First principles global optimization of metal clusters and nanoalloys

The global optimization of nanoparticles, such as pure or bimetallic metal clusters, has become a very important and sophisticated research field in modern nanoscience. The possibility of using more rigorous quantum chemical first principle methods during the global optimization has been facilitated by the development of more powerful computer hardware as well as more efficient algorithms. In this review, recent advances in first principle global optimization methods are described, with the main focus on genetic algorithms coupled with density functional theory for optimizing sub-nanometre metal clusters and nanoalloys

Growth, coalescence and equilibration of metallic nanoparticles and nanoalloys studied by computational methods

Among nanoscale systems, metallic nanoparticles (NPs) certainly play a primary role, due to their highly tunable properties and to the wide variety of their applications. The properties of NPs are known to strongly depend on their size and geometric shape. In the case of bimetallic nanoparticles, also known as nanoalloys, further parameters can be exploited, i.e. the NP composition and the spatial arrangement of the two atomic species within the NP volume, here referred to as chemical ordering. Within this framework, the fine control of the NP configuration (here intended as the interplay between size, shape, composition and chemical ordering) is essential in sight of the possible technological applications. To this aim, a deep understanding of the NP formation process is highly desirable: one has to clearly know what are the different stages of such process, and what are the physical forces and the chemical effects involved. Moreover, a clear knowledge of the thermodynamic stability of the produced phases under the operating conditions is desirable as well. Computer simulations can be of great help in this sense, as they can provide clear information on both the equilibrium properties and the kinetic behaviour of the NPs. Specifically, the most thermodynamically favourable configurations of a given system can be determined, and the evolution pathways can be simulated and analysed at the atomic level, therefore allowing to rationalize the experimental findings. This Ph.D. thesis is devoted to the computational study of mono- and bi-metallic NPs, with particular attention to some of the nonequilibrium phenomena undergone by them. Different examples are presented and discussed; specifically, different metallic systems are treated, all of which are of great interest due to their practical applications, and different phenomena are analysed

THEORETICAL PREDICTION AND STUDIES OF SELECTED NOVEL MATERIALS UNDER AMBIENT AND EXTREME CONDITIONS

The development of powerful computer algorithms that are specialized at exploring the energy landscape of chemical systems has revolutionized chemical physics and its derived disciplines. Such algorithms that ranges from random search to genetic algorithm are capable of uncovering a geometric configuration for a combination of chemical elements with minimum energy. The unbiased particle swarm-intelligence optimization algorithm extends the capabilities of the genetic algorithm by incorporating social intelligence through particle communication. Social communication during energy surface exploration improves the efficiency and convergence of the algorithm by preventing prediction of similar-energy structures. Particle swarm-intelligence optimization algorithm is capable of solving crystal structure problems and predicting novel crystal structures across dimensions ranging from 0D (clusters) to 3D bulk solids at specific pressure. In this study, the particle swarm-intelligence optimization algorithm was used to study and solve crystal structure problems relating to two classes of materials of industrial significance – high energy density materials and bimetallic nanoclusters. As a significant step towards solving the problem of finding a single-bonded allotrope of nitrogen, we discuss the prediction and characterization of this member of very important class of material – high energy density materials (HEDMs). A new allotrope of nitrogen formed solely by N−N single bonds is predicted to exist between 100 and 150 GPa using the metadynamics algorithm with a biased potential. The crystal structure is characterized by a distorted tetrahedral network consisting of fused N8, N10, and N12 rings. Stability of the structure is established by phonon and vibrational free energy calculations at zero and finite temperatures, respectively. The simulated x-ray diffraction pattern of the new phase is compared to the pattern of a recently synthesized nitrogen phase at the same P-T conditions and an excellent agreement is observed. This suggests the new phase is likely to form above the stability field of cubic gauche (cg) phase. The outstanding metastability of the new phase is attributed to the intrinsic stability of the sp3 bonding as well as the energetically favorable dihedral angles between N−N single bonds, in either gauche or trans conformation. The results of this work after the lab-synthesized cg phase will stimulate new research on metastable phases of nitrogen and their applications as environment-friendly HEDMs. Furthermore, in the second part of this thesis, bimetallic cluster growth is theoretically explored up to the bulk phase. Small clusters provide a unique medium between a single atom and the bulk crystal. Preliminary theoretical and experimental results show that the geometric structures and electronic properties of clusters often differ radically from those of the solid state. Here, a first-principles investigation to explore the growth mechanism of bimetallic clusters AlnAun (n=1-10) and AlAu crystal structures is carried out. It was found that the tetrahedral Al2Au2 cluster can serve as the building block to construct the subsequent nanomaterials as a function of the cluster size until the AlAu bulk. The results in this work provide a clear illustration of how structure evolve from a two-atom particle to multi-atom nanoclusters, and to 3D bulk element. Continued experimental and theoretical studies of these AlnAun clusters may lead to the discovery of how properties transform from a particle to the bulk phase which has important technological implications in electronics, engineering and catalysis

Structural and electronic investigations into subnanometre metallic clusters

This thesis presents computational studies of the geometric and electronic structures and energetic properties of homo- and heterometallic subnanometre clusters (SNCs). The first two chapters give an introductory overview of nanoparticles and the basics of the sophisticated search algorithms, Genetic Algorithms (GAs), as applied to clusters, and outline a general introduction to the computational methodologies applied in this work, the coupling of the recently developed combination of GAs with Density Functional Theory (DFT) calculations. This is followed by four results chapters, in which these computational methods are adapted to several SNC systems to elucidate their applicability in catalysis. Six publications are documented in the results chapters. Gold-palladium SNCs, which are promising catalysts for a wide variety of chemical reactions, are studied extensively in chapters 3 and 4. Chapter 3 considers the evolution of structural motifs as a function of size and composition for neutral Au-Pd clusters in the gas-phase and supported on a MgO(100) surface. Quantum-regime effects are observed and energetics are further studied. In chapter 4, a rigorous approach is presented to explore structure and stability of mono-cationic Pd-doped Au clusters and their reactivity with CO gas. The Birmingham Parallel Genetic Algorithm BPGA-DFT approach is combined with experimental techniques, including mass spectrometry, and infra-red multiphoton dissociation spectroscopy. This study gives unique insights into Pd dopant effects on cluster stability, as measured by their photo-fragmentation properties, and on their CO adsorption properties. Computational investigations into AuCu SNCs, using the Mexican Enhanced Genetic Algorithm MEGA-DFT code to rationalize the efficient catalytic properties, are presented in chapter 5. Free clusters and those supported on a MgO(100) surface are compared in this approach. The interactions with the support are extensively probed in order to better understand their role in catalysis at the atomistic level. Chapter 6 is dedicated to the structural characterisation, which is vital first step in order to understanding catalytic activity, of Ru-Pt clusters, which are electrode catalysts in direct methanol fuel cells. Ru@Pt core-shell chemical ordering is predicted. Finally, overall conclusions and outlook are presented in chapter 7

Machine Learning Methods for the Accelerated Global Structural Optimization of Thiolate-Protected Gold Nanoclusters

Modern computational chemistry techniques allow for the calculation of a wide set of material properties at the level of quantum physics, but such calculations require as input the atomic structure of the material in question. The first-principles prediction of a substance’s atomic structure from knowledge of its composition is a standing challenge in chemistry and materials science, and this thesis documents efforts to surmount this challenge for a model system of thiolated gold nanoclusters. We employ a pool-based genetic algorithm to efficiently search configuration space for global optima, learning the most likely structures for a given ligated cluster composition by iteratively selecting and recombining elements from the stablest-yet-discovered examples. In previous work, density functional theory calculations were used to determine the stability of each new structure discovered by the genetic algorithm, but this approach scales poorly for ligand-terminated systems, which have more atoms and more geometric and electronic degrees of freedom. To extend the capabilities of our genetic algorithm and bring ligated systems within reach, we accelerate energetic evaluation by implementing a class of machine-learned interatomic potentials known as moment tensor potentials. After being initialized on a small set of ab initio structure-energy data, these potentials can be used to calculate energies in good agreement with DFT and to directly optimize newly generated structures via gradient descent. We make use of an active learning approach to select optimal subsets of candidate structures for the training of moment tensor potentials, to quantify the reliability of energetic evaluations by these potentials, and to prevent unrealistic structures from being propagated in the course of the genetic algorithm. By tailoring the training set to emphasize low-energy candidates, we help our potentials to learn with high accuracy the evolving hull of lowest-energy structures observed so far. Applying these methods, we study the impact of ligand substitution on the ground state structure of Au18(SR)14 and report new ground states for R = CH3

Determining Material Structures and Surface Chemistry by Genetic Algorithms and Quantum Chemical Simulations

With the advent of modern computing, the use of simulation in chemistry has become just as important as experiment. Simulations were originally only applicable to small molecules, but modern techniques, such as density functional theory (DFT) allow extension to materials science. While there are many valuable techniques for synthesis and characterization in chemistry laboratories, there are far more materials possible than can be synthesized, each with an entire host of surfaces. This wealth of chemical space to explore begs the use of computational chemistry to mimic synthesis and experimental characterization. In this work, genetic algorithms (GA), for the former, and DFT calculations, for the latter, are developed and used for the in silico exploration of materials chemistry. Genetic algorithms were first theorized in 1975 by John Holland and over the years subsequently expanded and developed for a variety of purposes. The first application to chemistry came in the early 1990’s and surface chemistry, specifically, appeared soon after. To complement the ability of a GA to explore chemical space is a second algorithmic technique: machine learning (ML) wherein a program is able to categorize or predict properties of an input after reviewing many, many examples of similar inputs. ML has more nebulous origins than GA, but applications to chemistry also appeared in the 1990’s. A history perspective and assessment of these techniques towards surface chemistry follows in this work. A GA designed to find the crystal structure of layered chemical materials given the material’s X-ray diffraction pattern is then developed. The approach reduces crystals into layers of atoms that are transformed and stacked until they repeat. In this manner, an entire crystal need only be represented by its base layer (or two, in some cases) and a set of instructions on how the layers are to be arranged and stacked. Molecules that may be present may not quite behave in this fashion, and so a second set of descriptors exist to determine the molecule’s position and orientation. Finally, the lattice of the unit cell is specified, and the structure is built to match. The GA determines the structure’s X-ray diffraction pattern, compares it against a provided experimental pattern, and assigns it a fitness value, where a higher value indicates a better match and a more fit individual. The most fit individuals mate, exchanging genetic material (which may mutate) to produce offspring which are further subjected to the same procedure. This GA can find the structure of bulk, layered, organic, and inorganic materials. Once a material’s bulk structure has been determined, surfaces of the material can be derived and analyzed by DFT. In this thesis, DFT is used to validate results from the GA regarding lithium-aluminum layered double hydroxide. Surface chemistry is more directly explored in the prediction of adsorbates on surfaces of lithiated nickel-manganese-cobalt oxide, a common cathode material in lithium-ion batteries. Surfaces are evaluated at the DFT+U level of theory, which reduces electron over-delocalization, and the energies of the surfaces both bare and with adsorbates are compared. By applying first-principles thermodynamics to predict system energies under varying temperatures and pressures, the behavior of these surfaces in experimental conditions is predicted to be mostly pristine and bare of adsorbates. For breadth, this thesis also presents an investigation of the electronic and optical properties of organic semiconductors via DFT and time-dependent DFT calculations

LEVERAGING INFORMATICS FOR ACCELERATING THE DISCOVERY OF MATERIALS

The application of materials informatics for the rational design of materials has been inspired by the increasing number of examples of success of machine learning in many fields, and it has been facilitated by the greater access to computational resources, the advances in algorithms and the growing open-source code community. This thesis presents two ways in which we have advanced the field of computational materials science through materials informatics. A promising application of materials informatics to materials science is the development of machine-learned interatomic potentials models that are orders of magnitude faster than ab initio methods such as density functional theory and can be nearly as accurate. However, these models are typically orders of magnitude slower than physics-derived models such as the embedded atom method (EAM), and they usually do not generalize well. We present a supervised machine learning approach for developing interatomic potential models to simulate atomic systems at large time and length scales from ab initio data. The models developed with our symbolic regression algorithm are computationally fast, simple (and interpretable), accurate, and transferrable. A reason for the success of our algorithm is that it learns models using a physics-informed hypothesis space. Another important component of our algorithm is the minimization of a multi-objective cost function to search simple, accurate and fast interatomic potential models. We first demonstrate our approach for elemental Cu, and then show how the models discovered for Cu transfer well to other fcc transition metals close to Cu on the periodic table. Then, we demonstrate how our algorithm can be used to discover new functional forms for the fcc transition metals close to Cu on the periodic table, benefiting from the information encoded in known models as a seed to the search. The machine learning interatomic potential models developed with our approach are 2-3 orders of magnitude faster than other machine learned potentials, they are on average one order of magnitude simpler than EAM-type models, and their transferability is at least as good as that of other EAM-type models. In addition, their simplicity opens the door for studying their functional forms to possibly gain insights into the atomic systems. This thesis also addresses the need for a database of atomically precise nanoclusters at the density functional theory level of accuracy. Our approach used a genetic algorithm to identify low-energy clusters, and to our knowledge, it constitutes the largest database of atomically precise nanoclusters at the level of accuracy of density functional theory. This database can inform studies that aim to design clusters for a variety of applications, it can be used to train machine learning models, or it can be used as a benchmark for other studies

Properties of bimetallic AuAg nanoparticles for H2 production

Nanoparticles exhibit distinctly different properties from those of bulk matter, as a result of their large surface area to volume ratio. They have been shown to be catalytically active for a number of reactions, and therefore hold great interest for use in industry, where a fine balance of turnover and cost must be achieved. This thesis focuses on bimetallic gold/silver nanoparticles, where the introduction of a secondary metallic species can be used to not only control nanoparticle properties, but also reduce scale up costs. For gold/silver bimetallic nanoparticles (nanoalloys), it is unclear as to how the two metals will mix: based on thermodynamic arguments alone, it is unclear whether similarities in atomic size and number of valence electrons lead to the formation of an alloy or a segregated core@shell arrangement. In this work, we investigate the most energetically favourable and stable chemical arrangements based on interatomic potential basin-hopping algorithms, re-optimised using density functional theory (DFT), evaluating their potential as co-catalysts for hydrogen production. Diffusion is particularly important in catalyst stability, and therefore we examine both Au-Ag interdiffusion and the interaction of Ag nanoparticles and amorphous Si (a-Si). We examine the influence of calcination processes, often used in experimental synthesis, on differing AuAg nanoparticle chemical arrangements using classical molecular dynamics simulations. Our calculations show Ag@Au nanoparticles are the most promising in terms of achieving a higher catalytic turnover; however, we also find that Ag@Au nanoparticles are particularly unstable due to the energetically favourable formation of a ‘rosette-like’ icosahedral geometry, which exposes core Ag atoms. In addition, diffusion of Ag nanoparticles into a-Si is studied for parallel comparison with experiment, performed at the University of Utrecht. Experimental observations suggest the diffusion of Ag atoms into the a-Si matrix, however, we do not observe the same computationally, suggesting that experimental voids in a-Si may facilitate this diffusion. Finally, we investigate the applicability of CO as a probe molecule for determining changes in surface composition through vibrational stretching frequencies, both experimentally and theoretically, using diffuse reflectance infrared Fourier transform spectroscopy and DFT modelling