Academic literature on the topic 'Condensed matter systems'

In this paper, general description of a cellular structure formation in a system of interacting particles has been proposed. Analytical results are presented for such structures in colloids, systems of particles immersed into a liquid crystal and gravitational systems. It is shown that physical nature of formation of cellular structures in all systems of interacting particles is identical. In all cases, a characteristic of the cellular structure, depending on strength of the interaction, concentration of particles and temperature, can be obtained.

Physical systems composed of a large number of reciprocally interacting constituents provide the natural context for the rise of emergent phenomena. Despite the intrinsic difficulty in providing a mathematical definition of what is meant for ‘emergence’ (see [Baas, in Langton, Alife III, Santa Fe Studies in the Sciences of Complexity, Proc. Volume XVII, Addison-Wesley, (1994)]), the intuitive notion of emergent property is that of a collection of interact- ing objects showing a novel collective behavior, qualitatively different from and not immediately attributable to the behaviors of the individual components. Non-linear interactions among elements of the system, or interactions between the system and the environment, or merely the large number of constituents are usually the motivations addressed to be responsible for emergent behavior. It is important to remark that emergent properties can only be inferred from a comprehension of the collective properties of the microscopic constituents [Kivelson et al, npj Quant. Mater. 1, 16024 (2016)]. In this regard, computer simulations provide a unique tool to support experimental observation, develop abstract models and investigate systems’ properties at a microscopic level. In general, condensed matter, particularly soft matter but also the complex systems studied in Physics, are necessarily described via simplified models, which include the key features of the corresponding real systems. On the one hand, this certainly represents a powerful approach when it finds its roots in the concept of universality, connected with critical phenomena, but this also turns into a limiting factor for the realistic description of the considered phenomena. On the other hand, it makes the properties of such abstract simulated systems calculable and investigable via computer simulations. As a consequence, the simulations assume a key role in complementing the comparison between experiments and theory [Frenkel and Smit, Understanding Molecular Simulations, Academic Press (2002); Allen and Tildesley, Computer simulation of liquids, Oxford University Press (2017)]. In this sense, simulations are often regarded as being computer experiments, in which materials properties and novel phases of matter can be investigated. The present PhD thesis is a collection of the main results coming from four different research lines which I have been involved into in the last 3 years. The topics could appear to be rather diverse but they are all connected by the presence of emergent phenomena which were studied via computer simulations (Molecular Dynamics and Monte Carlo methods, mainly). Three of these four research lines are related to collaborations with as many experimental groups. The first group I started collaborating with is led by dr. R. Grisenti, at the University of Frankfurt (https://www.atom. uni-frankfurt.de/hhng-grisenti/index.html). As reported in Chapter 1 and in a recent paper which I contributed to as first co-author [Schottelius, Mambretti et al., Nat. Mat. (2020)], we studied the crystal growth of supercooled Ar–Kr liquid mixtures by means of a micro–jet experiment, Molecular Dynamics simulation and thermodynamic analysis. The second ongoing collaboration is with the group of prof. P. Milani, which is the leader of the CIMaINa laboratories (http://cimaina.unimi.it/) at the Università degli Studi di Milano. We developed an abstract stochastic model of resistive switching devices that they are characterizing for neuromorphic applications (see Chapter 3). More recently, I started a collaboration with the group led by prof. T. Bellini at the Università degli Studi di Milano (https://sites.google.com/site/unimisoft/), in order to investigate the spinodal decomposition of mixtures of DNA nanostars via light scattering experiments and Monte Carlo simulations, as described in Chapter 4. I will now provide a brief overview of the contents of each Chapter, where each Chapter corresponds to a different research line. Crystal growth from a supercooled melt is of fundamental theoretical and practical importance in many fields, ranging from materials science to the production of phase–change memories. To date, the temperature dependence of the growth rates of many materials, including pure metals, metallic alloys, colloids and many others are still under intense scrutiny (see e.g. Tang et al., Nat. Mat. (2013) and Sun et al., Nat. Mat. (2018)). The majority of systems display a maximum growth rate at a temperature located between the melting point and the glass transition [Orava et al., J. Chem. Phys. (2014)]. Several materials are characterized by a range of many orders of magnitude between this maximum value and the crystal growth rates measured in other regimes. We still lack a deep comprehension of the mechanism underlying this phenomenology, which emerges from experiments and simulations both. Classical models of crystal growth from a melt hypothesize either a diffusion-limited process, or a collision–limited one, but for a lot of materials them both fail to fit the available data. This situation claims for further investigation about the key elements that tune the crystal growth rates from supercooled liquids, extending the current theoretical framework. Jointly with the experimental group of dr. Grisenti (which performed measurements at the EU-XFEL facility https://www.xfel.eu/), we studied the crystallization of supercooled mixtures of argon and krypton via Molecular Dynamics. Our results showed that their crystal growth rates (obtained from the analysis of simulated configurations exploiting Steinhardt angular order parameters) can be reconciled with existing crystal growth models only by explicitly accounting for the non–ideality of the mixtures. Our theoretical and computational contribution aided in highlighting the importance of thermodynamic aspects in describing the crystal growth kinetics, yielding a substantial step towards a more sophisticated theory of crystal growth. A second project concerns the study of soft matter systems in one dimension (1D), detailed in Chapter 2. Soft matter systems are made of particles which can overlap by paying a finite energy cost and they are renowned for being able to display complex emerging phenomena. Some of them, for example, are characterized by the presence of clustering phases [Prestipino, Phys. Rev. E (2014)]. Recently, a surprising quantum phase transition has been revealed in a 1D system composed of bosons interacting via a pairwise soft potential in the continuum. It was shown that the spatial coordinates undergoing two-particle clustering could be mapped into quantum spin variables of a 1D transverse Ising model [Rossotti et al., Phys. Rev. Lett. (2017)]. Extending the description and the results provided in a very recent paper I contributed to as first author [Mambretti et al., Phys. Rev. E (2020)], in the second Chapter we investigate the manifestation of an analogous critical phenomenon in 1D classical fluids of soft particles in the continuum. In particular, we studied the low–temperature behavior of three different classical models of 1D soft matter, whose inter–particle interactions allow for cluster- ing. The two–particle cluster phase is largely explored, by simulating the systems at the commensurate density via Monte Carlo and Simulated Annealing methods. The same string variables exploited in the aforementioned quantum case highlight that, at the right commensurate density, the peculiar pairing of neighboring soft particles can be nontrivially mapped onto a 1D discrete classical Ising model. We also observe a related phenomenon, i.e. the presence of an anomalous peak in the low–temperature specific heat, thus indicating the emergence of Schottky phenomenology in a non–magnetic fluid. The third Chapter presents the case of an electrical resistor network featuring novel emergent properties, such as memristivity and the possibility to be used as a self–assembled logic gate; an article on this topic is currently in preparation. The growing difficulties arising in the improvement of the performance of standard computing architectures encouraged the quest for different approaches aiming at reproducing the computational capability and energy efficiency of the human brain, by mimicking neurons and synapses as probabilistic computing units [Markovic et al., Nat. Rev. Phys. 2, 499–510 (2020)]. Networks based on the bottom–up assembling of nanoscale building blocks and characterized by resistive switching (RS) activities are becoming increasingly popular as possible solutions for a straightforward fabrication of complex architectures with neuromorphic features [Wang et al., Nat. Rev. Mat. 5, 173-195 (2020)]. Specifically, it has recently been demonstrated that metallic nanostructured Au films, under certain conditions show a non–ohmic electrical behavior and complex and reproducible resistive switching, which can be exploited for the innovative realization of logic gates. In these devices, the nonlinear dynamic switching behavior resulting from an applied input voltage can be exploited for developing hardware for reservoir computing applications. In Chapter 3, I show how it is possible to simulate a complex model (Stochastic Resistor Network Model, SRNM) able to imi- tate the phenomenology and give hints for the development of experiments ongoing at the CIMaINa research laboratories, regarding the electrical current passage through nanostructured cluster gold films [Mirigliano et al., Nanotechnology, 31, 23, (2020)]. To this purpose, I personally contributed to develop from scratch a C++ code, parallelized via the Armadillo library (http://arma.sourceforge.net/). To study the electrical transport properties of this system, we modeled the experimental sample as a network of interconnected resistors whose effective resistance under a given voltage can be determined using spectral graph theory. The network state evolves stochastically via random physically–inspired update moves, and its effective total resistance (and the related Power Spectral Density) has been analyzed. The structure and the topology of the network were studied via the investigation of the shortest path connecting the source and the sink of the system, thus exploring the possible paths in which the current could flow. Moreover, we also applied Information Theory entropy–based tools to investigate the time evolution of network resistance at a local, coarse–grained, scale. We observed that specific input signals corresponding to 2 logical ‘bits’ pro- duce rich outputs associable to a logical NAND gate, which posses functional completeness. Given that relevant differences could be detected between the behavior of the network at low voltage before and after the so called ‘writing’ step (where the system is under a high applied voltage), memristive effects naturally emerge in the study of network properties. These results encourage further investigations, both experimental and via the innovative SRNM approach we developed, in order to exploit these RS devices in hardware computing applications as self–assembled logic gates. Last, in Chapter 4 I focus on another soft matter system, that I have started to investigate during my PhD research activity, regarding Monte Carlo simulations of low valence DNA–based colloidal particles. This last Chapter is mainly devoted to the description of the simulation method I have been developing during my more recent PhD research activities, while the preliminary results presented obviously need to be confirmed and extended by further studies. Particles with a limited number of attractive spots (patches) on their surface are generally characterized by non–crystalline low energy states; they rather generate a disordered 3D network in which all the sticky sites are engaged in (mutually exclusive) patch–patch bonds [Bianchi et al., Phys. Rev. Lett. (2006)]. One of the most promising experimental realizations of such peculiar colloids is extremely recent: laboratory synthesized DNA nanostars (NS) with fixed valence [Bi et al., PNAS (2013)]. In this field the landmark is represented by our collaborators from the group led by prof. T. Bellini. Recently, they started to investigate the behavior of mixtures of nanostars with leftwise or rightwise chirality of the DNA strands, characterized by a merely repulsive interspecies interaction. To date, our contribution mainly consisted in the development of an abstract model of these DNA nanostars, schematized as limited valence soft patchy particles, whose equilibrium configurations are sampled via a canonical Monte Carlo program. Their different chirality is represented by a mixed interaction which only comprises excluded volume terms. Our goal in this project is twofold: on the one hand, we aim to reconstruct the temperature–density phase diagram of such mixtures, also depending on the mixing ratio. Experiments revealed a critical behavior and a phase separation processes for dilute mono–component DNA solutions; the properties of a mixture of two components, each found in critical conditions, are studied in this work. In this Chapter, after a detailed overview of the experimental, computational and theoretical studies regarding low valence particles, the simulation code is described and it is presented a comparison between the simulation results and the experimental measurements at equilibrium. The peculiar structures found in the patchy particles network claim for further analysis, as well as the interesting behavior near the critical point for mono–component and bi–component systems both. The second perspective of this research regards the unexplored aggregation and cluster growth process of such particles. In this concern, part of the future research effort will be devoted to the transformation of our custom code into a Brownian Monte Carlo in order to unveil the mechanisms underlying the dynamics of such particles during their aggregation stages. The conclusions and further perspectives concerning each of the four topics addressed in this work can be retrieved at the end of each Chapter.

Le sujet de cette these porte sur des calculs ab initio de proprietes d'etat fondamental et excite de systemes differents, dans le cadre de la dft et de la tddft. Du cote numerique, nous avons mis en oeuvre une methode originale pour l'evaluation de la polarisabilite dynamique a particules independantes, et generalise un code au spin dans le but d'etudier les proprietes magnetiques de systemes reels. Nous avons etudie des spectres de reflectivite anisotrope et de perte d'energie pour la surface (100) du silicium, propre ou oxydee. La comparaison entre les spectres mesures et simules, nous ont permis d'exclure la reconstruction p(2x1). En suite, nous avons mis en evidence le probleme de la description des excitations des systemes a couche ouverte presente un etude des proprietes optiques d'alliage magnetique interessants pour des applications en spintronique. Nous avons evalue les proprietes d'etat fondamental de ces alliages et la conductivite du fer ccc.

This thesis presents work done on the calculation of the effects of anharmonic nuclear motion on the properties of solid materials from first principles. Such anharmonic effects can be significant in many cases. A vibrational self-consistent field (VSCF) method is used as the basis for these calculations, which is then improved and applied to a variety of solid state systems. Firstly, work done to improve the efficiency of the VSCF method is presented. The standard VSCF method involves using density functional theory (DFT) to map the Born-Oppenheimer (BO) energy surface that the nuclei move in, a computationally expensive process. It is shown that the accurate forces available in plane-wave basis DFT can be used to help map the BO surface more accurately and reduce the computational cost. This improved VSCF+f method is tested on molecular and solid hydrogen, as well as lithium and zirconium, and is found to give a speed-up of up to 40%. The VSCF method is then applied to two different systems of physical interest. It is first applied to the case of the neutral vacancy in diamond, in order to resolve a known discrepancy between harmonic ab initio calculations and experiment -- the former predict a static Jahn-Teller distortion, whilst the latter leads to a dynamic Jahn-Teller effect. By including anharmonic corrections to the energy and nuclear wavefunction, we show that the inclusion of these effects results in agreement between first-principles calculations and experiment for the first time. Lastly, the VSCF method is applied to barium titanate, a prototypical ferroelectric material which undergoes a series of phase transitions from around 400 K downwards. The nature of these phase transitions is still unclear, and understanding them is an active area of research. We describe the physics of the phase transitions of barium titanate, including both anharmonicity and the effect of polarisation caused by long wavelength vibrations, to help understand the important physics from first principles.

This thesis is devoted to the ab initio calculation of ground state and excited state properties of different systems within the density functional theory and the time dependent density functional theory.From the numerical point of view we implemented an original method in a plane waves code devoted to calculate the independent particle response function.Moreover, we generalized the same code to the spin degree of freedom in order to study the magnetic properties of realistic condensed matter systems.We studied the reflectance anisotropy spectra and the energy loss spectraof the clean and oxidized surface, and we performed the analysis of the originof the main spectral features.Thanks to the comparison between experimental and theoretical REEL spectra,we could roule out the p(2x1) reconstruction for this surfcace.Moreover, we evidenced the problem of the correct description of the excitation spectra for open shell systems within the TDDFT framework, in thecase of the simple BeH molecule.In the second part of the thesis, we presented the study of the opticalproperties of magnetic systems such as FeS2, CoS2 or NiS2, interesting materialsfor possible technological applications in the growing field of spintronics.Within this context we calculated the ground state properties and the opticalcondictivity of BCC bulk iron, for which we found a nice agreement withavailable experimental data.

Motivated by the large diversity of two dimensional condensed matter systems, various two dimensional models were studied using computer simulation. The structure and dynamics in a monolayer of dipolar soft spheres were studied using Molecular Dynamics simulation. This is a model for colloidal ferrofluids. Langevin Dynamics simulations have been employed to study the aggregate kinetics of the dipolar chains and rings starting from a ‘random’ configuration. Clusters were identified using an energy criterion, and classified as chains, rings or defect clusters. The mechanisms by which these clusters form are discussed. At high density, there is a high transient concentration of defects, indicating an interconnected network structure. This suggests that the phase transition proposed by Tlusty and Safran [Tlusty and Safran, Science 290, 1328 (2000)] could be recovered as a metastable phase transition if the system could be kinetically trapped in this transient state. The dynamics of antiferromagnetically coupled Heisenberg spins on a kagomé lattice has been studied using numerical simulation. This system is highly frustrated – the lattice places competing constraints on the spins. We investigate the effect of breaking bonds in the lattice, which relieves the frustration to a certain extent. The phase diagram of a two dimensional system of hard disk trimers has been explored by Monte Carlo simulation. This might serve as a coarse grained model for the aggregation of proteins in a biological membrane. Many proteins are roughly triangular in shape or form trimeric units. The model consists of three hard disks fused together in a triangular arrangement. One of the disks interacts with the corresponding disk on other trimers via a square-well potential, representing specific interactions between the protein molecules. In the fluid phase at low density the trimers form aggregates containing typically four to six trimers. In the solid phase, the trimers pack such that individual disks are on a triangular lattice. There are different possible packings of the trimers consistent with this packing of individual disks.

Sorption of hydrophobic compounds to aliphatic components of soil organic matter (SOM) is poorly understood even though these aliphatic carbons are a major fraction of SOM. The main source of aliphatic compounds in SOM is above- and below-ground plant cuticular materials (cutin, cutan and suberin). As decomposition proceeds, these aliphatic moieties tend to accumulate in soils. Therefore, if we consider that cuticular material contributes significantly to SOM, we can hypothesize that the cuticular materials play an important role in the sorption processes of hydrophobic compounds (including pesticides) in soils, which has not yet been studied. The overall goal of this research was to illustrate the mechanism and significance of the refractory aliphatic structures of SOM in sorbing hydrophobic compounds (nonionic and weakly polar pesticides). The importance of this study is related to our ability to demonstrate the sorption relationship between key pesticides and an important fraction of SOM. The specific objectives of the project were: (1) To isolate and characterize cuticular fractions from selected plants; (2) To investigate the sorption mechanism of key hydrophobic pesticides and model compounds to cuticular plant materials; (3) To examine the sorption mechanisms at the molecular level using spectroscopic techniques; (4) To investigate the sorption of key hydrophobic pesticides to synthetic polymers; (5) To evaluate the content of cuticular materials in agricultural soils; and (6) To study the effect of incubation of plant cuticular materials in soils on their sorptive capabilities. This project demonstrates the markedly high sorption capacity of various plant cuticular fractions for hydrophobic organic compounds (HOCs) and polar organic pollutants. Both cutin (the main polymer of the cuticle) and cutan biopolymers exhibit high sorption capability even though both sorbents are highly aliphatic in nature. Sorption by plant cuticular matter occurs via hydrophobic interactions and H-bonding interactions with polar sorbates. The cutin biopolymer seems to facilitate reversible and noncompetitive sorption, probably due to its rubbery nature. On the other hand, the epicuticular waxes facilitate enhance desorption in a bi-solute system. These processes are possibly related to phase transition (melting) of the waxes that occur in the presence of high solute loading. Moreover, our data highlight the significance of polarity and accessibility of organic matter in the uptake of nonpolar and polar organic pollutants by regulating the compatibility of sorbate to sorbent. In summary, our data collected in the BARD project suggest that both cutin and cutan play important roles in the sorption of HOCs in soils; however, with decomposition the more condensed structure of the cutin and mainly the cutan biopolymer dominated sorption to the cuticle residues. Since cutin and cutan have been identified as part of SOM and humic substances, it is suggested that retention of HOCs in soils is also controlled by these aliphatic domains and not only by the aromaticrich fractions of SOM.