OGOLEM: Global cluster structure optimisation for arbitrary mixtures of flexible molecules. A multiscaling, object-oriented approach

Direct Evidence of Reversible Changes in Electrolyte and its Interplay with LiO2 Intermediate in Li-O2 Batteries

Lithium-oxygen batteries show promising energy storage potential with high theoretical energy density; however, further investigation of chemical reactions is required. In this study, experimental Raman and theoretical analyzes are performed for a Li-O2 battery with LiClO4/dimethyl sulfoxide (DMSO) electrolyte and carbon cathode to understand the role of intermediate species in the reactional mechanism of the cell using a high donor number solvent. Operando Raman results reveal reversible changes in the DMSO bands, in addition to the formation and decomposition of Li2O2. On discharge, a decrease in DMSO polarizability is observed and bands of DMSO-Li+-anion interactions are evidenced and supported by ab initio density functional theory (DFT) calculations. Molecular dynamics (MD) force field simulations and operando Raman show that DMSO interacts with LiO2(sol), highlighting the stability of the electrolyte compared to the interaction with reactiveO 2 - ${\rm O}_2^{-}$ . On charging, the presence of Li+ indicates the formation of a lithium-deficient phase, followed by the release of Li+ and oxygen. Therefore, this study contributes to understanding the discharge/charge chemistry of a Li-O2 cell, employing a common carbon cathode and DMSO electrolyte. The combination of a simple characterization technique in operando mode and theoretical studies provides essential information on the mechanism of Li-O2 system.

Reparameterization of GFN1-xTB for atmospheric molecular clusters: applications to multi-acid-multi-base systems

Atmospheric molecular clusters, the onset of secondary aerosol formation, are a major part of the current uncertainty in modern climate models. Quantum chemical (QC) methods are usually employed in a funneling approach to identify the lowest free energy cluster structures. However, the funneling approach highly depends on the accuracy of low-cost methods to ensure that important low-lying minima are not missed. Here we present a reparameterized GFN1-xTB model based on the clusteromics I-V datasets for studying atmospheric molecular clusters (AMC), denoted AMC-xTB. The AMC-xTB model reduces the mean of electronic binding energy errors from 7-11.8 kcal mol-1 to roughly 0 kcal mol-1 and the root mean square deviation from 7.6-12.3 kcal mol-1 to 0.81-1.45 kcal mol-1. In addition, the minimum structures obtained with AMC-xTB are closer to the ωB97X-D/6-31++G(d,p) level of theory compared to GFN1-xTB. We employ the new parameterization in two new configurational sampling workflows that include an additional meta-dynamics sampling step using CREST with the AMC-xTB model. The first workflow, denoted the "independent workflow", is a commonly used funneling approach with an additional CREST step, and the second, the "improvement workflow", is where the best configuration currently known in the literature is improved with a CREST + AMC-xTB step. Testing the new workflow we find configurations lower in free energy for all the literature clusters with the largest improvement being up to 21 kcal mol-1. Lastly, by employing the improvement workflow we massively screened 288 new multi-acid-multi-base clusters containing up to 8 different species. For these new multi-acid-multi-base cluster systems we observe that the improvement workflow finds configurations lower in free energy for 245 out of 288 (85.1%) cluster structures. Most of the improvements are within 2 kcal mol-1, but we see improvements up to 8.3 kcal mol-1. Hence, we can recommend this new workflow based on the AMC-xTB model for future studies on atmospheric molecular clusters.

Stability and Structure of Potentially Atmospherically Relevant Glycine Ammonium Bisulfate Clusters

New particle formation (NPF) is the process by which trace atmospheric acids and bases cluster and grow into particles that ultimately impact climate. Sulfuric acid concentration drives NPF, but nitrogen-containing bases promote the formation of more stable clusters via salt bridge formation. Recent computational efforts have suggested that amino acids can enhance NPF, predicting that they can stabilize new particles via multiple protonation sites, but there has yet to be experimental validation of these predictions. We used mass spectrometry and infrared spectroscopy to study the structure and stability of cationic clusters composed of glycine, sulfuric acid, and ammonia. When collisionally activated, clusters were significantly more likely to eliminate ammonia or sulfuric acid than glycine, while quantum chemical calculations predicted lower binding free energies for ammonia but similar binding free energies for glycine and sulfuric acid. These calculations predicted several low-energy structures, so we compared experimental and computed vibrational spectra to attempt to validate the computationally predicted minimum energy structure. Unambiguous identification of the experimental structure by comparison to these calculations was made difficult by the complexity of the experimental spectra and the fact that the identity of the computed lowest-energy structure depended strongly on temperature. If their vapors are present, amino acids are likely to be enriched in new particles by displacing more weakly bound ammonia, similar to the behavior of other atmospheric amines. The carboxylic acid groups were found to preferentially interact with other carboxylic acids, suggesting incipient organic/inorganic phase separation even at these small sizes.

On the brink of self-hydration: the water heptadecamer

For pure, neutral, isolated molecular clusters, (H2O)17 marks the transition from structures with all water molecules on the cluster surface to water self-hydration, i.e., cluster structures around one central water molecule. Getting this right with water model potentials turns out to be challenging. Even the best water potentials currently available, which reproduce collective properties very well, still deliver contradicting results for (H2O)17, when different low-energy isomers from global structure optimizations are examined. Interestingly, ab initio quantum chemistry also struggles with the only seemingly simple question if (H2O)17 is all-surface or water-centered. Hence, although the long history of water potential development may be entering its final phase, it is not quite finished yet.

Quantum Cluster Equilibrium Theory for Multicomponent Liquids

In this work, we present a new theory to treat multicomponent liquids based on quantum-chemically calculated clusters. The starting point is the binary quantum cluster equilibrium theory, which is able to treat binary systems. The theory provides one equation with two unknowns. In order to obtain another linearly independent equation, the conservation of mass is used. However, increasing the number of components leads to more unknowns, and this requires linearly independent equations. We address this challenge by introducing a generalization of the conservation of arbitrary quantities accompanied by a comprehensive mathematical proof. Furthermore, a case study for the application of the new theory to ternary mixtures of chloroform, methanol, and water is presented. Calculated enthalpies of vaporization for the whole composition range are given, and the populations or weights of the different clusters are visualized.

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.

Current and future machine learning approaches for modeling atmospheric cluster formation

The formation of strongly bound atmospheric molecular clusters is the first step towards forming new aerosol particles. Recent advances in the application of machine learning models open an enormous opportunity for complementing expensive quantum chemical calculations with efficient machine learning predictions. In this Perspective, we present how data-driven approaches can be applied to accelerate cluster configurational sampling, thereby greatly increasing the number of chemically relevant systems that can be covered.

The driving effects of common atmospheric molecules for formation of prenucleation clusters: the case of sulfuric acid, formic acid, nitric acid, ammonia, and dimethyl amine

How secondary aerosols form is critical as aerosols' impact on Earth's climate is one of the main sources of uncertainty for understanding global warming. The beginning stages for formation of prenucleation complexes, that lead to larger aerosols, are difficult to decipher experimentally. We present a computational chemistry study of the interactions between three different acid molecules and two different bases. By combining a comprehensive search routine covering many thousands of configurations at the semiempirical level with high level quantum chemical calculations of approximately 1000 clusters for every possible combination of clusters containing a sulfuric acid molecule, a formic acid molecule, a nitric acid molecule, an ammonia molecule, a dimethylamine molecule, and 0-5 water molecules, we have completed an exhaustive search of the DLPNO-CCSD(T)/CBS//ωB97X-D/6-31++G** Gibbs free energy surface for this system. We find that the detailed geometries of each minimum free energy cluster are often more important than traditional acid or base strength. Addition of a water molecule to a dry cluster can enhance stabilization, and we find that the (SA)(NA)(A)(DMA)(W) cluster has special stability. Equilibrium calculations of SA, FA, NA, A, DMA, and water using our quantum chemical ΔG° values for cluster formation and realistic estimates of the concentrations of these monomers in the atmosphere reveals that nitric acid can drive early stages of particle formation just as efficiently as sulfuric acid. Our results lead us to believe that particle formation in the atmosphere results from the combination of many different molecules that are able to form highly stable complexes with acid molecules such as SA, NA, and FA.

Probing the Free Energy of Small Water Clusters: Revisiting Classical Nucleation Theory

By addressing the defects in classical nucleation theory (CNT), we develop an approach for extracting the free energy of small water clusters from nucleation rate experiments without any assumptions about the form of the cluster free energy. For temperatures higher than ∼250 K, the extracted free energies from experimental data points indicate that their ratio to the free energies predicted by CNT exhibits nonmonotonic behavior as the cluster size changes. We show that this ratio increases from almost zero for monomers and passes through (at least) one maximum before approaching one for large clusters. For temperatures lower than ∼250 K, the behavior of the ratio between extracted energies and CNT's prediction changes; it increases with cluster size, but it remains below one for almost all of the experimental data points. We also applied a state-of-the-art quantum mechanics model to calculate free energies of water clusters (2-14 molecules); the results support the observed change in behavior based on temperature, albeit for temperatures above and below ∼298 K. We compared two different model chemistries, DLPNO-CCSD(T)/CBS//ωB97xD/6-31++G** and G3, against each other and the experimental value for formation of the water dimer.

Amino Acids Compete with Ammonia in Sulfuric Acid-Based Atmospheric Aerosol Prenucleation: The Case of Glycine and Serine

We present a computational investigation of the sulfuric acid, glycine, serine, ammonia, and water system to understand if this system can form prenucleation clusters, which are precursors to larger aerosols in the atmosphere. We have performed a comprehensive configurational search of all possible clusters in this system, starting with the four different monomers and zero to five waters. Accurate Gibbs free energies of formation have been calculated with the DLPNO-CCSD(T)/complete basis set (CBS) method on ωb97xd/6-31++G** geometries. For the dry dimers of sulfuric acid, the weakest base, serine, is found to form the most stable complex, which is a consequence of the strong di-ionic complex formed between the bisulfate ion and the protonated serine cation. For the dry dimers without sulfuric acid, the glycine-serine complex is more stable than the glycine-ammonia or serine-ammonia complexes, stemming from the detailed structure and not related to base strength. For the larger complexes, sulfuric acid deprotonates and the proton is shifted to glycine, serine, or ammonia. The two amino acids and ammonia are almost interchangeable and there is no easy way to predict which molecule will be protonated without the calculated results. Assuming reasonable starting concentrations and a closed system of sulfuric acid, glycine, serine, ammonia, and five waters, we predict the concentrations of all possible complexes at two temperatures spanning the troposphere. The most negative ΔG° values are a function of the detailed molecular interactions of these clusters. These details are more important than the base strength of ammonia, glycine, and serine.

Uncertainty quantification of phase transition quantities from cluster weighting calculations

In this work, we investigate how uncertainties in experimental input data influence the results of quantum cluster equilibrium calculations. In particular, we focus on the calculation of vaporization enthalpies and entropies of seven organic liquids, compare two computational approaches for their calculation and investigate how these properties are affected by changes in the experimental input data. It is observed that the vaporization enthalpies and entropies show a smooth dependence on changes in the reference density and boiling point. The reference density is found to have only a small influence on the vaporization thermodynamics, whereas the boiling point has a large influence on the vaporization enthalpy but only a small influence on the vaporization entropy. Furthermore we employed the Gauss--Hermite estimator in order to quantify the uncertainty in the thermodynamic functions that stems from inaccuracies in the experimental reference data at the example of the vaporization enthalpy of (\textit{R})-butan-2-ol. We quantify the uncertainty as 30.95~$\cdot$10$^{-3}$~kJ~mol$^{-1}$. Additionally we compare the convergence behaviour and computational effort of the Gauss--Hermite estimator with the Monte Carlo approach and show the superiority of the former. By this, we present how uncertainty quantification can be applied to examples from theoretical chemistry.

Reaction dynamics within a cluster environment

This perspective article reviews experimental and theoretical works where rare gas clusters and helium nanodroplets are used as a nanoreactor to investigate chemical dynamics in a solvent environment. A historical perspective is presented first followed by specific considerations on the mobility of reactants within these reaction media. The dynamical response of pure clusters and nanodroplets to photoexcitation is shortly reviewed before examining the role of the cluster (or nanodroplet) degrees of freedom in the photodynamics of the guest atoms and molecules.

Hydrogen-Bond Topology Is More Important Than Acid/Base Strength in Atmospheric Prenucleation Clusters

We explored the hypothesis that on the nanoscale level, acids and bases might exhibit different behavior than in bulk solution. Our study system consisted of sulfuric acid, formic acid, ammonia, and water. We calculated highly accurate Domain-based Local pair-Natural Orbital- Coupled-Cluster/Complete Basis Set (DLPNO-CCSD(T)/CBS) energies on DFT geometries and used the resulting Gibbs free energies for cluster formation to compute the overall equilibrium constants for every possible cluster. The equilibrium constants combined with the initial monomer concentrations were used to predict the formation of clusters at the top and the bottom of the troposphere. Our results show that formic acid is as effective as ammonia at forming clusters with sulfuric acid and water. The structure of formic acid is uniquely suited to form hydrogen bonds with sulfuric acid. Additionally, it can partner with water to form bridges from one side of sulfuric acid to the other, hence demonstrating that hydrogen bonding topology is more important than acid/base strength in these atmospheric prenucleation clusters.

Hydrogen Bonding and Vaporization Thermodynamics in Hexafluoroisopropanol-Acetone and -Methanol Mixtures. A Joined Cluster Analysis and Molecular Dynamic Study

Binary mixtures of hexafluoroisopropanol with either methanol or acetone are analyzed via classical molecular dynamics simulations and quantum cluster equilibrium calculations. In particular, their populations and thermodynamic properties are investigated with the binary quantum cluster equilibrium method, using our in-house code Peacemaker 2.8, upgraded with temperature-dependent parameters. A novel approach, where the final density from classical molecular dynamics, has been used to generate the necessary reference isobars. The hydrogen bond network in both type of mixtures at molar fraction of hexafluoroisopropanol of 0.2, 0.5, and 0.8 respectively is investigated via the molecular dynamics trajectories and the cluster results. In particular, the populations show that mixed clusters are preferred in both systems even at 0.2 molar fractions of hexafluoroisopropanol. Enthalpies and entropies of vaporization are calculated for the neat and mixed systems and found to be in good agreement with experimental values.

Monomers of Glycine and Serine Have a Limited Ability to Hydrate in the Atmosphere

The role of atmospheric aerosols on climate change is one of the biggest uncertainties in most global climate models. Organic aerosols have been identified as potential cloud condensation nuclei (CCN), and amino acids are organic molecules that could serve as CCN. Amino acids make up a significant portion of the total organic material in the atmosphere, and herein we present a systematic study of hydration for two of the most common atmospheric amino acids, glycine and serine. We compute DLPNO/CCSD(T)//M08-HX/MG3S thermodynamic properties and atmospheric concentrations of Gly(H₂O)_n and Ser(H₂O)_n, where n = 1-5. We predict that serine-water clusters have higher concentrations at n = 1 and 5, while glycine-water clusters have higher concentrations at n = 2-4. However, both glycine and serine are inferred to exist primarily in their nonhydrated monomer forms in the absence of other species such as sulfuric acid.

Calculation of improved enthalpy and entropy of vaporization by a modified partition function in quantum cluster equilibrium theory

In this work, we present an altered partition function that leads to an improved calculation of the enthalpy and entropy of vaporization in the framework of quantum cluster equilibrium theory. The changes are based on a previously suggested modification [S. Grimme, Chem. Eur. J. 18, 9955-9964 (2012)] of the molecular entropy calculation in the gas phase. Here, the low energy vibrational frequencies in the vibrational partition function are treated as hindered rotations instead of vibrations. The new scheme is tested on a set of nine organic solvents for the calculation of the enthalpy and entropy of vaporization. The enthalpies and entropies of vaporization show improvements from 6.5 error to 3.3 kJ mol^-1 deviation to experiment and from 28.4 error to 13.5 J mol^-1 K^-1 deviation to experiment, respectively. The effect of the corrected partition function is visible in the different populations of clusters, which become physically more meaningful in that larger clusters are higher populated in the liquid phase and the gas phase is mainly populated by the monomers. Furthermore, the corrected partition function also overcomes technical difficulties and leads to an increased stability of the calculations in regard to the size of the cluster set.

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

Globally Optimized Molecular Embeddings for Dynamic Reaction Solvate Shell Optimization and Active Site Design

We demonstrate how a full QM/MM derivatization of the recently developed GOCAT model can be utilized in the global optimization of molecular embeddings. To this end, we provide two distinct examples: An $$\text {S}_\text {N}2$$ S N 2 reaction, and one enzymatic example of recent interest, the ketosteroid isomerase. These serve us to highlight the advantages of such an approach and sketch the roadmap for further improvements.

ParAMS: Parameter Optimization for Atomistic and Molecular Simulations

This work introduces ParAMS-a versatile Python package that aims to make parametrization workflows in computational chemistry and physics more accessible, transparent, and reproducible. We demonstrate how ParAMS facilitates the parameter optimization for potential energy surface (PES) models, which can otherwise be a tedious specialist task. Because of the package's modular structure, various functionality can be easily combined to implement a diversity of parameter optimization protocols. For example, the choice of PES model and the parameter optimization algorithm can be selected independently. An illustration of ParAMS' strengths is provided in two case studies: (i) a density functional-based tight binding (DFTB) repulsive potential for the inorganic ionic crystal ZnO and (ii) a ReaxFF force field for the simulation of organic disulfides.

Calculating Reliable Gibbs Free Energies for Formation of Gas-Phase Clusters that Are Critical for Atmospheric Chemistry: (H2SO4)3

The effects of atmospheric aerosols on our climate are one of the biggest uncertainties in global climate models. Calculating the pathway for the formation of pre-nucleation clusters that become aerosols is challenging, requiring a comprehensive analysis of configurational space and highly accurate Gibbs free energy calculations. We identified a large set of minimum energy configurations of (H₂SO₄)₃ using a sampling technique based on a genetic algorithm and a stepwise density functional theory (DFT) approach and computed the thermodynamics of formation of these configurations with more accurate wavefunction-based electronic energies computed on the DFT geometries. The DLPNO-CCSD(T) methods always return more positive energies compared to the DFT energies. Within the DLPNO-CCSD(T) methods, extrapolating to the complete basis set limit gives more positive free energies compared to explicitly correlated single-point energies. The CBS extrapolation was shown to be robust as both the 4-5 inverse polynomial and Riemann zeta function schemes were within chemical accuracy of one another.

How does the composition of a PAH influence its microsolvation? A rotational spectroscopy study of the phenanthrene–water and phenanthridine–water clusters

The influence of a nitrogen atom in the backbone of a PAH was revealed by the hydrated clusters of phenanthrene and phenanthridine in a rotational spectroscopy study. Background image credit: NASA, ESA, and the Hubble Heritage Team (STScI/AURA) – ESA/Hubble Collaboration.

A method for predicting basins in the global optimization of nanoclusters with applications to AlxCuy alloys

The problem of obtaining the geometrical configuration of a molecule that minimizes its potential energy is a very complicated one for a series of applications, ranging from determining the structure of biological macromolecules to nanoclusters of atoms. Global optimization tools are available for this task, and many of them are based in performing successive local optimizations, where the starting geometries for these steps are determined by an intelligent algorithm. Here we develop a method to save computing time in the optimization of nanoclusters by predicting if a given minimum has been previously visited during local optimization steps. Our application to Cu-Al nanoalloys indicates that it is possible to save a substantial amount of computational cost. The application also reveals new promising Al_xCu_y clusters and explain their stabilities in terms of the jellium model.

Activity coefficients of binary methanol alcohol mixtures from cluster weighting

The hydrogen bond network of different small alcohols is investigated via cluster analysis. Methanol/alcohol mixtures are studied with increasing chain length and branching of the molecule. Those changes can play an important role in different fields, including solvent and metal extraction. The extended tight binding method GFN2-xTB allows the evaluation and geometry optimization of thousands of clusters built via a genetic algorithm. Interaction energies and geometries are evaluated and discussed for the neat systems. Thermodynamic properties, such as vaporization enthalpies and activity coefficients, are calculated with the binary quantum cluster equilibrium (bQCE) approach using our in-house code peacemaker 2.8. Combined distribution functions of the distances against the angles of the hydrogen bonds are evaluated for neat and mixed clusters and weighted by the equilibrium populations achieved from bQCE calculations.

Globally optimal catalytic fields for a Diels-Alder reaction

In a previous paper [M. Dittner and B. Hartke, J. Chem. Theory Comput. 14, 3547 (2018)], we introduced a preliminary version of our GOCAT (globally optimal catalyst) concept in which electrostatic catalysts are designed for arbitrary reactions by global optimization of distributed point charges that surround the reaction. In this first version, a pre-defined reaction path was kept fixed. This unrealistic assumption allowed for only small catalytic effects. In the present work, we extend our GOCAT framework by a sophisticated and robust on-the-fly reaction path optimization, plus further concomitant algorithm adaptions. This allows smaller and larger excursions from a pre-defined reaction path under the influence of the GOCAT point-charge surrounding, all the way to drastic mechanistic changes. In contrast to the restricted first GOCAT version, this new version is able to address real-life catalysis. We demonstrate this by applying it to the electrostatic catalysis of a prototypical Diels-Alder reaction. Without using any prior information, this procedure re-discovers theoretically and experimentally established features of electrostatic catalysis of this very reaction, including a field-dependent transition from the synchronous, concerted textbook mechanism to a zwitterionic two-step mechanism, and diastereomeric discrimination by suitable electric field components.

Configurational Sampling of Noncovalent (Atmospheric) Molecular Clusters: Sulfuric Acid and Guanidine

We studied the configurational sampling of noncovalently bonded molecular clusters relevant to the atmosphere. In this article, we discuss possible approaches to searching for optimal configurations and present one alternative based on systematic configurational sampling, which seems able to overcome the typical problems associated with searching for global minima on multidimensional potential energy surfaces. Since atmospheric molecular clusters are usually held together by intermolecular bonds, we also present a cost-effective strategy for treating hydrogen bonding and proton transferring by using rigid molecules and ions in different protonation states and illustrate its performance on clusters containing guanidine and sulfuric acid.

Exploring self-organization of molecular tether molecules on a gold surface by global structure optimization

We employ nondeterministic global cluster structure optimization, based on the evolutionary algorithms paradigm, to model the self-assembly of complex molecules on a surface. As a real-life application example directly related to many recent experiments, we use this approach for the assembly of triazatriangulene "platform" molecules on the Au(111) surface. Without additional restrictions like spatial discretizations, coarse-graining or precalculated adsorption poses, and despite the proof-of-principle character of this study, we achieve satisfactory qualitative agreement with several experimental observations and can provide answers to questions that experiments on these species had left open so far. © 2019 Wiley Periodicals, Inc.

Globally Optimal Catalytic Fields - Inverse Design of Abstract Embeddings for Maximum Reaction Rate Acceleration

The search for, and understanding of, good catalysts for chemical reactions is a central issue for chemists. Here, we present first steps toward developing a general computational framework to better support this task. This framework combines efficient, unbiased global optimization techniques with an abstract representation of the catalytic environment, to shrink the search space. To analyze the resulting catalytic embeddings, we employ dimensionality reduction and clustering techniques. This not only provides an inverse design approach to new catalytic embeddings but also illuminates the actual interactions behind catalytic effects. All this is illustrated here with a strictly electrostatic model for the environment and with two versions of a selected example reaction. We close with detailed discussions of future improvements of our framework.

Cluster structures influenced by interaction with a surface

Systematic investigation of how cluster structures change upon interaction with a surface, using global structure optimization by evolutionary algorithms.

Structural and homotop optimization of neutral Al–Si nanoclusters

The geometry and stability of aluminum–silicon alloys up to 13 atoms are investigated using electronic structure methods.

A global optimization perspective on molecular clusters

Although there is a long history behind the idea of chemical structure, this is a key concept that continues to challenge chemists. Chemical structure is fundamental to understanding most of the properties of matter and its knowledge for complex systems requires the use of state-of-the-art techniques, either experimental or theoretical. From the theoretical view point, one needs to establish the interaction potential among the atoms or molecules of the system, which contains all the information regarding the energy landscape, and employ optimization algorithms to discover the relevant stationary points. In particular, global optimization methods are of major importance to search for the low-energy structures of molecular aggregates. We review the application of global optimization techniques to several molecular clusters; some new results are also reported. Emphasis is given to evolutionary algorithms and their application in the study of the microsolvation of alkali-metal and Ca²⁺ ions with various types of solvents.This article is part of the themed issue 'Theoretical and computational studies of non-equilibrium and non-statistical dynamics in the gas phase, in the condensed phase and at interfaces'.

An efficient genetic algorithm for structure prediction at the nanoscale

We have developed and implemented a new global optimization technique based on a Lamarckian genetic algorithm with the focus on structure diversity. The key process in the efficient search on a given complex energy landscape proves to be the removal of duplicates that is achieved using a topological analysis of candidate structures. The careful geometrical prescreening of newly formed structures and the introduction of new mutation move classes improve the rate of success further. The power of the developed technique, implemented in the Knowledge Led Master Code, or KLMC, is demonstrated by its ability to locate and explore a challenging double funnel landscape of a Lennard-Jones 38 atom system (LJ₃₈). We apply the redeveloped KLMC to investigate three chemically different systems: ionic semiconductor (ZnO)_1-32, metallic Ni₁₃ and covalently bonded C₆₀. All four systems have been systematically explored on the energy landscape defined using interatomic potentials. The new developments allowed us to successfully locate the double funnels of LJ₃₈, find new local and global minima for ZnO clusters, extensively explore the Ni₁₃ and C₆₀ (the buckminsterfullerene, or buckyball) potential energy surfaces.

Global structure search for molecules on surfaces: Efficient sampling with curvilinear coordinates

Efficient structure search is a major challenge in computational materials science. We present a modification of the basin hopping global geometry optimization approach that uses a curvilinear coordinate system to describe global trial moves. This approach has recently been shown to be efficient in structure determination of clusters [C. Panosetti et al., Nano Lett. 15, 8044-8048 (2015)] and is here extended for its application to covalent, complex molecules and large adsorbates on surfaces. The employed automatically constructed delocalized internal coordinates are similar to molecular vibrations, which enhances the generation of chemically meaningful trial structures. By introducing flexible constraints and local translation and rotation of independent geometrical subunits, we enable the use of this method for molecules adsorbed on surfaces and interfaces. For two test systems, trans-β-ionylideneacetic acid adsorbed on a Au(111) surface and methane adsorbed on a Ag(111) surface, we obtain superior performance of the method compared to standard optimization moves based on Cartesian coordinates.