Computer-Aided Geometry Modeling

Techniques in computer-aided geometry modeling and their application are addressed. Mathematical modeling, solid geometry models, management of geometric data, development of geometry standards, and interactive and graphic procedures are discussed. The applications include aeronautical and aerospace structures design, fluid flow modeling, and gas turbine design

Parallel implementation of a virtual reality system on a transputer architecture

A Virtual Reality is a computer model of an environment, actual or imagined, presented to a user in as realistic a fashion as possible. Stereo goggles may be used to provide the user with a view of the modelled environment from within the environment, while a data-glove is used to interact with the environment. To simulate reality on a computer, the machine has to produce realistic images rapidly. Such a requirement usually necessitates expensive equipment. This thesis presents an implementation of a virtual reality system on a transputer architecture. The system is general, and is intended to provide support for the development of various virtual environments. The three main components of the system are the output device drivers, the input device drivers, and the virtual world kernel. This last component is responsible for the simulation of the virtual world. The rendering system is described in detail. Various methods for implementing the components of the graphics pipeline are discussed. These are then generalised to make use of the facilities provided by the transputer processor for parallel processing. A number of different decomposition techniques are implemented and compared. The emphasis in this section is on the speed at which the world can be rendered, and the interaction latency involved. In the best case, where almost linear speedup is obtained, a world containing over 250 polygons is rendered at 32 frames/second. The bandwidth of the transputer links is the major factor limiting speedup. A description is given of an input device driver which makes use of a powerglove. Techniques for overcoming the limitations of this device, and for interacting with the virtual world, are discussed. The virtual world kernel is designed to make extensive use of the parallel processing facilities provided by transputers. It is capable of providing support for mUltiple worlds concurrently, and for multiple users interacting with these worlds. Two applications are described that were successfully implemented using this system. The design of the system is compared with other recently developed virtual reality systems. Features that are common or advantageous in each of the systems are discussed. The system described in this thesis compares favourably, particularly in its use of parallel processors.KMBT_22

Early Stages of Precipitation In Aluminum Alloys by First-Principles and Machine-Learning Atomistic Simulations

Age hardening induced by the formation of (semi)-coherent precipitate phases is crucial for the processing and final properties of the widely used Al-6000 alloys despite the early stages of precipitation are still far from being fully understood. This crucial step in the technology of Al-based alloys is studied by means of multi-scale simulations that include first-principles atomistic modeling, surrogate models based on statistical learning, as well as kinetic Monte Carlo and continuum elasticity models to bridge time and length scales. We begin with an analysis of the energetics of nanometric precipitates of the meta-stable beta'' phases (that play a crucial role in this system) identifying the bulk, elastic strain and interface energies that contribute to the stability of a nucleating cluster. Results show that needle-shape precipitates are unstable to growth even at the smallest size beta'' formula unit. This study made it possible to develop a semi-quantitative classical nucleation theory model, including also elastic strain energy, that captures the trends in precipitate energy versus size and composition. This validates the use of mesoscale models to assess stability and interactions of beta'' precipitates. Studies of smaller 3D clusters also show stability relative to the solid solution state, indicating that the early stages of precipitation may be diffusion-limited. Our results thus point toward the need for a systematic study of the energetics of aggregates in the Guinier-Preston zone regime, and the interactions between those aggregates and vacancies and/or trace elements to understand and fine-tune the behavior of Al-6000 alloys in the early stages of precipitation. To enable full atomistic-level simulations of the whole precipitation sequence of this important alloy system, two Neural Network (NN) potentials have been created by representing just 2-body interactions and including also the 3-body interactions. For the latter, we developed an automatic scheme to determine the most appropriate representation of the structural features of this ternary alloy. Training of the NN uses an extensive database of energies and forces computed using Density Functional Theory, including complex precipitate phases. The NN potentials accurately reproduce most of the properties of pure Al which are relevant to the mechanical behavior and formation energies of small solute clusters and precipitates that are required for modeling the precipitation and mechanical strengthening. This success not only enables future detailed studies of Al-Mg-Si but also highlights the ability of machine learning methods to generate useful potentials in complex alloy systems. Finally, we used this NN potential to implement a kinetic Monte Carlo scheme to study the formation of pre-precipitation clusters. While quantitative accuracy will probably require further refinement of its training set, to achieve a more complete description of the interactions between solute atoms and vacancies, we could already observe some of the key mechanisms the determine the ultra-fast formation of aggregates. This work lays the foundations for a thorough investigation of the behavior of Al-6000 alloys over time and size scales that are technologically relevant and demonstrates a combination of atomistic modeling techniques that could be adapted to a large number of similar metallic alloys

Accelerating Materials Discovery with Machine Learning

As we enter the data age, ever-increasing amounts of human knowledge are being recorded in machine-readable formats. This has opened up new opportunities to leverage data to accelerate scientific discovery. This thesis focuses on how we can use historical and computational data to aid the discovery and development of new materials. We begin by looking at a traditional materials informatics task -- elucidating the structure-function relationships of high-temperature cuprate superconductors. One of the most significant challenges for materials informatics is the limited availability of relevant data. We propose a simple calibration-based approach to estimate the apical and in-plane copper-oxygen distances from more readily available lattice parameter data to address this challenge for cuprate superconductors. Our investigation uncovers a large, unexplored region of materials space that may yield cuprates with higher critical temperatures. We propose two experimental avenues that may enable this region to be accessed. Computational materials exploration is bottle-necked by our ability to provide input structures to feed our workflows. Whilst \textit{ab-intio} structure identification is possible, it is computationally burdensome and we lack design rules for deciding where to target searches in high-throughput setups. To address this, there is a need to develop tools that suggest promising candidates, enabling automated deployment and increased efficiency. Machine learning models are well suited to this task, however, current approaches typically use hand-engineered inputs. This means that their performance is circumscribed by the intuitions reflected in the chosen inputs. We propose a novel way to formulate the machine learning task as a set regression problem over the elements in a material. We show that our approach leads to higher sample efficiency than other well-established composition-based approaches. Having demonstrated the ability of machine learning to aid in the selection of promising compound compositions, we next explore how useful machine learning might be for identifying fabrication routes. Using a recently released data-mined data set of solid-state synthesis reactions, we design a two-stage model to predict the products of inorganic reactions. We critically explore the performance of this model, showing that whilst the predictions fall short of the accuracy required to be chemically discriminative, the model provides valuable insights into understanding inorganic reactions. Through careful investigation of the model's failure modes, we explore the challenges that remain in the construction of forward inorganic reaction prediction models and suggest some pathways to tackle the identified issues. One of the principal ways that material scientists understand and categorise materials is in terms of their symmetries. Crystal structure prototypes are assigned based on the presence of symmetrically equivalent sites known as Wyckoff positions. We show that a powerful coarse-grained representation of materials structures can be constructed from the Wyckoff positions by discarding information about their coordinates within crystal structures. One of the strengths of this representation is that it maintains the ability of structure-based methods to distinguish polymorphs whilst also allowing combinatorial enumeration akin to composition-based approaches. We construct an end-to-end differentiable model that takes our proposed Wyckoff representation as input. The performance of this approach is examined on a suite of materials discovery experiments showing that it leads to strong levels of enrichment in materials discovery tasks. The research presented in this thesis highlights the promise of applying data-driven workflows and machine learning in materials discovery and development. This thesis concludes by speculating about promising research directions for applying machine learning within materials discovery