Molecular modelling of organic functional materials

The investigation of the mechanisms lying behind the (photo-)chemical processes is fundamental to address and improve the design of new organic functional materials. In many cases, dynamics simulations represent the only tool to capture the system properties emerging from complex interactions between many molecules. Despite the outstanding progresses in calculation power, the only way to carry out such computational studies is to introduce several approximations with respect to a fully quantum mechanical (QM) description. This thesis presents an approach that combines QM calculations with a classical Molecular Dynamics (MD) approach by means of accurate QM-derived force fields. It is based on a careful selection of the most relevant molecular degrees of freedom, whose potential energy surface is calculated at QM level and reproduced by the analytic functions of the force field, as well as by an accurate tuning of the approximations introduced in the model of the process to be simulated. This is made possible by some tools developed purposely, that allow to obtain and test the FF parameters through comparison with the QM frequencies and normal modes. These tools were applied in the modelling of three processes: the npi* photoisomerisation of azobenzene, where the FF description was extended to the excited state too and the non-adiabatic events were treated stochastically with Tully fewest switching algorithm; the charge separation in donors-acceptors bulk heterojunction organic solar cells, where a tight-binding Hamiltonian was carefully parametrised and solved by means of a code, also written specifically; the effect of the protonation state on the photoisomerisation quantum yield of the aryl-azoimidazolium unit of the axle molecule of a rotaxane molecular shuttle. In each case, the QM-based MD models that were specifically developed gave noteworthy information about the investigated phenomena, proving to be a fundamental key for a deeper comprehension of several experimental evidences.

Modelling the influence of diradical character and aggregation on conjugated molecular materials

Molecular materials are made by the assembly of specifically designed molecules to obtain bulk structures with desired solid-state properties, enabling the development of materials with tunable chemical and physical properties. These properties result from the interplay of intra-molecular constituents and weak intermolecular interactions. Thus, small changes in individual molecular and electronic structure can substantially change the properties of the material in bulk. The purpose of this dissertation is, thus, to discuss and to contribute to the structure-property relationships governing the electronic, optical and charge transport properties of organic molecular materials through theoretical and computational studies. In particular, the main focus is on the interplay of intra-molecular properties and inter-molecular interactions in organic molecular materials. In my three-years of research activity, I have focused on three major areas: 1) the investigation of isolated-molecule properties for the class of conjugated chromophores displaying diradical character which are building blocks for promising functional materials; 2) the determination of intra- and intermolecular parameters governing charge transport in molecular materials and, 3) the development and application of diabatization procedures for the analysis of exciton states in molecular aggregates. The properties of diradicaloids are extensively studied both regarding their ground state (diradical character, aromatic vs quinoidal structures, spin dynamics, etc.) and the low-lying singlet excited states including the elusive double-exciton state. The efficiency of charge transport, for specific classes of organic semiconductors (including diradicaloids), is investigated by combining the effects of intra-molecular reorganization energy, inter-molecular electronic coupling and crystal packing. Finally, protocols aimed at unravelling the nature of exciton states are introduced and applied to different molecular aggregates. The role of intermolecular interactions and charge transfer contributions in determining the exciton state character and in modulating the H- to J- aggregation is also highlighted.

NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

A take on complexity: bio-molecules and human metabolism interaction modelling for health and nutrition with machine learning

The advent of omic data production has opened many new perspectives in the quest for modelling complexity in biophysical systems. With the capability of characterizing a complex organism through the patterns of its molecular states, observed at different levels through various omics, a new paradigm of investigation is arising. In this thesis, we investigate the links between perturbations of the human organism, described as the ensemble of crosstalk of its molecular states, and health. Machine learning plays a key role within this picture, both in omic data analysis and model building. We propose and discuss different frameworks developed by the author using machine learning for data reduction, integration, projection on latent features, pattern analysis, classification and clustering of omic data, with a focus on 1H NMR metabolomic spectral data. The aim is to link different levels of omic observations of molecular states, from nanoscale to macroscale, to study perturbations such as diseases and diet interpreted as changes in molecular patterns. The first part of this work focuses on the fingerprinting of diseases, linking cellular and systemic metabolomics with genomic to asses and predict the downstream of perturbations all the way down to the enzymatic network. The second part is a set of frameworks and models, developed with 1H NMR metabolomic at its core, to study the exposure of the human organism to diet and food intake in its full complexity, from epidemiological data analysis to molecular characterization of food structure.