Ab initio simulations of α- and β-ammonium carbamate (NH₄·NH₂CO₂), and the thermal expansivity of deuterated α-ammonium carbamate from 4.2 to 180 K by neutron powder diffraction

Experimental and computational studies of ammonium carbamate have been carried out, with the objective of studying the elastic anisotropy of the framework manifested in (i) the thermal expansion and (ii) the compressibility; furthermore, the relative thermodynamic stability of the two known polymorphs has been evaluated computationally. Using high-resolution neutron powder diffraction data, the crystal structure of α-ammonium carbamate (ND4·ND2CO2) has been refined [space group Pbca, Z = 8, with a = 17.05189 (15), b = 6.43531 (7), c = 6.68093 (7) Å and V = 733.126 (9) Å^{3} at 4.2 K] and the thermal expansivity of α-ammonium carbamate has been measured over the temperature range 4.2-180 K. The expansivity shows a high degree of anisotropy, with the b axis most expandable. The ab initio computational studies were carried out on the α- and β-polymorphs of ammonium carbamate using density functional theory. Fitting equations of state to the P(V) points of the simulations (run athermally) gave the following values: V0 = 744 (2) Å^{3} and bulk modulus K0 = 16.5 (4) GPa for the α-polymorph, and V0 = 713.6 (5) Å^{3} and K0 = 24.4 (4) GPa for the β-polymorph. The simulations show good agreement with the thermoelastic behaviour of α-ammonium carbamate. Both phases show a high-degree of anisotropy; in particular, α-ammonium carbamate shows unusual compressive behaviour, being determined to have negative linear compressibility (NLC) along its a axis above 5 GPa. The thermodynamically stable phase at ambient pressure is the α-polymorph, with a calculated enthalpy difference with respect to the β-polymorph of 0.399 kJ mol^{-1}; a transition to the β-polymorph could occur at ∼0.4 GPa

Computational prediction of organic crystal structures

Crystal Structure Prediction (CSP) is used by the pharmaceutical industry to assess whether other polymorphs of active pharmaceutical ingredients (API) might cause problems during manufacturing processes. In the 7th Blind Test of CSP, organized by the Cambridge Crystallographic Data Centre (CCDC), one of the targets (XXX) was to predict the possible stoichiometries of two co−crystals of cannabinol (CBN) and tetramethylpyrazine (TMP). This thesis describes the methodology used for the submission of predicted structures of these co−crystals, concluding that the likely stoichiometries were 1CBN:1TMP and 1CBN:2TMP, as these were more stable than the component structures and had plausible crystal packings. Following submission, this thesis analysed the crystal structures of TMP and have proposed starting points for the crystal structure refinement of a structure (MPYRAZ03) on the Cambridge Structural Database (CSD) that has no atomic coordinates. The CBN search failed to find the Z’=2 experimental crystal structure (CANNOL) that is on the CSD, which has a high energy molecular conformation. This failure was found to be due to the limits on the structure generation program (Sobol sequence and density setting) and was exacerbated by the point charge model failing to model the CANNOL hydrogen bonding adequately. Alternative strategies to find the experimental structure were proposed, but they were deemed too expensive to run a full search. As this thesis was being completed, experimental co−crystal structures were provided by CCDC. After comparing with experimental structures, there was no experimental co−crystal structure in co−crystal CSP searches used in this thesis. This problem was caused by the folded pentane tail instead of the hydroxyl group

An Electromagnetics Water Flooding System With Nanofluid For EOR

The major challenge for oil industry is to increase the recovery of oil from the reservoir. EOR by nanofluids induction has been used in water flooding process. This work deals with a new electromagnetics water flooding system using nanofluid for EOR. A simulation on the density of state (DOS) and band structure of zinc oxide (ZnO) and iron oxide (Fe2O3) was carried out; it was observed that the band gap value for ZnO is 0.808ev and for Fe2O3 is 0.201ev. The percentage difference between the band gap of ZnO and Fe2O3 is 301%. For ZnO, Zn 4s state contributes to conduction band and O 2p state contributes to valence band. For Fe2O3 valence band is a mixture of O 2p state and the majority is Fe 3d state, while the conduction band consists of Fe 3d state. As Fe2O3 has lowest band gap, its dielectric constant is greater than ZnO which has the highest band gap, thus it has the lowest dielectric constan

Solid-State Nuclear Magnetic Resonance Spectroscopy of Unreceptive Quadrupolar Nuclei in Inorganic Materials

Preparation and characterization of inorganic materials is a crucial practice because understanding the relationship between structure and property is important for improving current performance and developing novel materials. Many metal centers in technologically and industrially important materials are unreceptive low-γ quadrupolar nuclei (i.e., possessing low natural abundance, low NMR frequencies and large quadrupole moments) and they usually give rise to very broad NMR resonances and low signal-to-noise ratios, making it difficult to acquire their solid-state NMR spectra. This thesis focuses on the characterization of inorganic materials using solid-state NMR (SSNMR) spectroscopy at very high magnetic field of 21.1 T in combination with quantum chemical calculations for computational modeling. In the first part of this thesis, 67Zn and 17O SSNMR studies of several microporous materials were reported. The results of 67Zn SSNMR studies from several important metal-organic frameworks (MOFs), in particular, zeolitic imidazolate frameworks (ZIFs) were presented. 67Zn SSNMR spectroscopy was used to gain structural information regarding the desolvation process in MOF-5. Furthermore, 67Zn SSNMR spectroscopy were utilized to study the host-guest interactions in ZIF-8 loaded with different guest molecules. Static 67Zn SSNMR spectra of microporous zinc phosphites (ZnP) and zinc phosphates (ZnPO) were also acquired at natural abundance. The Gaussian calculation results on a model cluster for ZnP indicate that Zn–O bond length is the most dominant factor to the observed quadrupolar coupling constant (CQ) among other geometric parameters around Zn centres. The local structures of the framework oxygen sites in molecular sieve SAPO-34 were directly probed by several 17O SSNMR techniques. The involvement of water vapor during the SAPO-34 formation in dry-gel conversion (DGC) synthesis was also investigated. In the second part, 91Zr and 33S SSNMR spectra of layered zirconium phosphates (ZrP) and transition metal disulfides (MS2) were obtained. The empirical correlations between NMR parameters and various structural parameters were used for obtaining partial structural information in Li+ and Co(NH3)63+ exchanged layered ZrP. For a series of closely related MS2 materials, the observed differences in the CQ(33S) values were rationalized by considering the difference in their geometrical arrangements. The final part of this thesis featured two examples of SSNMR spectroscopy of “exotic” nuclei in some interesting inorganic materials. (i) The experimental 135/137Ba SSNMR spectroscopy and theoretical studies of β-BBO, an important non-linear optical (NLO) material, indicate that the true crystal structure of β-BBO is R3c space group rather than R3. (ii) An ultrahigh field natural abundance 73Ge SSNMR study of two representative germanium containing materials [GeCl2•dioxane and GePh4] demonstrated that acquiring 73Ge wideline NMR spectra of germanium compounds where the Ge experiences an extremely large quadrupolar interaction is feasible and that the small 73Ge chemical shielding anisotropy (CSA) can be directly measured

Adaptive heterogeneous parallelism for semi-empirical lattice dynamics in computational materials science.

With the variability in performance of the multitude of parallel environments available today, the conceptual overhead created by the need to anticipate runtime information to make design-time decisions has become overwhelming. Performance-critical applications and libraries carry implicit assumptions based on incidental metrics that are not portable to emerging computational platforms or even alternative contemporary architectures. Furthermore, the significance of runtime concerns such as makespan, energy efficiency and fault tolerance depends on the situational context. This thesis presents a case study in the application of both Mattsons prescriptive pattern-oriented approach and the more principled structured parallelism formalism to the computational simulation of inelastic neutron scattering spectra on hybrid CPU/GPU platforms. The original ad hoc implementation as well as new patternbased and structured implementations are evaluated for relative performance and scalability. Two new structural abstractions are introduced to facilitate adaptation by lazy optimisation and runtime feedback. A deferred-choice abstraction represents a unified space of alternative structural program variants, allowing static adaptation through model-specific exhaustive calibration with regards to the extrafunctional concerns of runtime, average instantaneous power and total energy usage. Instrumented queues serve as mechanism for structural composition and provide a representation of extrafunctional state that allows realisation of a market-based decentralised coordination heuristic for competitive resource allocation and the Lyapunov drift algorithm for cooperative scheduling

Ab initio studies of the structural, dynamical and thermodynamical properties of graphitic and hydrogenated graphitic materials and their potential for hydrogen storage

The study presented in this PhD thesis is related to exploration of the properties of graphitic materials within the frame-work of ab initio methods. Structural and dynamical properties of graphitic materials are evaluated using the ab initio pseudopotential method. In graphitic materials, properties are obtained by incorporating Van der Waals interactions together with the generalized gradient approximation to density functional theory. These Van der Waals interactions improve the structural and dynamics of graphitic systems.In order to study the dynamical properties, the finite displacement method has been used to construct the dynamical matrix and force constant matrix. Phonon dispersions are investigated by the direct force constant matrix method in supercells.In this approach, force constants are assumed to be zero beyond a certain limit. Phonon frequencies are calculated from the force constant matrix. The dispersion relations and the Brillouin zone integrated density of states are also investigated.The significance of phonon dispersion has been studied to in various regions. Results are compared with dispersion corrected scheme and without dispersion corrected schemes to understand the importance of dispersion correction. Conclusions are also drawn on the applicability of theoretical approximations used.Further, ab initio results are also compared with the available data from experimentalstudies. The binding energies and electronic band gaps of exo-hydrogenated carbon nanotubes are determined to investigate the stability and band gap opening using density functional theory. The vibrational density of states for hydrogenated carbon nanotubes has been calculated to confirm the C-H stretching mode due to sp3hybridization. The thermodynamical stability of hydrogenated carbon nanotubes has been explored in the chemisorption limit. Statistical physics and density functional theory calculations have been used to predict hydrogen release temperatures at standard pressure in zigzag and armchair carbon nanotubes

Atomistic modelling of precipitation in Ni-base superalloys

The presence of the ordered $\gamma^{\prime}$ phase ($\text{Ni}_{3}\text{Al}$) in Ni-base superalloys is fundamental to the performance of engineering components such as turbine disks and blades which operate at high temperatures and loads. Hence for these alloys it is important to optimize their microstructure and phase composition. This is typically done by varying their chemistry and heat treatment to achieve an appropriate balance between $\gamma^{\prime}$ content and other constituents such as carbides, borides, oxides and topologically close packed phases. In this work we have set out to investigate the onset of $\gamma^{\prime}$ ordering in Ni-Al single crystals and in Ni-Al bicrystals containing coincidence site lattice grain boundaries (GBs) and we do this at high temperatures, which are representative of typical heat treatment schedules including quenching and annealing. For this we use the atomistic simulation methods of molecular dynamics (MD) and density functional theory (DFT). In the first part of this work we develop robust Bayesian classifiers to identify the $\gamma^{\prime}$ phase in large scale simulation boxes at high temperatures around 1500 K. We observe significant \gamma^{\prime} ordering in the simulations in the form of clusters of $\gamma^{\prime}$-like ordered atoms embedded in a $\gamma$ host solid solution and this happens within 100 ns. Single crystals are found to exhibit the expected homogeneous ordering with slight indications of chemical composition change and a positive correlation between the Al concentration and the concentration of $\gamma^{\prime}$ phase. In general, the ordering is found to take place faster in systems with GBs and preferentially adjacent to the GBs. The sole exception to this is the $\Sigma3 \left(111\right)$ tilt GB, which is a coherent twin. An analysis of the ensemble and time lag average displacements of the GBs reveals mostly `anomalous diffusion' behaviour. Increasing the Al content from pure Ni to Ni 20 at.% Al was found to either consistently increase or decrease the mobility of the GB as seen from the changing slope of the time lag displacement average. The movement of the GB can then be characterized as either `super' or `sub-diffusive' and is interpreted in terms of diffusion induced grain boundary migration, which is posited as a possible precursor to the appearance of serrated edge grain boundaries. In the second part of this work we develop a method for the training of empirical interatomic potentials to capture more elements in the alloy system. We focus on the embedded atom method (EAM) and use the Ni-Al system as a test case. Recently, empirical potentials have been developed based on results from DFT which utilize energies and forces, but neglect the electron densities, which are also available. Noting the importance of electron densities, we propose a route to include them into the training of EAM-type potentials via Bayesian linear regression. Electron density models obtained for structures with a range of bonding types are shown to accurately reproduce the electron densities from DFT. Also, the resulting empirical potentials accurately reproduce DFT energies and forces of all the phases considered within the Ni-Al system. Properties not included in the training process, such as stacking fault energies, are sometimes not reproduced with the desired accuracy and the reasons for this are discussed. General regression issues, known to the machine learning community, are identified as the main difficulty facing further development of empirical potentials using this approach.EPSRC, Rolls-Royc

Novel methods to predict solid-state material properties

Solid-state materials find ubiquitous use in modern technology - from semiconductors in electronics to steel in buildings and superconductors in MRI machines. Theoretical understanding of the atomic-scale behaviour of these materials can be leveraged to design new materials with desirable properties. In this thesis, we investigate the challenges that arise when this is attempted in practice. Accurate and inexpensive methods to tackle the atomic-scale problem are a prerequisite for materials discovery. We begin with a description of existing methods. This is followed by the development of a Monte Carlo method to calculate expectation values from the many-body picture without the need for a trial wavefunction, which is both a fundamental, and practical, limitation in existing techniques. Having explored first-principles methods, we turn to their use in understanding materials, beginning with an investigation of the structure of Lithium. Structure searching calculations result in a mixed-phase model at low temperatures, in good agreement with previous experimental and theoretical results. The quasi-harmonic treatment of finite-temperature thermodynamics is extended to include anharmonic nuclear vibrations, which are shown to not alter the phase diagram despite the small mass of the Li atoms. Focus then shifts towards leveraging these same methods to discover novel superconductors. This begins with an investigation of the LaH$_{10}$ and YH$_{10}$ compounds, where a new hexagonal phase of LaH$_{10}$ provides an explanation for recent experimental measurements. Machine-learning techniques and novel screening methods are then employed to discover hydrides of Rb and Cs that exhibit superconductivity at significantly lower pressures than LaH$_{10}$. Optimizations to, and automation of, the workflow then enables the discovery of superconductors on an unprecedented scale, leading to hundreds of new high-temperature superconductors. Throughout the thesis, the importance of structures that are saddle-points of the energy landscape becomes apparent. The thesis closes with the development of a new algorithm to locate saddle-points that requires no additional information beyond that used by the cheapest existing methods. This thesis demonstrates that there is progress to be made at every stage of the first-principles materials discovery process and highlights that improving the workflow itself is a non-trivial, but fruitful, pursuit