuum environments, the role of ionisation in the formation of PAH clusters is poorly understood, particularly if a chirality-dependent aggregation route is considered. Here we report on photoelectron spectroscopy experiments on [4]helicene clusters performed with a vacuum ultraviolet synchrotron beamline. Aggregates (up to the heptamer) of [4]helicene, the smallest PAH with helical chirality, were produced and investigated with a combined experimental and theoretical approach using several state-of-the-art quantum-chemical methodologies. The ionisation onsets are extracted for each cluster size from the mass-selected photoelectron spectra and compared with calculations of vertical ionisation energies. We explore the complex aggregation topologies emerging from the multitude of isomers formed through clustering of P and M, the two enantiomers of [4]helicene. The very satisfactory benchmarking between experimental ionisation onsets vs. predicted ionisation energies allows the identification of theoretically predicted potential aggregation motifs and corresponding energetic ordering of chiral clusters. Our structural models suggest that a homochiral aggregation route is energetically favoured over heterochiral arrangements with increasing cluster size, hinting at potential symmetry breaking in PAH cluster formation at the scale of small grains.

Electronic properties and collision cross sections of AgOkHm± (k, m = 1-4) aerosol ionic clusters

Experimental evidence shows that hydroxylated metal ions are often produced during cluster synthesis by atmospheric pressure spark ablation. In this work, we predict the ground state equilibrium structures of AgOkHm± clusters (k and m = 1-4), which are readily produced when spark ablating Ag, using the coupled cluster with singles and doubles (CCSD) method. The stabilization energy of these clusters is calculated with respect to the dissociation channel having the lowest energy, by accounting perturbative triples corrections to the CCSD method. The interatomic interactions in each of the systems have been investigated using the frontier molecular orbital (FMO), natural bond orbital (NBO) and quantum theory of atoms in molecules (QTAIM) methods. Many of the ground states of these ionic clusters are found to be stable, corroborating experimental observations. We find that clusters having singlet spin states are more stable in terms of dissociation than the clusters that have doublet or triplet spin states. Our calculations also indicate a strong affinity of the ionic and neutral Ag atom towards water and hydroxyl radicals or ions. Many 3-center, 4-electron (3c/4e) hyperbonds giving rise to more than one resonance structure are identified primarily for the anionic clusters. The QTAIM analysis shows that the O-H and O-Ag bonds in the clusters of both polarities are respectively covalent and ionic. The FMO analysis indicates that the anionic clusters are more reactive than the cationic ones. Using the cluster structures predicted by the CCSD method, we calculate the collision cross sections of the AgOkHm± family, with k and m ranging from 1 to 4, by the trajectory method. In turn, we predict the electrical mobilities of these clusters when suspended in helium at atmospheric pressure and compare them with experimental measurements.

Size effects and electronic properties of zinc-doped boron clusters Zn B n (n = 1-15)

CONTEXT

To gain a deeper understanding of zinc-doped boron clusters, theoretical calculations were performed to investigate the size effects and electronic properties of zinc-doped boron clusters. The study of the electronic properties, spectral characteristics, and geometric structures of Zn B n (n = 1-15) is of great significance in the fields of semiconductor materials science, material detection, and improving catalytic efficiency. The results indicate that Zn B n (n = 1-15) clusters predominantly exhibit planar or quasi-planar structures, with the Zn atom positioned in the outer regions of the B n framework. The second stable structure of Zn B 3 is a three-dimensional configuration, indicating that the structures of zinc-doped boron clusters begin to convert from the planar or quasi-planar structures to the 3D configurations. The second low-energy structure of Zn B 15 is a novel configuration. Relative stability analyses show that the Zn B 12 has better chemical stability than other clusters with a HOMO-LUMO gap of 2.79 eV. Electric charge analysis shows that part electrons on zinc atoms are transferred to boron atoms, and electrons prefer to cluster near the B n framework. According to the electron localization function, it gets harder to localize electrons as the equivalent face value drops, and it's challenging to see covalent bond formation between zinc and boron atoms. The spectrograms of Zn B n (n = 1-15) exhibit distinct properties and notable spectral features, which can be used as a theoretical basis for the identification and confirmation of boron clusters doped with single-atom transition metals.

METHODS

The calculations were performed using the ABCluster global search technique combined with density functional theory (DFT) methods. The selected low-energy structures were subjected to geometric optimization and frequency calculations at the PBE0/6-311 + G(d) level to ensure structural stability and eliminate any imaginary frequencies. To acquire more precise relative energies, we performed single-point energies calculations for the low-lying isomers of Zn B n (n = 1-15) at the CCSD(T)/6-311 + G(d)//PBE0/6-311 + G(d) level of theory. All calculations were performed using Gaussian 09 software. To facilitate analysis, we utilized software tools such as Multiwfn, and VMD.

Dynamic Copper Site Redispersion through Atom Trapping in Zeolite Defects

Single-site copper-based catalysts have shown remarkable activity and selectivity for a variety of reactions. However, deactivation by sintering in high-temperature reducing environments remains a challenge and often limits their use due to irreversible structural changes to the catalyst. Here, we report zeolite-based copper catalysts in which copper oxide agglomerates formed after reaction can be repeatedly redispersed back to single sites using an oxidative treatment in air at 550 °C. Under different environments, single-site copper in Cu-Zn-Y/deAlBeta undergoes dynamic changes in structure and oxidation state that can be tuned to promote the formation of key active sites while minimizing deactivation through Cu sintering. For example, single-site Cu2+ reduces to Cu1+ after catalyst pretreatment (270 °C, 101 kPa H2) and further to Cu0 nanoparticles under reaction conditions (270-350 °C, 7 kPa EtOH, 94 kPa H2) or accelerated aging (400-450 °C, 101 kPa H2). After regeneration at 550 °C in air, agglomerated CuO was dispersed back to single sites in the presence and absence of Zn and Y, which was verified by imaging, in situ spectroscopy, and catalytic rate measurements. Ab initio molecular dynamics simulations show that solvation of CuO monomers by water facilitates their transport through the zeolite pore, and condensation of the CuO monomer with a fully protonated silanol nest entraps copper and reforms the single-site structure. The capability of silanol nests to trap and stabilize copper single sites under oxidizing conditions could extend the use of single-site copper catalysts to a wider variety of reactions and allows for a simple regeneration strategy for copper single-site catalysts.

Theoretical study on structural evolution, photoelectron and vibrational spectra, and thermochemistry properties of neutral, anionic and di-anionic titanium-doped tin (TiSnn0/-/2- (n = 4-17)) nanoalloy clusters

The structural evolution, chemical stability, electronic and vibrational properties, as well as charge transfer and bonding character of TiSnn0/-/2- (n = 4-17) clusters have been performed with density functional theory calculations using ABCluster search technique. Structurally, it is found that the growth patterns prefer three kinds of absorbed stages from polygonal bipyramidal configuration for n = 4-6, to absorbing additional Sn on the adjacent surfaces of pentagonal bipyramid unit from n = 7-12, and finally to the TiSn130/-/2- cluster as the first foundational architectures, of which the encapsulated cage structure is formed when n = 11. The simulated PES spectra agree with available experiments. More interestingly, the neutral TiSn16 cluster not only possesses the high thermodynamic and relative stability but also preferable photochemical reactivity, that can be further explained by superatom features and delocalized multi-center bonds (AdNDP), while the strong p-d hybridization between Ti atom and Sn unit plays an important role in the stabilities of clusters, making it as the most suitable building units. In addition, the UV-Vis absorption spectra of TiSn16 are discussed, and the main transitions of crucial excited states are analyzed in detail. The Infrared and Raman vibrational characteristic peaks of all these neutral and charged species are properly assigned, of which the TiSnn0/-/2- (n = 10-17) clusters possess degenerating deformation mode of Ti atom wagging in Sn cage framework (Infrared active) and breathing mode of Sn cage framework (Raman active). All these findings will provide a further understanding for the nanoalloy cluster as the most suitable building block with further development as a potential optoelectronic material.

Structural Growth Pattern, Electronic Configurations, and Spectral and Thermochemistry Properties of ZrSnn0/-/2- (n = 4-17) Nanoscale Compounds: A Systematic Study Using Density Functional Theory

By performing density functional theory (DFT) calculations for geometric optimization in conjunction with the artificial bee colony algorithm for cluster (ABCluster) global search approach, the ground-state structures of the neutral, anionic, and dianionic ZrSnn0/-/2- (n = 4-17) nanoscale compounds are obtained. Their structural growth evolution, spectral information, and electronic and thermochemical properties are investigated. Regarding the architectural evolution of the neutral, anion, and dianionic species, ZrSnn0/-/2- (n = 4-17) compounds possess two different stages of adsorption patterns in which, when n = 4-7 and n = 8-17, ZrSn40/-/2- and ZrSn80/-/2- compounds as the basic motif adsorb Sn atoms to become the larger clusters, respectively. The simulated photoelectron spectra (PES) of anionic compounds are in good agreement with the available experimental PES. The infrared and Raman spectra can be summarized as follows: under infrared vibrational modes, the sealed cages of ZrSnn0/-/2- compounds belong to the deformation mode, and under Raman vibrational modes, they belong to the breathing mode of the Sn cage framework. The density of states (DOS) spectra and natural population analysis (NPA) indicate that the interaction between the Zr atom and Snn frameworks of capsulated compounds has been developing stronger than for unsealed compounds. The results of thermochemical properties, molecular orbital shell (MOs) analysis, and ultraviolet-visible (UV-vis) absorption spectrum indicate that the neutral ZrSn16 nanoscale compound possesses not only both thermodynamic and chemical stability but also far-infrared sensing and optoelectronic properties and hence, is the best building block motif for new multipurpose nanoscale materials.

Monte Carlo-Simulated Annealing and Machine Learning-Based Funneled Approach for Finding the Global Minimum Structure of Molecular Clusters

Understanding the physical underpinnings and geometry of molecular clusters is of great importance in many fields, ranging from studying the beginning of the universe to the formation of atmospheric particles. To this end, several approaches have been suggested, yet identifying the most stable cluster geometry (i.e., global potential energy minimum) remains a challenge, especially for highly symmetric clusters. Here, we suggest a new funneled Monte Carlo-based simulated annealing (SA) approach, which includes two key steps: generation of symmetrical clusters and classification of the clusters according to their geometry using machine learning (MCSA-ML). We demonstrate the merits of the MCSA-ML method in comparison to other approaches on several Lennard-Jones (LJ) clusters and four molecular clusters-Ser8(Cl-)2, H+(H2O)6, Ag+(CO2)8, and Bet4Cl-. For the latter of these clusters, the correct structure is unknown, and hence, we compare the experimental and simulated fragmentation patterns, and the fragmentation of the proposed global minimum matches experiments closely. Additionally, based on the fragmentation of the predicted betaine cluster, we were able to identify hitherto unknown neutral fragmentation channels. In comparison to results obtained with other methods, we demonstrated a superior ability of MCSA-ML to predict clusters with high symmetry and similar abilities to predict clusters with asymmetrical structures.

Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning

Computational modeling of atmospheric molecular clusters requires a comprehensive understanding of their complex configurational spaces, interaction patterns, stabilities against fragmentation, and even dynamic behaviors. To address these needs, we introduce the Jammy Key framework, a collection of automated scripts that facilitate and streamline molecular cluster modeling workflows. Jammy Key handles file manipulations between varieties of integrated third-party programs. The framework is divided into three main functionalities: (1) Jammy Key for configurational sampling (JKCS) to perform systematic configurational sampling of molecular clusters, (2) Jammy Key for quantum chemistry (JKQC) to analyze commonly used quantum chemistry output files and facilitate database construction, handling, and analysis, and (3) Jammy Key for machine learning (JKML) to manage machine learning methods in optimizing molecular cluster modeling. This automation and machine learning utilization significantly reduces manual labor, greatly speeds up the search for molecular cluster configurations, and thus increases the number of systems that can be studied. Following the example of the Atmospheric Cluster Database (ACDB) of Elm (ACS Omega, 4, 10965-10984, 2019), the molecular clusters modeled in our group using the Jammy Key framework have been stored in an improved online GitHub repository named ACDB 2.0. In this work, we present the Jammy Key package alongside its assorted applications, which underline its versatility. Using several illustrative examples, we discuss how to choose appropriate combinations of methodologies for treating particular cluster types, including reactive, multicomponent, charged, or radical clusters, as well as clusters containing flexible or multiconformer monomers or heavy atoms. Finally, we present a detailed example of using the tools for atmospheric acid-base clusters.

The application of machine learning to air pollution research: A bibliometric analysis

Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.

Computational description of surface hydride phases on Pt(111) electrodes

Surface platinum hydride structures may exist and play a potentially important role during electrocatalysis and cathodic corrosion of Pt(111). Earlier work on platinum hydrides suggests that Pt may form clusters with multiple equivalents of hydrogen. Here, using thermodynamic methods and density functional theory, we compared several surface hydride structures on Pt(111). The structures contain multiple monolayers of hydrogen in or near the surface Pt layer. The hydrogen in these structures may bind the subsurface or reconstruct the surface both in the set of initial configurations and in the resulting (meta)stable structures. Multilayer stable configurations share one monolayer of subsurface H stacking between the top two Pt layers. The structure containing two monolayers (MLs) of H is formed at -0.29 V vs normal hydrogen electrode, is locally stable with respect to configurations with similar H densities, and binds H neutrally. Structures with 3 and 4 ML H form at -0.36 and -0.44 V, respectively, which correspond reasonably well to the experimental onset potential of cathodic corrosion on Pt(111). For the 3 ML configuration, the top Pt layer is reconstructed by interstitial H atoms to form a well-ordered structure with Pt atoms surrounded by four, five, or six H atoms in roughly square-planar and octahedral coordination patterns. Our work provides insight into the operando surface state during low-potential reduction reactions on Pt(111) and shows a plausible precursor for cathodic corrosion.

Growth Pattern of Large Morse Clusters with Medium-Range Potentials

Due to the extremely complex potential energy surfaces of large Morse clusters with medium-range potentials (i.e., ρ = 6 and 10), global optimization studies in the literature are limited to a cluster size (N) of ≤240. Starting from completely random structures, we successfully systematically studied Morse clusters with up to 700 atoms using our unbiased fuzzy global optimization (FGO) method. While all of the putative global minima reported previously have been efficiently obtained, new global minima with lower energies are identified for N values of 176, 258, 485, 561, 817, and 923 with ρ = 6 and for N values of 151, 202, 226, and 229 with ρ = 10. A detailed growth pattern and magic clusters are obtained. For the first time, we find that a central vacancy is present in Morse clusters containing 542, 543, 548, and 922 atoms with ρ = 6. FGO has achieved high performance in large clusters with different interatomic interaction ranges, thus showing great application potential in the global structure optimization of general clusters.

Theoretical Insights into the Geometrical Evolution, Photoelectron Spectra, and Vibrational Properties of YGe n - (n = 6-20) Anions: From Y-Linked to Y-Encapsulated Structures

The structural evolution behavior of germanium anionic clusters doped with the rare-earth metal yttrium, YGe _n ^- (n = 6-20), has been investigated using a mPW2PLYP density functional scheme and an ABCluster structure searching technique. The results reveal that with increasing cluster size n, the structure evolution pattern is from the Y-linked framework (n = 10-14), where Y serves as a linker (the Y atom bridges two germanium subclusters), to the Y-encapsulated framework (n = 15-20), where the Y atom is located in the center of the Ge cage. The simulated PES spectra show satisfying agreement with the experimental PES spectra for n = 12-20, which reveals that the global minimum structures reported here are reliable. In particular, the anionic YGe₁₆ ^- nanocluster is found to be the most stable structure in the size range of n = 6-20 through analyzes of the relative stability, highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital (LUMO) gap, spherical jellium model, and isochemical shielding surface. Moreover, spectral properties such as infrared and Raman spectra were reported. In addition, the UV-vis spectra of the YGe₁₆ ^- nanocluster are in good agreement with solar energy distribution, showing that such substances serve as multifunctional building blocks to be potentially used in optoelectronic devices or solar energy converters.

Electronic Structure, Stability, and Electrical Mobility of Cationic Silver Oxide Atomic Clusters

Silver oxide cluster cations (Ag_nO_m⁺) can readily be produced by a number of methods including atmospheric-pressure spark ablation of pure silver electrodes when trace amounts of oxygen are present in the carrier gas. Here we determine the equilibrium geometries of Ag_nO_m⁺ clusters (n = 1-4; m = 1-5) using accurate coupled cluster with singles and doubles (CCSD) method, while the stabilization energies are calculated with additional perturbative triples correction (CCSD(T)). Although a number of stable states have been identified, our results show that the Ag_nO_m⁺ clusters with m = 1 are more stable than those with m ≥ 2 due to the absence of the terminally attached O₂ molecule, corroborating recent observations by mass spectrometry. Using the computed structures, we calculate the electrical mobilities of the Ag_nO_m⁺ clusters and label the values on a respective experimentally determined spectrum in an attempt to better interpret the occurrence of the peaks and troughs in the measurements.

Structural evolution, photoelectron spectra and vibrational properties of anionic GdGe n - (n = 5-18) nanoalloy clusters: a DFT insight

The structural growth of Gd-doped germanium anionic nanoclusters, GdGe_n⁻ (n = 5–18), has been explored via quantum chemistry calculations using the mPW2PLYP method and an unprejudiced structural searching technique known as ABCluster. The optimized geometries exhibited that when n = 10–14, the structural evolution favors the Gd-linked configuration where the Gd atom as a connector bridges two Ge subgroups, while the Gd atom is encapsulated in a closed cage-like Ge frame when n = 15–18. The properties like magnetic moment, charge transfer, relative stability, HOMO–LUMO gap, photoelectron spectra, and infrared and Raman spectra have been predicted. The information of these spectra could provide extra approaches to experimentally determine the electronic structures and equilibrium configuration of these compounds. The largest spin magnetic moment of 7 μ_B is attained via half-filled 4f states. The GdGe₁₆⁻ nanocluster is determined to be a superatom because its total valence of 75 electrons can be distributed to the orbital sequence of 1S²1P⁶(4f⁷)1D¹⁰1F¹⁴2S²2P²1G¹⁸2P⁴2D¹⁰, which complies with not only Hund's rule, but also the spherical jellium model. Particularly, its UV-Vis spectra match well with solar energy distribution. Such materials act as nano multifunctional building units potentially used in solar energy converters or ultra-highly sensitive near-infrared photodetectors.

The structural evolution, electronic and vibrational properties of GdGe_n⁻ (n=5-18) nanoclusters were studied by quantum chemical calculations, which revealed GdGe₁₆⁻ nanocluster is a superatom to the orbital of 1S²1P⁶(4f⁷)1D¹⁰1F¹⁴2S²2P²1G¹⁸2P⁴2D¹⁰.

Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixtures containing 75 unique compounds. The presence of strong hydrogen bonding molecules may lead to clustering or dimerization resulting in non-linear diffusive behavior. To address this, self- and binary association energies were calculated for each molecule and mixture to provide information on intermolecular interaction strength and were used as input features to the ANN. An accurate, generalized ANN model was developed with an overall average absolute deviation of 4.1%. Forward input feature selection reveals the importance of critical properties and self-association energies along with other fluid properties. Additional ANNs were developed with subsets of the full input feature set to further investigate the impact of various properties on model performance. The results from two specific mixtures are discussed in additional detail: one providing an example of strong hydrogen bonding and the other an example of extreme pressure changes, with the ANN models predicting self-diffusion well in both cases.

Improving the Accuracy of Atomistic Simulations of the Electrochemical Interface

Atomistic simulation of the electrochemical double layer is an ambitious undertaking, requiring quantum mechanical description of electrons, phase space sampling of liquid electrolytes, and equilibration of electrolytes over nanosecond time scales. All models of electrochemistry make different trade-offs in the approximation of electrons and atomic configurations, from the extremes of classical molecular dynamics of a complete interface with point-charge atoms to correlated electronic structure methods of a single electrode configuration with no dynamics or electrolyte. Here, we review the spectrum of simulation techniques suitable for electrochemistry, focusing on the key approximations and accuracy considerations for each technique. We discuss promising approaches, such as enhanced sampling techniques for atomic configurations and computationally efficient beyond density functional theory (DFT) electronic methods, that will push electrochemical simulations beyond the present frontier.

Tuning the Charge and Hydrophobicity of Graphene Oxide Membranes by Functionalization with Ionic Liquids at Epoxide Sites

Functionalization of graphene oxide (GO) membranes is generally achieved using carboxyl groups as binding sites for ligands. Herein, by taking advantage of the ability of imidazolium-based ionic liquids (ILs) to undergo an epoxide ring-opening reaction, a new approach of GO modification was established, in which ILs were bonded to the abundant epoxides on GO without sacrificing the carboxyl groups. Computational methods confirmed this unique configuration of ILs on GO, which enabled the dispersion of IL/GO flakes in water for facile casting into laminate membranes. Compared with neat GO, the ILs in IL/GO membranes served as spacers that substantially reduced the multi-valent cation mobility, simultaneously facilitated ion desolvation, and increased the water flux across the membrane. Our studies found that the higher separation efficiency of IL/GO membranes may be attributed to the synergistic modification of the hydrophobicity and surface charge. Specifically, the protonated nitrogen of the imidazolium cations altered the surface charge of GO, thereby generating electrostatic repulsion that enhanced the selectivity of cation rejection. On the other hand, the increased length of the alkyl chains bound to the imidazolium rings was found to increase the hydrophobicity of GO, which, in turn, aided the fine-tuning of the water desolvation/transport dynamics at the GO/IL interface to achieve a high water flux. Additionally, the water retention was reduced on the hydrophobic planes, which inhibited GO swelling during aqueous separations. Molecular dynamics simulations revealed increased water diffusivity when ILs were intercalated within GO layers. We establish that without requiring a high energy input, functionalization of GO membranes with ILs may be a promising approach to achieve efficient ion separation and critical material recovery.

Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility

Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~10⁸ potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally.

The discovery of heterogeneous catalysts for large molecule conversion has been lagging due to the combinatorial inventory of intermediates. Here, the author presents an automated framework to explore the chemical space of reaction intermediates.

High-Performance of Electrocatalytic CO2 Reduction on Defective Graphene-Supported Cu4S2 Cluster

Electrochemical CO2 reduction reaction (CO2RR) to high-value chemicals is one of the most splendid approaches to mitigating environmental threats and energy shortage. In this study, the catalytic performance of CO2RR on defective graphene-supported Cu4S2 clusters as well as isolated Cu4Xn (X = O, S, Se; n = 2, 4) was systematically investigated based on density functional theory (DFT) computations. Calculation results revealed that the most thermodynamically feasible product is CH3OH among the C1 products on Cu4X2 clusters, in which the Cu4S2 cluster has the best activity concerning CH3OH synthesis with a limiting potential of −0.48 V. When the Cu4S2 cluster was further supported on defective graphene, the strong interaction between cluster and substrate could greatly improve the performance via tuning the electronic structure and improving the stability of the Cu4S2 cluster. The calculated free energy diagram indicated that it is also more energetically preferable for CH3OH production with a low limiting potential of −0.35 V. Besides, the defective graphene support has a significant ability to suppress the competing reactions, such as the hydrogen evolution reaction (HER) and CO and HCOOH production. Geometric structures, limiting potentials, and reduction pathways were also discussed to gain insight into the reaction mechanism and to find the minimum-energy pathway for C1 products. We hope this work will provide theoretical reference for designing and developing advanced supported Cu-based electrocatalysts for CO2 reduction.

Potential Application of Machine-Learning-Based Quantum Chemical Methods in Environmental Chemistry

It is an important topic in environmental sciences to understand the behavior and toxicology of chemical pollutants. Quantum chemical methodologies have served as useful tools for probing behavior and toxicology of chemical pollutants in recent decades. In recent years, machine learning (ML) techniques have brought revolutionary developments to the field of quantum chemistry, which may be beneficial for investigating environmental behavior and toxicology of chemical pollutants. However, the ML-based quantum chemical methods (ML-QCMs) have only scarcely been used in environmental chemical studies so far. To promote applications of the promising methods, this Perspective summarizes recent progress in the ML-QCMs and focuses on their potential applications in environmental chemical studies that could hardly be achieved by the conventional quantum chemical methods. Potential applications and challenges of the ML-QCMs in predicting degradation networks of chemical pollutants, searching global minima for atmospheric nanoclusters, discovering heterogeneous or photochemical transformation pathways of pollutants, as well as predicting environmentally relevant end points with wave functions as descriptors are introduced and discussed.

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.

Protonation of Serine in Gas and Condensed and Microsolvated States in Aqueous Solution

Identification of molecules and elucidation of their chemical structure are ubiquitous problems in chemistry. Mass spectrometry (MS) can be used due to its sensitivity and versatility. For detection to occur, analytes must be ionized and transferred to the gas phase. Soft ionization processes such as electrospray ionization are popular; however, resulting microsolvated phases can alter the chemistry of analytes and therefore detection and identification. To understand these processes, we use computational methods to probe the ionization propensity of serine in the gas phase, aqueous microsolvated clusters, and aqueous solution. We show that the tautomeric form of serine is altered by the presence of water, as five water molecules can stabilize the zwitterionic tautomer. Inclusion of cosolutes such as ions can stabilize the zwitterion with as few as one or two water molecules present. We demonstrate that ionization propensity, as measured by gas phase bacisity, can increase by over 100 kJ/mol when placed in a small water-serine cluster, showing the sensitivity of the chemistry of microsolvated analytes. Finally, detailed analysis reveals that small droplets (less than seven water molecules) are extremely sensitive to addition of further water molecules. Beyond this limit, structural and electronic properties change little with droplet size.

Structural Evolution, Electronic Structures, and Vibrational Properties of Anionic LuGen (n = 5-17) Clusters: From Lu-Linked to Lu-Encapsulated Configurations

The structural evolution pattern and electronic properties of Lu-doped germanium anion clusters, LuGe_n^- (n = 5-17), have been investigated using a global search method combined with a double hybrid density functional theory and by comparing the theoretical PES spectra with the experimental ones. It is found that, for the structural growth patterns, a Lu-linked configuration is preferred for n = 10-14 in which the Lu atom as a linker connects two Ge subclusters and a Lu-encapsulated Ge cage-like motif is preferred for n = 15-17. The simulated PES spectra agree with experimental ones, revealing that the current global minimum structures are the true minima. The properties such as relative stability, charge transfer, highest-energy occupied molecular orbital-lowest-energy unoccupied molecular orbital (HOMO-LUMO) gap, IR, Raman, and ultraviolet-visible (UV-vis) spectra have been evaluated. The results of IR and Raman spectra could provide additional ways to experimentally identify the structure of these clusters. The results of stability, HOMO-LUMO gap, and UV-vis spectra could make the LuGe₁₆^- cluster the most suitable building block for further development as a potential optoelectronic material.

Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials

Smoothness/defectiveness of the carbon material surface is a key issue for many applications, spanning from electronics to reinforced materials, adsorbents and catalysis. Several surface defects cannot be observed with conventional analytic techniques, thus requiring the development of a new imaging approach. Here, we evaluate a convenient method for mapping such "hidden" defects on the surface of carbon materials using 1-5 nm metal nanoparticles as markers. A direct relationship between the presence of defects and the ordering of nanoparticles was studied experimentally and modeled using quantum chemistry calculations and Monte Carlo simulations. An automated pipeline for analyzing microscopic images is described: the degree of smoothness of experimental images was determined by a classification neural network, and then the images were searched for specific types of defects using a segmentation neural network. An informative set of features was generated from both networks: high-dimensional embeddings of image patches and statics of defect distribution.

Ligand control of low-frequency electron paramagnetic resonance linewidth in Cr(III) complexes

Understanding how the ligand shell controls low-frequency electron paramagnetic resonance (EPR) spectroscopic properties of metal ions is essential if they are to be used in EPR-based bioimaging schemes. In this work, we probe how specific variations in the ligand structure impact L-band (ca. 1.3 GHz) EPR spectroscopic linewidths in the trichloride salts of five Cr(iii) complexes: [Cr(RR-dphen)3]3+ (RR-dphen = (1R,2R)-(+)-diphenylethylenediamine, 1), [Cr(en)3]3+ (en = ethylenediamine, 2), [Cr(me-en)3]3+ (me-en = 1,2-diaminopropane, 3), [Cr(tn)3]3+ (tn = 1,3-diaminopropane, 4) [Cr(trans-chxn)3]3+ (trans-chxn = trans-(±)-1,2-diaminocyclohexane, 5). Spectral broadening varies in a nonintuitive manner across the series, showing the sharpest peaks for 1 and broadest for 5. Molecular dynamics simulations provide evidence that the broadening is correlated to rigidity in the inner coordination sphere and reflected in ligand-dependent distribution of Cr-N bond distances that can be found in frozen solution.

Thermochemical Properties and Growth Mechanism of the Ag-Doped Germanium Clusters, AgGe n λ with n = 1-13 and λ = -1, 0, and +1

A systematic investigation of the silver-doped germanium clusters AgGe n with n = 1-13 in the neutral, anionic, and cationic states is performed using the unbiased global search technique combined with a double-density functional scheme. The lowest-energy minima of the clusters are identified based on calculated energies and measured photoelectron spectra (PES). Total atomization energies and thermochemical properties such as electron affinity (EA), ionization potential (IP), binding energy, hardness, and highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap are obtained and compared with those of pure germanium clusters. For neutral and anionic clusters, although the most stable structures are inconsistent when n = 7-10, their structure patterns have an exohedral structure except for n = 12, which is a highly symmetrical endohedral configuration. For the cationic state, the most stable structures are attaching structures (in which an Ag atom is adsorbed on the Ge n cluster or a Ge atom is adsorbed on the AgGe n-1 cluster) at n = 1-12, and when n = 13, the cage configuration is formed. The analyses of binding energy indicate that doping of an Ag atom into the neutral and charged Ge n clusters decreases their stability. The theoretical EAs of AgGe n clusters agree with the experimental values. The IP of neutral Ge n clusters is decreasing when doped with an Ag atom. The chemical activity of AgGe n is analyzed through HOMO-LUMO gaps and hardness, and the variant trend of both versus cluster size is slightly different. The accuracy of the theoretical analyses in this paper is demonstrated successfully by the agreement between simulated and experimental results such as PES, IP, EA, and binding energy.

Application of Optimization Algorithms in Clusters

The structural characterization of clusters or nanoparticles is essential to rationalize their size and composition-dependent properties. As experiments alone could not provide complete picture of cluster structures, so independent theoretical investigations are needed to find out a detail description of the geometric arrangement and corresponding properties of the clusters. The potential energy surfaces (PES) are explored to find several minima with an ultimate goal of locating the global minima (GM) for the clusters. Optimization algorithms, such as genetic algorithm (GA), basin hopping method and its variants, self-consistent basin-to-deformed-basin mapping, heuristic algorithm combined with the surface and interior operators (HA-SIO), fast annealing evolutionary algorithm (FAEA), random tunneling algorithm (RTA), and dynamic lattice searching (DLS) have been developed to solve the geometrical isomers in pure elemental clusters. Various model or empirical potentials (EPs) as Lennard-Jones (LJ), Born-Mayer, Gupta, Sutton-Chen, and Murrell-Mottram potentials are used to describe the bonding in different type of clusters. Due to existence of a large number of homotops in nanoalloys, genetic algorithm, basin-hopping algorithm, modified adaptive immune optimization algorithm (AIOA), evolutionary algorithm (EA), kick method and Knowledge Led Master Code (KLMC) are also used. In this review the optimization algorithms, computational techniques and accuracy of results obtained by using these mechanisms for different types of clusters will be discussed.

Study on the growth behavior and photoelectron spectroscopy of neodymium-doped silicon nanoclusters NdSin0/- (n = 8-20) with a double-hybrid density functional theory

Structural evolution, magnetic moment, and thermochemical and spectral properties of NdSi_n^0/- (n = 8-20) nanoclusters were studied. Optimized structures for NdSi_n demonstrated that the configuration with quintet ground state prefers Nd-substituted for a Si of the most stable Si_n + 1 (n = 8-11) structure to Nd-linked configuration with Si₉ tricapped trigonal prism subcluster (n = 12-19). Finally, the configuration prefers to Nd-encapsulated into Si cage framework (n = 20). For anion, the evolution at the quartet state prefers Nd-linked structure for n = 8-19 (excluded 9), and prefers Nd-encapsulated structure of n = 20. The spectral information including electron affinity, vertical detachment energy, and simulated photoelectron spectroscopy were also observed. The 4f electrons of Nd atom in NdSi_n with n = 8-10 hardly participate in bonding, but take part in remaining neutral clusters and all anionic NdSi_n^- clusters. The calculations of average bond energy, HOMO-LUMO gap, and chemical bonding analyses reveal that NdSi₂₀^- possesses perfect thermodynamic and ideal chemical stability, making it as the most appropriate constitutional units for novel multi-functional semiconductors.

Consecutive methane activation mediated by single metal boride cluster anions NbB4. Phys Chem Chem Phys 2021;23:12592-12599. [PMID: 34047332 DOI: 10.1039/d1cp01418h]

Cleavage of all C-H bonds in two methane molecules by gas-phase cluster ions at room temperature is a challenging task. Herein, mass spectrometry and quantum chemical calculations have been used to identify one single metal boride cluster anions NbB4- that can activate eight C-H bonds in two methane molecules and release one H2 molecule each time under thermal collision conditions. In these consecutive reactions, the loaded Nb atoms and the support B4 units play different roles but act synergistically to activate CH4, which is responsible for the interesting reactivity of NbB4-. Moreover, there are some mechanistic differences in these two reactions, including the mechanisms for the first C-H bond activation steps, dihydrogen desorption sites, and major electron donors. This study shows that non-noble metal boride species are reactive enough to facilitate thermal C-H bond cleavages, and boron-based materials may be one kind of potential support material facilitating surface hydrogen transport.

Structure and Stability of the Ionic Liquid Clusters [EMIM]n[BF4]n+1- (n = 1-9): Implications for Electrochemical Separations

Precise functionalization of electrodes with size-selected ionic liquid (IL) clusters may improve the application of ILs in electrochemical separations. Herein we report our combined experimental and theoretical investigation of the IL clusters 1-ethyl-3-methylimidazolium tetrafluoroborate [EMIM]_n[BF₄]_n+1^- (n = 1-9) and demonstrate their selectivity and efficiency toward targeted adsorption of ions from solution. The structures and energies of the IL clusters, predicted with global optimization, agree with and help interpret the ion abundances and stabilities measured by high-mass-resolution electrospray ionization mass spectrometry and collision-induced dissociation experiments. The [EMIM][BF₄]₂^- cluster, which was identified as the most stable IL cluster, was selectively soft-landed onto a working electrode. Electrochemical impedance spectroscopy revealed a lower charge transfer resistance on the soft-landed electrode containing [EMIM][BF₄]₂^- compared with an electrode prepared by drop-casting of an IL solution containing the full range of IL clusters. Our findings indicate that specific IL clusters may be used to increase the efficiency of electrochemical separations by lowering the overpotentials involved.