Calix[6]arene Derivatives as Pivotal Components of Working Devices and Molecular Machine Prototypes

This Ph.D. thesis describes the synthesis, characterization and study of calix[6]arene derivatives as pivotal components for the construction of molecular machine prototypes. Initially, the ability of a calix[6]arene wheel to supramolecularly assist and increase the rate of a nucleophilic substitution reaction was exploited for the synthesis of two constitutionally isomeric oriented rotaxanes. Then, the synthesis and characterization of several hetero-functionalised calix[6]arene derivatives and the possibility to obtain molecular muscle prototypes was reported. The ability of calix[6]arenes to form oriented pseudorotaxane towards dialkyl viologen axles was then exploited for the synthesis of two calixarene-based [2]catenanes. As last part of this thesis, studies on the electrochemical response of the threading-dethreading process of calix[6]arene-based pseudorotaxanes and rotaxanes supported on glassy carbon electrodes are reported.

Molecular approaches to smart materials via interface recognition and conformationally switchable cavitands

In this thesis the molecular level design of functional materials and systems is reported. In the first part, tetraphosphonate cavitand (Tiiii) recognition properties towards amino acids are studied both in the solid state, through single crystal X-ray diffraction, and in solution, via NMR and ITC experiments. The complexation ability of these supramolecular receptors is then applied to the detection of biologically remarkable N-methylated amino acids and peptides using complex dynamic emulsions-based sensing platforms. In the second part, a general supramolecular approach for surface decoration with single-molecule magnets (SMMs) is presented. The self-assembly of SMMs is achieved through the formation of a multiple hydrogen bonds architecture (UPy-NaPy complexation). Finally we explore the possibility to impart auxetic behavior to polymeric material through the introduction of conformationally switchable monomers, namely tetraquinoxaline cavitands (QxCav). Their interconversion from a closed vase conformation to an extended kite form is studied first in solution, then in polymeric matrixes via pH and tensile stimuli by UV-Vis spectroscopy.

Modeling the molecular weight distribution of block copolymer formation in a reversible addition-fragmentation chain transfer mediated living radical polymerization

Block copolymers have become an integral part of the preparation of complex architectures through self-assembly. The use of reversible addition-fragmentation chain transfer (RAFT) allows blocks ranging from functional to nonfunctional polymers to be made with predictable molecular weight distributions. This article models block formation by varying many of the kinetic parameters. The simulations provide insight into the overall polydispersities (PDIs) that will be obtained when the chain-transfer constants in the main equilibrium steps are varied from 100 to 0.5. When the first dormant block [polymer-S-C(Z)=S] has a PDI of 1 and the second propagating radical has a low reactivity to the RAFT moiety, the overall PDI will be greater than 1 and dependent on the weight fraction of each block. When the first block has a PDI of 2 and the second propagating radical has a low reactivity to the RAFT moiety, the PDI will decrease to around 1.5 because of random coupling of two broad distributions. It is also shown how we can in principle use only one RAFT agent to obtain block copolymers with any desired molecular weight distribution. We can accomplish this by maintaining the monomer concentration at a constant level in the reactor over the course of the reaction. (c) 2005 Wiley Periodicals, Inc.

Molecular dynamics simulations of the hydrophobin SC3 at a hydrophobic/hydrophilic interface

Hydrophobins are small (similar to 100 aa) proteins that have an important role in the growth and development of mycelial fungi. They are surface active and, after secretion by the fungi, self-assemble into amphipathic membranes at hydrophobic/hydrophilic interfaces, reversing the hydrophobicity of the surface. In this study, molecular dynamics simulation techniques have been used to model the process by which a specific class I hydrophobin, SC3, binds to a range of hydrophobic/ hydrophilic interfaces. The structure of SC3 used in this investigation was modeled based on the crystal structure of the class II hydrophobin HFBII using the assumption that the disulfide pairings of the eight conserved cysteine residues are maintained. The proposed model for SC3 in aqueous solution is compact and globular containing primarily P-strand and coil structures. The behavior of this model of SC3 was investigated at an air/water, an oil/water, and a hydrophobic solid/water interface. It was found that SC3 preferentially binds to the interfaces via the loop region between the third and fourth cysteine residues and that binding is associated with an increase in a-helix formation in qualitative agreement with experiment. Based on a combination of the available experiment data and the current simulation studies, we propose a possible model for SC3 self-assembly on a hydrophobic solid/water interface.

Advancing atomic nanolithography: cold atomic Cs beam exposure of alkanethiol self-assembled monolayers

We report the results of a study into the quality of functionalized surfaces for nanolithographic imaging. Self-assembled monolayer (SAM) coverage, subsequent post-etch pattern definition and minimum feature size all depend on the quality of the Au substrate used in atomic nanolithographic experiments. We find sputtered Au substrates yield much smoother surfaces and a higher density of {111} oriented grains than evaporated Au surfaces. A detailed study of the self-assembly mechanism using molecular resolution AFM and STM has shown that the monolayer is composed of domains with sizes typically of 5-25 nm, and multiple molecular domains can exist within one Au grain. Exposure of the SAM to an optically-cooled atomic Cs beam traversing a two-dimensional array of submicron material masks ans also standing wave optical masks allowed determination of the minimum average Cs dose (2 Cs atoms per SAM molecule) and the realization of < 50 nm structures. The SAM monolayer contains many non-uniformities such as pin-holes, domain boundaries and monoatomic depressions which are present in the Au surface prior to SAM adsorption. These imperfections limit the use of alkanethiols as a resist in atomic nanolithography experiments. These studies have allowed us to realize an Atom Pencil suitable for deposition of precision quantities of material at the microand nanoscale to an active surface.

The nature of alkanethiol self-assembled monolayer adsorption on sputtered gold substrates

A detailed study of the self-assembly and coverage by 1-nonanethiol of sputtered Au surfaces using molecular resolution atomic force microscopy (AFM) and scanning tunneling microscopy (STM) is presented. The monolayer self-assembles on a smooth Au surface composed predominantly of {111} oriented grains. The domains of the alkanethiol monolayer are observed with sizes typically of 5-25 nm, and multiple molecular domains can exist within one Au grain. STM imaging shows that the (4 × 2) superlattice structure is observed as a (3 × 2√3) structure when imaged under noncontact AFM conditions. The 1-nonanethiol molecules reside in the threefold hollow sites of the Au{111} lattice and aligned along its lattice vectors. The self-assembled monolayer (SAM) contains many nonuniformities such as pinholes, domain boundaries, and monatomic depressions which are present in the Au surface prior to SAM adsorption. The detailed observations demonstrate limitations to the application of 1-nonanethiol as a resist in atomic nanolithography experiments to feature sizes of ∼20 nm.

Resonance Energy Transfer-Based Molecular Switch Designed Using a Systematic Design Process Based on Monte Carlo Methods and Markov Chains

A RET network consists of a network of photo-active molecules called chromophores that can participate in inter-molecular energy transfer called resonance energy transfer (RET). RET networks are used in a variety of applications including cryptographic devices, storage systems, light harvesting complexes, biological sensors, and molecular rulers. In this dissertation, we focus on creating a RET device called closed-diffusive exciton valve (C-DEV) in which the input to output transfer function is controlled by an external energy source, similar to a semiconductor transistor like the MOSFET. Due to their biocompatibility, molecular devices like the C-DEVs can be used to introduce computing power in biological, organic, and aqueous environments such as living cells. Furthermore, the underlying physics in RET devices are stochastic in nature, making them suitable for stochastic computing in which true random distribution generation is critical.

In order to determine a valid configuration of chromophores for the C-DEV, we developed a systematic process based on user-guided design space pruning techniques and built-in simulation tools. We show that our C-DEV is 15x better than C-DEVs designed using ad hoc methods that rely on limited data from prior experiments. We also show ways in which the C-DEV can be improved further and how different varieties of C-DEVs can be combined to form more complex logic circuits. Moreover, the systematic design process can be used to search for valid chromophore network configurations for a variety of RET applications.

We also describe a feasibility study for a technique used to control the orientation of chromophores attached to DNA. Being able to control the orientation can expand the design space for RET networks because it provides another parameter to tune their collective behavior. While results showed limited control over orientation, the analysis required the development of a mathematical model that can be used to determine the distribution of dipoles in a given sample of chromophore constructs. The model can be used to evaluate the feasibility of other potential orientation control techniques.

A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.

Enzymatic Polymerization of High Molecular Weight DNA

The use of DNA as a polymeric building material transcends its function in biology and is exciting in bionanotechnology for applications ranging from biosensing, to diagnostics, and to targeted drug delivery. These applications are enabled by DNA’s unique structural and chemical properties, embodied as a directional polyanion that exhibits molecular recognition capabilities. Hence, the efficient and precise synthesis of high molecular weight DNA materials has become key to advance DNA bionanotechnology. Current synthesis methods largely rely on either solid phase chemical synthesis or template-dependent polymerase amplification. The inherent step-by-step fashion of solid phase synthesis limits the length of the resulting DNA to typically less than 150 nucleotides. In contrast, polymerase based enzymatic synthesis methods (e.g., polymerase chain reaction) are not limited by product length, but require a DNA template to guide the synthesis. Furthermore, advanced DNA bionanotechnology requires tailorable structural and self-assembly properties. Current synthesis methods, however, often involve multiple conjugating reactions and extensive purification steps.

The research described in this dissertation aims to develop a facile method to synthesize high molecular weight, single stranded DNA (or polynucleotide) with versatile functionalities. We exploit the ability of a template-independent DNA polymerase−terminal deoxynucleotidyl transferase (TdT) to catalyze the polymerization of 2’-deoxyribonucleoside 5’-triphosphates (dNTP, monomer) from the 3’-hydroxyl group of an oligodeoxyribonucleotide (initiator). We termed this enzymatic synthesis method: TdT catalyzed enzymatic polymerization, or TcEP.

Specifically, this dissertation is structured to address three specific research aims. With the objective to generate high molecular weight polynucleotides, Specific Aim 1 studies the reaction kinetics of TcEP by investigating the polymerization of 2’-deoxythymidine 5’-triphosphates (monomer) from the 3’-hydroxyl group of oligodeoxyribothymidine (initiator) using in situ 1H NMR and fluorescent gel electrophoresis. We found that TcEP kinetics follows the “living” chain-growth polycondensation mechanism, and like in “living” polymerizations, the molecular weight of the final product is determined by the starting molar ratio of monomer to initiator. The distribution of the molecular weight is crucially influenced by the molar ratio of initiator to TdT. We developed a reaction kinetics model that allows us to quantitatively describe the reaction and predict the molecular weight of the reaction products.

Specific Aim 2 further explores TcEP’s ability to transcend homo-polynucleotide synthesis by varying the choices of initiators and monomers. We investigated the effects of initiator length and sequence on TcEP, and found that the minimum length of an effective initiator should be 10 nucleotides and that the formation of secondary structures close to the 3’-hydroxyl group can impede the polymerization reaction. We also demonstrated TcEP’s capacity to incorporate a wide range of unnatural dNTPs into the growing chain, such as, hydrophobic fluorescent dNTP and fluoro modified dNTP. By harnessing the encoded nucleotide sequence of an initiator and the chemical diversity of monomers, TcEP enables us to introduce molecular recognition capabilities and chemical functionalities on the 5’-terminus and 3’-terminus, respectively.

Building on TcEP’s synthesis capacities, in Specific Aim 3 we invented a two-step strategy to synthesize diblock amphiphilic polynucleotides, in which the first, hydrophilic block serves as a macro-initiator for the growth of the second block, comprised of natural and/or unnatural nucleotides. By tuning the hydrophilic length, we synthesized the amphiphilic diblock polynucleotides that can self-assemble into micellar structures ranging from star-like to crew-cut morphologies. The observed self-assembly behaviors agree with predictions from dissipative particle dynamics simulations as well as scaling law for polyelectrolyte block copolymers.

In summary, we developed an enzymatic synthesis method (i.e., TcEP) that enables the facile synthesis of high molecular weight polynucleotides with low polydispersity. Although we can control the nucleotide sequence only to a limited extent, TcEP offers a method to integrate an oligodeoxyribonucleotide with specific sequence at the 5’-terminus and to incorporate functional groups along the growing chains simultaneously. Additionally, we used TcEP to synthesize amphiphilic polynucleotides that display self-assemble ability. We anticipate that our facile synthesis method will not only advance molecular biology, but also invigorate materials science and bionanotechnology.

Droplet etching of deep nanoholes for filling with self-aligned complex quantum structures

Strain-free epitaxial quantum dots (QDs) are fabricated by a combination of Al local droplet etching (LDE) of nanoholes in AlGaAs surfaces and subsequent hole filling with GaAs. The whole process is performed in a conventional molecular beam epitaxy (MBE) chamber. Autocorrelation measurements establish single-photon emission from LDE QDs with a very small correlation function g (2)(0)≃ 0.01 of the exciton emission. Here, we focus on the influence of the initial hole depth on the QD optical properties with the goal to create deep holes suited for filling with more complex nanostructures like quantum dot molecules (QDM). The depth of droplet etched nanoholes is controlled by the droplet material coverage and the process temperature, where a higher coverage or temperature yields deeper holes. The requirements of high quantum dot uniformity and narrow luminescence linewidth, which are often found in applications, set limits to the process temperature. At high temperatures, the hole depths become inhomogeneous and the linewidth rapidly increases beyond 640 °C. With the present process technique, we identify an upper limit of 40-nm hole depth if the linewidth has to remain below 100 μeV. Furthermore, we study the exciton fine-structure splitting which is increased from 4.6 μeV in 15-nm-deep to 7.9 μeV in 35-nm-deep holes. As an example for the functionalization of deep nanoholes, self-aligned vertically stacked GaAs QD pairs are fabricated by filling of holes with 35 nm depth. Exciton peaks from stacked dots show linewidths below 100 μeV which is close to that from single QDs.

Development of molecular-level switches and implementation into novel nanostructures and smart materials

The work presented in this thesis deals with the design, synthesis and investigation of (supra)molecular switches, and their implementation into novel nanostructures and smart devices. Part A deals with investigation of fundamental properties of Donor Acceptor Stenhouse Adducts (DASAs) as well as their implementation into polymer matrices in order to construct novel smart materials. Part B deals with the implementation of azobenzene photoswitches into pseudorotaxanes and the investigation of the effect of light-driven isomerization on the self-assembly and disassembly processes.

Percolation of colloids with distinct interaction sites

We generalize the Flory-Stockmayer theory of percolation to a model of associating (patchy) colloids, which consists of hard spherical particles, having on their surfaces f short-ranged-attractive sites of m different types. These sites can form bonds between particles and thus promote self-assembly. It is shown that the percolation threshold is given in terms of the eigenvalues of a m x m matrix, which describes the recursive relations for the number of bonded particles on the ith level of a cluster with no loops; percolation occurs when the largest of these eigenvalues equals unity. Expressions for the probability that a particle is not bonded to the giant cluster, for the average cluster size and the average size of a cluster to which a randomly chosen particle belongs, are also derived. Explicit results for these quantities are computed for the case f = 3 and m = 2. We show how these structural properties are related to the thermodynamics of the associating system by regarding bond formation as a (equilibrium) chemical reaction. This solution of the percolation problem, combined with Wertheim's thermodynamic first-order perturbation theory, allows the investigation of the interplay between phase behavior and cluster formation for general models of patchy colloids.

Patching up dipoles: can dipolar particles be viewed as patchy colloids?

We investigate whether the liquid-vapour phase transition of strongly dipolar fluids can be understood using a model of patchy colloids. These consist of hard spherical particles with three short-ranged attractive sites (patches) on their surfaces. Two of the patches are of type A and one is of type B. Patches A on a particle may bond either to a patch A or to a patch B on another particle. Formation of an AA (AB) bond lowers the energy by epsilon AA (epsilon AB). In the limit [image omitted], this patchy model exhibits condensation driven by AB-bonds (Y-junctions). Y-junctions are also present in low-density, strongly dipolar fluids, and have been conjectured to play a key role in determining their critical behaviour. We map the dipolar Yukawa hard-sphere (DYHS) fluid onto this 2A + 1B patchy model by requiring that the latter reproduce the correct DYHS critical point as a function of the isotropic interaction strength epsilon Y. This is achieved for sensible values of epsilon AB and the bond volumes. Results for the internal energy and the particle coordination number are in qualitative agreement with simulations of DYHSs. Finally, by taking the limit [image omitted], we arrive at a new estimate for the critical point of the dipolar hard-sphere fluid, which agrees with extrapolations from simulation.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Generalization of Wertheim's theory for the assembly of various types of rings

We generalize Wertheim's first order perturbation theory to account for the effect in the thermodynamics of the self-assembly of rings characterized by two energy scales. The theory is applied to a lattice model of patchy particles and tested against Monte Carlo simulations on a fcc lattice. These particles have 2 patches of type A and 10 patches of type B, which may form bonds AA or AB that decrease the energy by epsilon(AA) and by epsilon(AB) = r epsilon(AA), respectively. The angle theta between the 2 A-patches on each particle is fixed at 601, 90 degrees or 120 degrees. For values of r below 1/2 and above a threshold r(th)(theta) the models exhibit a phase diagram with two critical points. Both theory and simulation predict that rth increases when theta decreases. We show that the mechanism that prevents phase separation for models with decreasing values of theta is related to the formation of loops containing AB bonds. Moreover, we show that by including the free energy of B-rings ( loops containing one AB bond), the theory describes the trends observed in the simulation results, but that for the lowest values of theta, the theoretical description deteriorates due to the increasing number of loops containing more than one AB bond.