The threshold algorithm: Description of the methodology and new developments

Dependence of lactose adsorption on the exposed crystal facets of metals: a comparative study of gold, silver and copper

In this theoretical work, we investigated the adsorption of a lactose molecule on metal-based surfaces, with a focus on the influence of the nature of the metal and of the type of exposed crystal facet on the adsorbed structures and energetics. More precisely, we considered three flat crystallographic facets of three face-centered cubic metals (gold, silver, and copper). For the global exploration of the energy landscape, we employed a multi-stage procedure where high-throughput searches, using a stochastic method that performs global optimization by iterating local searches, are followed by a refinement of the most probable adsorption conformations of the molecule at the ab initio level. We predicted the optimal conformation of lactose on each of the nine metal-surface combinations, classified the many low-energy minima into possible adsorption modes, and analyzed the structural, electronic and energetic aspects of the lactose molecule on the surface, as well as their dependence on the type of metal and exposed crystal facet. We observed structural similarities between the various minimum-energy conformations of lactose in vacuum and on the surface, a rough correlation between adsorption and interaction energies of the molecule, and a small charge transfer between molecule and surface whose direction is metal-dependent. During adsorption, an electronic reorganization occurs at the metal-molecule interface only, without affecting the vacuum-pointing atoms of the lactose molecule. For all types of surfaces, lactose exhibits the weakest adsorption on silver substrates, while for each coinage metal the adsorption is strongest on the (110) crystal facet. This study demonstrates that the control of exposed facets can allow to modulate the interaction between metals and small saccharides.

Energy landscapes-Past, present, and future: A perspective

Energy landscapes and the closely related cost function landscapes have been recognized in science, mathematics, and various other fields such as economics as being highly useful paradigms and tools for the description and analysis of the properties of many systems, ranging from glasses, proteins, and abstract global optimization problems to business models. A multitude of algorithms for the exploration and exploitation of such landscapes have been developed over the past five decades in the various fields of applications, where many re-inventions but also much cross-fertilization have occurred. Twenty-five years ago, trying to increase the fruitful interactions between workers in different fields led to the creation of workshops and small conferences dedicated to the study of energy landscapes in general instead of only focusing on specific applications. In this perspective, I will present some history of the development of energy landscape studies and try to provide an outlook on in what directions the field might evolve in the future and what larger challenges are going to lie ahead, both from a conceptual and a practical point of view, with the main focus on applications of energy landscapes in chemistry and physics.

Energy Landscape of Relaxation and Interaction of an Amino Acid, Glutamine (L), on Pristine and Au/Ag/Cu-Doped TiO2 Surfaces

Studying the interaction of inorganic systems with organic ones is a highly important avenue for finding new drugs and treatment methods. Tumor cells show an increased demand for amino acids due to their rapid proliferation; thus, targeting their metabolism is becoming a potential oncological therapeutic strategy. One of the inorganic materials that show antitumor properties is titanium dioxide, while its doping was found to enhance interactions with biological systems. Thus, in this study, we investigated the energy landscape of glutamine (L), an amino acid, on pristine and doped TiO2 surfaces. We first locally optimized 2D-slab structures of pristine and Au/Ag/Cu-doped anatase (001 and 101 surfaces) and similarly optimized a single molecule of glutamine in vacuum. Next, we placed the pre-optimized glutamine molecule in various orientations and on a variety of locations onto the relaxed substrate surfaces (in vacuum) and performed ab initio relaxations of the molecule on the substrate slabs. We employed the DFT method with a GGA-PBE functional implemented in the Quantum Espresso code. Comparisons of the optimized conformations and electronic structures of the amino acid in vacuum and on the surfaces yield useful insights into various biological processes.

Low-Energy Transformation Pathways between Naphthalene Isomers

The transformation pathways between low-energy naphthalene isomers are studied by investigating the topology of the energy landscape of this astrophysically relevant molecule. The threshold algorithm is used to identify the minima basins of the isomers on the potential energy surface of the system and to evaluate the probability flows between them. The transition pathways between the different basins and the associated probabilities were investigated for several lid energies up to 11 eV, this value being close to the highest photon energy in the interstellar medium (13.6 eV). More than a hundred isomers were identified and a set of 23 minima was selected among them, on the basis of their energy and probability of occurrence. The return probabilities of these 23 minima and the transition probabilities between them were computed for several lid energies up to 11 eV. The first connection appeared at 3.5 eV while all minima were found to be connected at 9.5 eV. The local density of state was also sampled inside their respective basins. This work gives insight into both energy and entropic barriers separating the different basins, which also provides information about the transition regions of the energy landscape.

Reducing overprediction of molecular crystal structures via threshold clustering

Crystal structure prediction is becoming an increasingly valuable tool for assessing polymorphism of crystalline molecular compounds, yet invariably, it overpredicts the number of polymorphs. One of the causes for this overprediction is in neglecting the coalescence of potential energy minima, separated by relatively small energy barriers, into a single basin at finite temperature. Considering this, we demonstrate a method underpinned by the threshold algorithm for clustering potential energy minima into basins, thereby identifying kinetically stable polymorphs and reducing overprediction.

GIGA: a versatile genetic algorithm for free and supported clusters and nanoparticles in the presence of ligands

We present a versatile parallelised genetic algorithm, which is able to perform global optimisation from first principles for pure and mixed free clusters in the gas phase, supported on surfaces or in the presence of one or several atomic or molecular species (ligands or adsorbates). The genetic algorithm is coupled to different quantum chemical software packages in order to permit a large variety of methods for the global optimisation. The genetic algorithm is also capable of optimising different electronic spin multiplicities explicitly, which allows global optimisation on several potential energy hypersurfaces in parallel. We employ the genetic algorithm to study ligand-passivated clusters [Cd3Se3(H2S)3]+ and to investigate adsorption of [Pt6(H2O)2]+ supported on graphene. The explicit consideration of the electronic spin multiplicity during global optimisation is investigated for nanoalloy clusters Pt4V2.

Polymorphism in carbohydrate self-assembly at surfaces: STM imaging and theoretical modelling of trehalose on Cu(100)

Saccharides, also commonly known as carbohydrates, are ubiquitous biomolecules, but little is known about their interaction with surfaces. Soft-landing electrospray ion beam deposition in conjunction with high-resolution imaging by scanning tunneling microscopy now provides access to the molecular details of the surface assembly of this important class of bio-molecules. Among carbohydrates, the disaccharide trehalose is outstanding as it enables strong anhydrobiotic effects in biosystems. This ability is closely related to the observed polymorphism. In this work, we explore the self-assembly of trehalose on the Cu(100) surface. Molecular imaging reveals the details of the assembly properties in this reduced symmetry environment. Already at room temperature, we observe a variety of self-assembled motifs, in contrast to other disaccharides like e.g. sucrose. Using a multistage modeling approach, we rationalize the conformation of trehalose on the copper surface as well as the intermolecular interactions and the self-assembly behavior.

We rationalize the experimentally observed variety of trehalose assemblies on Cu(100) by modeling based on STM images and global optimization.

Au147 nanoparticles: Ordered or amorphous?

Structural aspects of the Au₁₄₇ cluster have been investigated through a density functional based tight binding global optimization involving a parallel tempering molecular dynamics scheme with quenching followed by geometries relaxation at the Density Functional Theory (DFT) level. The focus is put on the competition between relaxed ordered regular geometries and disordered (or amorphous) structures. The present work shows that Au₁₄₇ amorphous geometries are relevant low energy candidates and are likely to contribute in finite temperature dynamics and thermodynamics. The structure of the amorphous-like isomers is discussed from the anisotropy parameters, the atomic coordinations, the radial and pair distribution functions, the IR spectra, and the vibrational DOS. With respect to the regular structures, the amorphous geometries are shown to be characterized by a larger number of surface atoms, a less dense volume with reduced coordination number per atom, a propensity to increase the dimension of flat facets at the surface, and a stronger anisotropy. Moreover, all amorphous clusters have similar IR spectra, almost continuous with active frequencies over the whole spectral range, while symmetric clusters are characterized by a few lines with large intensities.