Towards Generalized Noise-Level Dependent Crystallographic Symmetry Classifications of More or Less Periodic Crystal Patterns

Geometric Akaike Information Criteria (G-AICs) for generalized noise-level dependent crystallographic symmetry classifications of two-dimensional (2D) images that are more or less periodic in either two or one dimensions as well as Akaike weights for multi-model inferences and predictions are reviewed. Such novel classifications do not refer to a single crystallographic symmetry class exclusively in a qualitative and definitive way. Instead, they are quantitative, spread over a range of crystallographic symmetry classes, and provide opportunities for inferences from all classes (within the range) simultaneously. The novel classifications are based on information theory and depend only on information that has been extracted from the images themselves by means of maximal likelihood approaches so that these classifications are objective. This is in stark contrast to the common practice whereby arbitrarily set thresholds or null hypothesis tests are employed to force crystallographic symmetry classifications into apparently definitive/exclusive states, while the geometric feature extraction results on which they depend are never definitive in the presence of generalized noise, i.e., in all real-world applications. Thus, there is unnecessary subjectivity in the currently practiced ways of making crystallographic symmetry classifications, which can be overcome by the approach outlined in this review

Objective Distinctions Between Genuine Plane Symmetries and Pseudosymmetries in Crystal Patterns of Graphic Artwork

A recently developed method for the objective identification of the plane symmetry group of a noisy crystal pattern is briefly described and subsequently applied to two pieces of graphic art. Pseudo-symmetries do not distract from the beauty of graphic art but add to it. They are here distinguished from the genuine symmetries that combine to form the best-fitting plane symmetry group. The gray-value deviations of the individual pixel values of graphic artworks from their perfectly symmetric abstractions are considered to be chiefly due to the handiwork and employed creative procedures of an individual artists. As different graphic techniques/procedures were employed in the creation of the here classified crystal patterns, one may glean insights on how well a particular technique or procedure supports the realization of an intended crystallographic symmetry group in a graphic work of art.Comment: 8 pages, 3 figure

Phase Transitions in Nanoscale Designed Magnetic Thin Films

187 p.El fenómeno de las transiciones de fase termodinámicas (TPT, por sus siglas en inglés) en la materia, asociadas con un cambio abrupto en cierta cantidad física, son de una importancia fundamental, tanto en su comprensión teórica como en aplicaciones tecnológicas. Algunos ejemplos significativos de aplicaciones de las TPT incluyen la transición de fase en materiales superconductores, usados, por ejemplo, en la reducción del consumo energético en equipos de resonancia magnética, o los materiales de cambio de fase, empleados en la refrigeración de ordenadores o en almacenamiento térmico de energía.Los materiales ferromagnéticos (FM) son también un tipo de sistemas en los que pueden observarse TPTs. En estos materiales, un tipo de TPT está determinado por una temperatura especifica, llamada temperatura de Curie TC, por debajo de la cual el sistema exhibe una fase FM ordenada. A temperaturas inferiores a TC, el material presenta dos estados equivalentes en ausencia de campo magnético externo, también separados por otro tipo de TPT. Dichas TPT en materiales FM y sus estados magnéticos asociados son relevantes en aplicaciones tecnológicas ampliamente extendidas, como la refrigeración o las memorias magnéticas.En general, las tecnologías basadas en películas delgadas FM han considerado históricamente interfases abruptas entre capas principalmente. Dichos cambios abruptos inducen y/o amplifican efectos necesarios, por ejemplo, para la lectura y escritura de sus estados magnéticos. Sin embargo, cambiosgraduales en las propiedades físicas de las películas delgadas pueden mejorar su rendimiento, en determinadas circunstancias.En este contexto, es bien sabido que cambios graduales en la interacción de intercambio en películas delgadas FM influyen en el fenómeno de la TPT. En estas películas, pueden coexistir diferentes fases quasi-paramagneticas (PM)/FM. No solo eso, dicha separación de fases puede ser controlada con la temperatura, mediante el diseño del perfil de energía de intercambio en la película. Así, películas delgadas FM con intercambio-gradual, son una herramienta novedosa con potenciales aplicaciones tecnológicas mediante el diseño de dichos perfiles.Paralelamente, el fenómeno de la TPT está asociado con el equilibrio termodinámico, en el que todas las cantidades permanecen constantes en el tiempo. Sin embargo, las transiciones de fase pueden ocurrir en sistemas que se encuentran lejos del equilibrio termodinámico en presencia de una fuerza dependiente del tiempo. De hecho, las llamadas transiciones dinámicas de fase (DPT), son bien conocidas en materiales FM. Sin embargo, su verificación experimental sólo ha sido posible recientemente, mediante experimentos diseñados exprofeso. Comprender las DPTs es crucial en la física del no-equilibrio termodinámico, debido a sus similitudes con respecto a las TPT. Dichas similitudes permitirían usar metodologías originalmente concebidas para las TPT en sistemas dinámicos.En esta tesis, se llevan a cabo una serie de investigaciones relacionadas con la fenomenología de las transiciones de fase en películas delgadas FM diseñadas en la nanoescala. Más concretamente, se investigan aspectos relevantes de los dos fenómenos previamente mencionados, es decir, las transiciones de fase en películas delgadas con interacción de intercambio graduada, y transiciones dinámicas de fase en películas delgadas F

EXPERIMENTAL AND COMPUTATIONAL ANALYSIS OF RELATIVE ENERGETIC STABILITIES OF CRYSTALLINE ANHYDROUS POLYMORPHS AND PSEUDOPOLYMORPHS

The stability of pharmaceutical solids is impacted by the properties of both active and inactive ingredients. Given that the aqueous solubility of solid-state medicinal products can be directly linked to the component properties, it is prudent to carefully study these materials to predict bioavailability and shelf stability. The relative energetic stabilities of the molecular crystals of interest are governed by both the intermolecular forces and the molecular conformations within the structure. In this research, the electronic origins of crystalline stability were investigated using a combination of solid-state density functional theory (ss-DFT) and terahertz time-domain spectroscopy (THz-TDS). Terahertz spectroscopy of the lattice vibrations offers a sensitive probe of solid-state interactions and serves as a rigorous benchmark for testing the quality of the applied theoretical methods. Vibrational simulations of different polymorphic forms are also useful for investigating the relative thermodynamic stabilities of these structures. Through the calculation of Gibbs free energy versus temperature trends, it was possible to not only identify enantiotropic or monotropic relationships between polymorphs, but also the precise transition temperature linking enantiotropic pairs. These combined experimental and computational methods were extended to analyzing the relative stabilities of not only pure solids, but also cocrystals. The successful use of DFT for identifying relative stabilities of known crystal structures led to its use for crystal structure prediction. Overall, this work has demonstrated the extensive applicability of ss-DFT in the analysis of electronic and thermodynamic relationships within polymorphic and pseudopolymorhic systems. Application of this methodology to pharmaceutical solids has provided new insights into the most important contributors to the stabilities of these materials

Artificial Intelligence in Materials Science: Applications of Machine Learning to Extraction of Physically Meaningful Information from Atomic Resolution Microscopy Imaging

Materials science is the cornerstone for technological development of the modern world that has been largely shaped by the advances in fabrication of semiconductor materials and devices. However, the Moore’s Law is expected to stop by 2025 due to reaching the limits of traditional transistor scaling. However, the classical approach has shown to be unable to keep up with the needs of materials manufacturing, requiring more than 20 years to move a material from discovery to market. To adapt materials fabrication to the needs of the 21st century, it is necessary to develop methods for much faster processing of experimental data and connecting the results to theory, with feedback flow in both directions. However, state-of-the-art analysis remains selective and manual, prone to human error and unable to handle large quantities of data generated by modern equipment. Recent advances in scanning transmission electron and scanning tunneling microscopies have allowed imaging and manipulation of materials on the atomic level, and these capabilities require development of automated, robust, reproducible methods.Artificial intelligence and machine learning have dealt with similar issues in applications to image and speech recognition, autonomous vehicles, and other projects that are beginning to change the world around us. However, materials science faces significant challenges preventing direct application of the such models without taking physical constraints and domain expertise into account.Atomic resolution imaging can generate data that can lead to better understanding of materials and their properties through using artificial intelligence methods. Machine learning, in particular combinations of deep learning and probabilistic modeling, can learn to recognize physical features in imaging, making this process automated and speeding up characterization. By incorporating the knowledge from theory and simulations with such frameworks, it is possible to create the foundation for the automated atomic scale manufacturing

Quantitative electron microscopy for microstructural characterisation

Development of materials for high-performance applications requires accurate and useful analysis tools. In parallel with advances in electron microscopy hardware, we require analysis approaches to better understand microstructural behaviour. Such improvements in characterisation capability permit informed alloy design. New approaches to the characterisation of metallic materials are presented, primarily using signals collected from electron microscopy experiments. Electron backscatter diffraction is regularly used to investigate crystallography in the scanning electron microscope, and combined with energy-dispersive X-ray spectroscopy to simultaneusly investigate chemistry. New algorithms and analysis pipelines are developed to permit accurate and routine microstructural evaluation, leveraging a variety of machine learning approaches. This thesis investigates the structure and behaviour of Co/Ni-base superalloys, derived from V208C. Use of the presently developed techniques permits informed development of a new generation of advanced gas turbine engine materials.Open Acces