An efficient genetic algorithm for structure prediction at the nanoscale

Discharging of Ramsdellite MnO2 Cathode in a Lithium-Ion Battery

We report an application of our unbiased Monte Carlo approach to investigate thermodynamic and electrochemical properties of lithiated manganese oxide in the ramsdellite phase (R-MnO2) to uncover the mechanism of lithium intercalation and understand charging/discharging of R-MnO2 as a cathode material in lithium-ion batteries. The lithium intercalation reaction was computationally explored by modeling thermodynamically significant distributions of lithium and reduced manganese in the R-MnO2 framework for a realistic range of lithium molar fractions 0 < x < 1 in Li x MnO2. We employed interatomic potentials and analyzed the thermodynamics of the resultant grand canonical ensemble. We found ordered or semiordered phases at x = 0.5 and 1.0 in Li x MnO2, verified by configurational entropy changes and simulated X-ray diffraction patterns of partially lithiated R-MnO2. The radial distribution functions show the preference of lithium for homogeneous distributions across the one-dimensional 2 × 1 ramsdellite channels accompanied by alternating reduced/oxidized manganese ions. The occupation of the interstitial sites in the channels is correlated with the calculated voltage profile, showing a sharp voltage drop at x = 0.5, which is explained by the energy penalty of shifting lithium ions from stable tetrahedral to unstable octahedral sites. To facilitate this work, our in-house software, Knowledge Led Master Code (KLMC) was extended to support massive parallelism on high-performance computers.

Predicting observable infrared signatures of nanosilicates in the diffuse interstellar medium

The destruction time scale of dust in the diffuse interstellar medium is estimated to be an order of magnitude shorter than its residence time. Nevertheless, dust is observed in the interstellar medium, leading to the conclusion that reformation and grain growth must take place. Direct observations of nanometre-sized silicate grains, the main constituent of interstellar dust, would provide a smoking gun for the occurrence of grain condensation in the diffuse interstellar medium. Here we employ quantum chemical calculations to obtain the mid-infrared (IR) optical properties of a library of Mg-end member silicate nanoparticles with olivine (Mg2SiO4) and pyroxene (MgSiO3) stoichiometries. We use this library as an input for a foreground-screen model to predict the spectral appearance of the absorption profile due to mixtures of bulk and nanoparticle silicates towards bright background sources. The mid-IR spectrum observed towards an O8V star or a carbon-rich Wolf-Rayet star starts to change when ∼3% of the silicate mass is in the form of nanosilicates. We predict that a 3-10% nanosilicate fraction can be detected with the James Webb Space Telescope (JWST) using the mid-IR instrument (MIRI). With our upcoming JWST observations using MIRI, we will be able to detect or place limits on the nanosilicate content in the diffuse interstellar medium, and thus potentially directly confirm interstellar dust formation.

Structure and Migration Mechanisms of Small Vacancy Clusters in Cu: A Combined EAM and DFT Study

Voids in face-centered cubic (fcc) metals are commonly assumed to form via the aggregation of vacancies; however, the mechanisms of vacancy clustering and diffusion are not fully understood. In this study, we use computational modeling to provide a detailed insight into the structures and formation energies of primary vacancy clusters, mechanisms and barriers for their migration in bulk copper, and how these properties are affected at simple grain boundaries. The calculations were carried out using embedded atom method (EAM) potentials and density functional theory (DFT) and employed the site-occupation disorder code (SOD), the activation relaxation technique nouveau (ARTn) and the knowledge led master code (KLMC). We investigate stable structures and migration paths and barriers for clusters of up to six vacancies. The migration of vacancy clusters occurs via hops of individual constituent vacancies with di-vacancies having a significantly smaller migration barrier than mono-vacancies and other clusters. This barrier is further reduced when di-vacancies interact with grain boundaries. This interaction leads to the formation of self-interstitial atoms and introduces significant changes into the boundary structure. Tetra-, penta-, and hexa-vacancy clusters exhibit increasingly complex migration paths and higher barriers than smaller clusters. Finally, a direct comparison with the DFT results shows that EAM can accurately describe the vacancy-induced relaxation effects in the Cu bulk and in grain boundaries. Significant discrepancies between the two methods were found in structures with a higher number of low-coordinated atoms, such as penta-vacancies and di-vacancy absortion by grain boundary. These results will be useful for modeling the mechanisms of diffusion of complex defect structures and provide further insights into the structural evolution of metal films under thermal and mechanical stress.

Crystal structure prediction: achievements and opportunities

This editorial gives gives an update on the current status of crystal structure prediction and its opportunities and challenges.

Search for Global Minimum Structures of P 2 n + 1 + (n = 1-15) Using xTB-Based Basin-Hopping Algorithm

A new program for searching global minimum structures of atomic clusters using basin-hopping algorithm based on the xTB method was developed here. The program can be performed with a much higher speed than its replacement directly based on DFT methods. Considering the structural varieties and complexities in finding their global minimum structures, phosphorus cluster cations were studied by the program. The global minimum structures of cationic P2n+1+ (n = 1–15) clusters are determined through the unbiased structure searching method. In the last step, further DFT optimization was performed for the selected isomers. For P2n+1+ (n = 1–4, 7), the found global minimum structures are in consistent with the ones previously reported; while for P2n+1+ (n = 5, 6, 8–12), newly found isomers are more energy-favorable than those previously reported. And those for P2n+1+ (n = 13–15) are reported here for the first time. Among them, the most stable isomers of P2n+1+ (n = 4–6, 9) are characterized by their C_3v, C_s, C_2v and C_s symmetry, in turn. But those of P2n+1+ (n = 7, 8, 10–12), no symmetry has been identified. The most stable isomers of P29+ and P31+ are characterized by single P-P bonds bridging units inside the clusters. Further analysis shows that the pnicogen bonds play an important role in the stabilization of these clusters. These results show that the new developed program is effective and robust in searching global minimum structures for atom clusters, and it also provides new insights into the role of pnicogen bonds in phosphorus clusters.

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.

Structure prediction of crystals, surfaces and nanoparticles

We review the current techniques used in the prediction of crystal structures and their surfaces and of the structures of nanoparticles. The main classes of search algorithm and energy function are summarized, and we discuss the growing role of methods based on machine learning. We illustrate the current status of the field with examples taken from metallic, inorganic and organic systems. This article is part of a discussion meeting issue 'Dynamic in situ microscopy relating structure and function'.

Computational modelling as a tool in structural science

This editorial gives a brief discussion of the current status and role of computational modelling as a technique in structural science.

Structure and Properties of Nanosilicates with Olivine (Mg2SiO4) N and Pyroxene (MgSiO3) N Compositions

Magnesium-rich silicates are ubiquitous both terrestrially and astronomically, where they are often present as small particles. Nanosized Mg-rich silicate particles are likely to be particularly important for understanding the formation, processing, and properties of cosmic dust grains. Although astronomical observations and laboratory studies have revealed much about such silicate dust, our knowledge of this hugely important class of nanosolids largely rests on top-down comparisons with the properties of bulk silicates. Herein, we provide a foundational bottom-up study of the structure and properties of Mg-rich nanosilicates based on carefully procured atomistic models. Specifically, we employ state-of-the-art global optimization methods to search for the most stable structures of silicate nanoclusters with olivine (Mg₂SiO₄) _N and pyroxene (MgSiO₃) _N compositions with N = 1-10. To ensure the reliability of our searches, we develop a new interatomic potential that has been especially tuned for nanosilicates. Subsequently, we refine these searches and calculate a range of physicochemical properties of the most stable nanoclusters using accurate density functional theory based electronic structure calculations. We report a detailed analysis of structural and energy properties, charge distributions, and infrared vibrational spectra, where in all cases we compare our finding for nanosilicates with those of the corresponding bulk silicate crystals. For most properties considered, we find large differences with respect to the bulk limit, underlining the limitations of a top-down approach for describing these species. Overall, our work provides a new platform for an accurate and detailed understanding of nanoscale silicates.

A New Genetic Algorithm Approach Applied to Atomic and Molecular Cluster Studies

A new procedure is suggested to improve genetic algorithms for the prediction of structures of nanoparticles. The strategy focuses on managing the creation of new individuals by evaluating the efficiency of operators (o₁, o₂,…,o₁₃) in generating well-adapted offspring. This is done by increasing the creation rate of operators with better performance and decreasing that rate for the ones which poorly fulfill the task of creating favorable new generation. Additionally, several strategies (thirteen at this level of approach) from different optimization techniques were implemented on the actual genetic algorithm. Trials were performed on the general case studies of 26 and 55-atom clusters with binding energy governed by a Lennard-Jones empirical potential with all individuals being created by each of the particular thirteen operators tested. A 18-atom carbon cluster and some polynitrogen systems were also studied within REBO potential and quantum approaches, respectively. Results show that our management strategy could avoid bad operators, keeping the overall method performance with great confidence. Moreover, amongst the operators taken from the literature and tested herein, the genetic algorithm was faster when the generation of new individuals was carried out by the twist operator, even when compared to commonly used operators such as Deaven and Ho cut-and-splice crossover. Operators typically designed for basin-hopping methodology also performed well on the proposed genetic algorithm scheme.

Structural properties of sub-nanometer metallic clusters

At the nanoscale, the investigation of structural features becomes fundamental as we can establish relationships between cluster geometries and their physicochemical properties. The peculiarity lies in the variety of shapes often unusual and far from any geometrical and crystallographic intuition clusters can assume. In this respect, we should treat and consider nanoparticles as a new form of matter. Nanoparticle structures depend on their size, chemical composition, ordering, as well as external conditions e.g. synthesis method, pressure, temperature, support. On top of that, at finite temperatures nanoparticles can fluctuate among different structures, opening new and exciting horizons for the design of optimal nanoparticles for advanced applications. This article aims to overview geometrical features of transition metal clusters and of their various rearrangements.

An evolutionary algorithm for the discovery of porous organic cages

The chemical and structural space of possible molecular materials is enormous, as they can, in principle, be built from any combination of organic building blocks. Here we have developed an evolutionary algorithm (EA) that can assist in the efficient exploration of chemical space for molecular materials, helping to guide synthesis to materials with promising applications. We demonstrate the utility of our EA to porous organic cages, predicting both promising targets and identifying the chemical features that emerge as important for a cage to be shape persistent or to adopt a particular cavity size. We identify that shape persistent cages require a low percentage of rotatable bonds in their precursors (<20%) and that the higher topicity building block in particular should use double bonds for rigidity. We can use the EA to explore what size ranges for precursors are required for achieving a given pore size in a cage and show that 16 Å pores, which are absent in the literature, should be synthetically achievable. Our EA implementation is adaptable and easily extendable, not only to target specific properties of porous organic cages, such as optimal encapsulants or molecular separation materials, but also to any easily calculable property of other molecular materials.

Synthesis Target Structures for Alkaline Earth Oxide Clusters

Knowing the possible structures of individual clusters in nanostructured materials is an important first step in their design. With previous structure prediction data for BaO nanoclusters as a basis, data mining techniques were used to investigate candidate structures for magnesium oxide, calcium oxide and strontium oxide clusters. The lowest-energy structures and analysis of some of their structural properties are presented here. Clusters that are predicted to be ideal targets for synthesis, based on being both the only thermally accessible minimum for their size, and a size that is thermally accessible with respect to neighbouring sizes, include global minima for: sizes n = 9 , 15 , 16 , 18 and 24 for (MgO) n ; sizes n = 8 , 9 , 12 , 16 , 18 and 24 for (CaO) n ; the greatest number of sizes of (SrO) n clusters ( n = 8 , 9 , 10 , 12 , 13 , 15 , 16 , 18 and 24); and for (BaO) n sizes of n = 8 , 10 and 16.

Thermodynamically accessible titanium clusters TiN, N = 2–32

We have performed a genetic algorithm search on the tight-binding interatomic potential energy surface (PES) for small Ti_N (N = 2–32) clusters.

What is the best or most relevant global minimum for nanoclusters? Predicting, comparing and recycling cluster structures with WASP@N

Our WASP@N project is an open-access database of cluster structures with a web-assisted interface and toolkit for structure prediction.

The Zn12O12 cluster-assembled nanowires as a highly sensitive and selective gas sensor for NO and NO2

Motivated by the recent realization of cluster-assembled nanomaterials as gas sensors, first-principles calculations are carried out to explore the stability and electronic properties of Zn₁₂O₁₂ cluster-assembled nanowires and the adsorption behaviors of environmental gases on the Zn₁₂O₁₂-based nanowires, including CO, NO, NO₂, SO₂, NH₃, CH₄, CO₂, O₂ and H₂. Our results indicate that the ultrathin Zn₁₂O₁₂ cluster-assembled nanowires are particularly thermodynamic stable at room temperature. The CO, NO, NO₂, SO₂, and NH₃ molecules are all chemisorbed on the Zn₁₂O₁₂-based nanowires with reasonable adsorption energies, but CH₄, CO₂, O₂ and H₂ molecules are only physically adsorbed on the nanowire. The electronic properties of the Zn₁₂O₁₂-based nanowire present dramatic changes after the adsorption of the NO and NO₂ molecules, especially their electric conductivity and magnetic properties, however, the other molecules adsorption hardly change the electric conductivity of the nanowire. Meanwhile, the recovery time of the nanowire sensor at T = 300 K is estimated at 1.5 μs and 16.7 μs for NO and NO₂ molecules, respectively. Furthermore, the sensitivities of NO and NO₂ are much larger than that of the other molecules. Our results thus conclude that the Zn₁₂O₁₂-based nanowire is a potential candidate for gas sensors with highly sensitivity for NO and NO₂.