Chemical separation undoubtedly accounts for a large proportion of process industries' activities. In the past few decades, 10-15% of the world's energy consumed was resulted from separation process. Tremendous efforts have been made in separating the components of large quantities of chemical mixtures into pure or purer forms in most industrial chemists. In addition, industrial development and population growth would lead to a further increase in the global demand for energy in the future. This makes the effective and efficient energy separation process one of the most challenging tasks in engineering. Adsorptive separation using porous materials is widely used in industry today. In order for an adsorptive separation process to be efficient, the essential requirement is a selective adsorbent that possesses high surface area and preferentially adsorbs one component (or class of similar components). Metal-organic frameworks (MOFs) are promising materials for separation purposes as their diversity, due to their building block synthesis from metal clusters and organic linker, gives rise to a wide range of porous structures. Engineering of a separation process is a multi-disciplinary problem that requires a holistic approach. In particular, material selection for industrial applications in the field of MOFs is one of the most significant engineering challenges. The complexity of a screening exercise for adsorptive separations arises from the multitude of existing porous adsorbents including MOFs. There are more than 80,000 structures that have been synthesised so far, as well as the multivariate nature of that performance criteria that need to be considered when selecting or designing an optimal adsorbent for a separation process. However, it is infeasible to assess all the potential materials experimentally to identify the promising structure for a particular application. Recently, molecular simulation and mathematical modelling have seen an ever- growing contribution to the research field of MOFs. The development of these computational tools offers a unique platform for the characterisation, prediction and understanding of MOFs, complementary to experimental techniques. In the first part of this research, Monte Carlo molecular simulation and a number of advanced mathematical methods were used to investigate newly synthesised or not well-known MOFs. These computational techniques allowed not only to characterise materials with their textural properties, but also to predict and understand adsorption performances at the atomic level. Based on the insight gained from the molecular simulation, two computational high-throughput screening approaches were designed and assessed. A multi-scale approach has been proposed and used which combined high-throughput molecular simulation, data mining and advanced visualisation, process system modelling and experimental synthesis and testing. The focus here was on two main applications. On one hand, the challenging CO/N2 separation, which is critical for the petrochemical sector, where two molecules have very similar physical properties. On the other hand, the separation of chiral molecules. For CO/N2 separation, a database of 184 Cu- Cu paddle-wheels MOFs, which contains unsaturated metal centres as strong interaction sites, was extracted from CSD (Cambridge Structural Database) MOF subset for material screening. In the case of chiral separation, an efficient high-throughput approach based on calculation of Henry's constant was developed in this research. Owning to the nature of chirality, this separation of relevance to the pharmaceutical sector is crucially important. A database of 1407 homochiral MOFs was extracted, again, from CSD MOF subset for material screening of enantioselective adsorption. The results obtained in these computational high-throughput approaches allows the screening of interesting, existing structures, and would have a huge impact on making MOFs to be industrially interesting adsorbents as well as guiding the synthesis of these materials. From the many different possibilities, the ultimate interest of this work is in developing an integrated systematic study of the structure-adsorption performance relationship working with a limited library of candidate MOF structures in order to identify promising trends and materials for the specific applications mentioned above. In summary, the overall aim of this research was exploiting different computational techniques, developing novel high-throughput approaches in order to tackle important engineering challenges.

The objective of this work was to develop a molecular simulation method with the capacity to represent the synthesis of metal-organic framework (MOF) structures to the extent of being able to accurately predict the MOF structures that form under specified reaction conditions. MOFs are a class of porous, crystalline solids composed of metal-ion vertices coordinated by organic linker molecules. MOFs are created in a self-assembly process in which the building blocks (reactants) retain their integrity. Under different experimental synthesis conditions, a particular combination of building blocks can react to form differing MOF structures. The structure of MOFs confers a large degree of tunability, allowing almost limitless potential for the materials to be designed with the capacity to fulfil the requirements of a specific application. Consequently, MOFs have shown promise for a variety of applications including gas storage, separation and catalysis. Thus, the ability to accurately predict the MOF formed by specifying reaction parameters such as temperature, pH and the concentrations of reactants has great potential because, upon identification of a promising hypothetical structure for a particular application, the synthesis conditions ascertained via the simulation method could be used as the basis for the determination of an experimental synthesis procedure. In addition, a simulation method with the capacity to predict MOF structures affords the opportunity to gain a fundamental understanding of the influence of the experimental synthesis conditions on the structures formed, so as to enable progress towards the rational design of MOFs. In this work, the experimental synthesis of MOFs via self-assembly is modelled using a kinetic Monte Carlo approach. Ideally, simulation of the self-assembly of the building blocks would be modelled atomistically with all atoms in the reactant and solvent molecules represented explicitly. However, due to the prohibitive computational requirements of such a simulation, in this work a “potential-of-mean-force” (PMF) approach was used to represent the solvent implicitly by encompassing the solvent-mediated behaviour in the interactions between building blocks, thereby reducing the computational cost. The PMF approach to the implicit representation of the solvent involved the utilisation of effective pairwise interactions between the constituents of the reactant species. Following extensive testing to ensure that the explicit-solvent behaviour of the reactants could be replicated using the PMF method, this approach allowed computationally efficient implicit-solvent simulations of the synthesis of MOF materials to be performed. Thorough assessment of a method developed to simulate the synthesis of MOFs required investigation of a system which, under different reaction conditions, forms differing structures. In this respect, the cobalt succinates represent an unparalleled test because under different reaction temperatures, reactant concentrations, pH and reaction time, seven different phases have been identified. Furthermore, the parameters within which the different phases form have been clearly delineated experimentally. The method developed has been employed, under the appropriate reaction conditions, to simulate the synthesis of two of the seven identified phases of the cobalt succinates. Whilst still subject to computational limitations, the MOF-synthesis simulation method yields structures characteristic of those expected experimentally under corresponding reaction conditions.

Transition metals represent some of the first catalysts used in industrial processes and are still used today to produce many of the most needed chemicals. Adopting from ancient metallurgical techniques, it followed that the performance of these basic transition metals can be refined by adding multiple components. Since that time, improvements to these alloy catalysts has been mostly incremental due to the difficulty of producing new catalysts experimentally and a lack of fundamental understanding of the underlying physics. More recently, computational chemistry has proven itself an increasingly effective means for identifying these underlying physics. Through the use of d-band interactions of adsorbates with the surface, basic adsorption characteristics can be predicted across transition metals with limited initial information. However, although these models function well as high-level screening tools, much work is yet to be done before optimal catalysts can be comfortably designed from properties which experimentalists can directly control. This remains particularly challenging for alloy modeling, primarily due to the large number of possible atomic configurations, even for two metal systems. This work focuses on developing the methods for modeling optimal reaction properties at the surface of a transition metal alloy. Based on thermodynamic equilibrium between the surface, bulk, and gas reservoir, a model for the prediction of segregation under vacuum and adsorbate conditions can be predicted. Furthermore, by relating strain in the bulk lattice constant to the adsorption energies of varying local active sites, the optimal surface compositions can be related to bulk composition; a feature which can easily be selected for. Although useful for identifying trends across bulk composition space, these methods are limited to a small subset of active site configurations. To capture the complexity of more sophisticated processes, such as segregation, higher-timescale methods are required. Traditional computational tools are often too expensive to implement for these methods, and as such, they are usually completed with less-accurate potentials. In this work, we demonstrate that machine learning techniques have improved accuracy compared to physical potentials. We then go on to demonstrate how this improved accuracy can lead to experimentally accurate predictions of segregation.

The behavior of dense colloidal fluids near surfaces can now be probed in great detail with experimental techniques like video and confocal microscopy. In fact we are approaching a point where quantitative comparisons of experiments with particle-level theory, such as classical density functional theory (DFT), are appropriate. In a forward sense, we may use a known surface potential to predict a particle density distribution function from DFT; in an inverse sense, we may use an experimentally measured particle density distribution function to predict the underlying surface potential from DFT. In this dissertation, we tested the ability of the closure-based DFT to perform forward and inverse calculations on potential models commonly employed for colloidal particles and surface under different surface topographies. To reduce sources of uncertainty in this initial study, Monte Carlo simulation results played the role of experimental data. The accuracy of the predictions depended on the bulk particle density, potential well depth and the choice of DFT closure relationships. For a reasonable range of choices of the density, temperature, potential parameters, and surface features, the inversion procedure yielded particle-surface potentials to an accuracy on the order of 0.1 kBT. Our results demonstrated that DFT is a valuable numerical tool for microcopy experiments to image three-dimensional surface energetic landscape accurately and rapidly. B

By means of molecular simulation, we propose possible packing models for £\ and £\¡¬ phases in poly(9,9-di-n-hexyl-2,7-fluorene) (PFH). Simulated multi-chain unit cell structures are compared with experimental diffraction patterns of PFH where the unit cell structure (and the space group) of the high-temperature £\ crystals was identified as monoclinic (C2) and that of £\¡¬ phase (kinetically favored upon programmed cooling) triclinic (P1). Results show that £\ phase prefers to adopt bi-radial side-chain conformation whereas the £\¡¬ phase prefers tetra-radial one. Both models exhibit embracing behavior between adjacent chains in spite of differences in inter-chain distance. A group of embracing chains aligned along b-axis in £\ phase and the comparatively greater inter-chain distance in £\¡¬ phase are consistent with the observed faceting along (100) planes and the tensile cracking along the a-axis. A qualitative analysis of co-existing £\ and £\¡¬ phases reproduce the [001] SAED pattern quite well. In addition, we also show that arbitrary alternation of 40o and 140o in dihedral angle between neighboring monomers generates equally stable single-chain conformations in this case of linear alkyl side-chains.

Atomistic modeling and simulation methods facilitate biomedical research from many respects, including structure-based drug design. The ability of these methods to address biologically relevant problems is largely determined by the accuracy of the treatment of complex solvation effects in target biomolecules surrounded by water. The implicit solvent model – which treats solvent as a continuum with the dielectric and non-polar properties of water – offers a good balance between accuracy and speed. Simple and efficient, generalized Born (GB) model has become a widely used implicit solvent responsible for the estimation of key electrostatic interactions. The main goal of this research is to improve the accuracy of protein-ligand binding calculations in the implicit solvent framework. To address the problem (1) GBNSR6, an accurate yet efficient flavor of GB, has been thoroughly explored in the context of protein-ligand binding, (2) a global multidimensional optimization pipeline is developed to find the optimal dielectric boundary made of atomic and water probe radii specifically for protein-ligand binding calculations using GBNSR6. The pipeline includes (3) two novel post-processing steps for optimum robustness analysis and optimization landscape visualization. In the final step of this research, (4) accuracy gain the optimal dielectric boundary can bring in practice is explored on binding benchmarks, including the SARS-CoV-2 spike receptor-binding domain and the human ACE2 receptor. / Doctor of Philosophy / Drug discovery is one of the most challenging tasks in biological sciences as it takes about 10-15 years and $1.5-2 billion on average to discover a new drug. Therefore, efforts to speed up this process or lower its costs are highly valuable. Computer-aided drug design (CADD) plays a crucial role in the early stage of drug discovery. In CADD, computational approaches are used in order to discover, develop, and analyze drugs and similar biologically active molecules, such as proteins. Proteins are an important class of biological macromolecules that perform their functionality mainly through interactions with other molecules, for example, binding to small molecules so-called ligands. Thorough understanding of protein-ligand interactions is central to comprehending biology at the molecular level. In this study, we introduce and analyze a computational model used for protein-ligand binding free energy calculations. A global multidimensional optimization pipeline is developed to find the optimal parameters of the model,˘aparticularly˘athose parameters involved in the dielectric boundary. In order to examine the robustness of the optimal model to unavoidable perturbations and uncertainties, virtually inevitable in any complex system being optimized, a novel robustness metric is introduced. Finally, the robust optimal model is tested on protein-ligand benchmarks, including a complex related to the novel coronavirus. Results demonstrate relatively higher accuracy in terms of binding free energy calculations compared to reference models.

No description available.

Yes / The removal or structural disruption of crystallised lipid is a pivotal but energy-intensive step in a wide range of industrial and biological processes. Strategies to disrupt the structure of crystallised lipid in aqueous solution at lower temperatures are much needed, where nanoparticle-based strategies show enormous promise. Using the aqueous tristearin bilayer as a model for crystallised lipid, we demonstrate that the synergistic use of surfactant and detonation nanodiamonds can depress the onset temperature at which disruption of the crystallised lipid structure occurs. Our simulations reveal the molecular-scale mechanisms by which this disruption takes place, indicating that the nanodiamonds serve a dual purpose. First, the nanodiamonds are predicted to facilitate delivery of surfactant to the lipid/water interface, and second, nanodiamond adsorption acts to roughen the lipid/water interface, enhancing ingress of surfactant into the bilayer. We find the balance of the hydrophobic surface area of the nanodiamond and the nanodiamond surface charge density to be a key determinant of the effectiveness of using nanodiamonds to facilitate lipid disruption. For the nanodiamond size considered here, we identify a moderate surface charge density, that ensures the nanodiamonds are neither too hydrophobic nor too hydrophilic, to be optimal.

No / We report the structure of the electrical double layer, determined from molecular dynamics simulations, for a range of saline solutions (NaCl, KCl, MgCl2 and CaCl2) at both 0.16 and 0.60 mol kg(-1) on different facets of the gold and silver aqueous interfaces. We consider the Au/Ag(111), native Au/Ag(100) and reconstructed Au(100)(5×1) facets. For a given combination of metallic surface and facet, some variations in density profile are apparent across the different cations in solution, with the corresponding chloride counterion profiles remaining broadly invariant. All density profiles at the higher concentration are predicted to be very similar to their low-concentration counterparts. We find that each electrolyte responds differently to the different metallic surface and facets, particularly those of the divalent metal ions. Our findings reveal marked differences in density profiles between facets for a given metallic interface for both Mg(2+) and Ca(2+), with Na(+) and K(+) showing much less distinction. Mg(2+) was the only ion for which we find evidence of materials-dependent differences in interfacial solution structuring between the Ag and Au. / Veski, Air Force Office for Scientific Research grant #FA9550-12-1-0226

No / Controllable 3D assembly of multicomponent inorganic nanomaterials by precisely positioning two or more types of nanoparticles to modulate their interactions and achieve multifunctionality remains a major challenge. The diverse chemical and structural features of biomolecules can generate the compositionally specific organic/inorganic interactions needed to create such assemblies. Toward this aim, we studied the materials-specific binding of peptides selected based upon affinity for Ag (AgBP1 and AgBP2) and Au (AuBP1 and AuBP2) surfaces, combining experimental binding measurements, advanced molecular simulation, and nanomaterial synthesis. This reveals, for the first time, different modes of binding on the chemically similar Au and Ag surfaces. Molecular simulations showed flatter configurations on Au and a greater variety of 3D adsorbed conformations on Ag, reflecting primarily enthalpically driven binding on Au and entropically driven binding on Ag. This may arise from differences in the interfacial solvent structure. On Au, direct interaction of peptide residues with the metal surface is dominant, while on Ag, solvent-mediated interactions are more important. Experimentally, AgBP1 is found to be selective for Ag over Au, while the other sequences have strong and comparable affinities for both surfaces, despite differences in binding modes. Finally, we show for the first time the impact of these differences on peptide mediated synthesis of nanoparticles, leading to significant variation in particle morphology, size, and aggregation state. Because the degree of contact with the metal surface affects the peptide’s ability to cap the nanoparticles and thereby control growth and aggregation, the peptides with the least direct contact (AgBP1 and AgBP2 on Ag) produced relatively polydispersed and aggregated nanoparticles. Overall, we show that thermodynamically different binding modes at metallic interfaces can enable selective binding on very similar inorganic surfaces and can provide control over nanoparticle nucleation and growth. This supports the promise of bionanocombinatoric approaches that rely upon materials recognition. / Air Office of Scientific Research grant number FA9550-12-1-0226