In this project, various computational energy landscape methods were accelerated using graphics processing units (GPUs). Basin-hopping global optimisation was treated using a version of the limited-memory BFGS algorithm adapted for CUDA, in combination with GPU-acceleration of the potential calculation. The Lennard-Jones potential was implemented using CUDA, and an interface to the GPU-accelerated AMBER potential was constructed. These results were then extended to form the basis of a GPU-accelerated version of hybrid eigenvector-following. The doubly-nudged elastic band method was also accelerated using an interface to the potential calculation on GPU. Additionally, a local rigid body framework was adapted for GPU hardware. Tests were performed for eight biomolecules represented using the AMBER potential, ranging in size from 81 to 22\,811 atoms, and the effects of minimiser history size and local rigidification on the overall efficiency were analysed. Improvements relative to CPU performance of up to two orders of magnitude were obtained for the largest systems. These methods have been successfully applied to both biological systems and atomic clusters. An existing interface between a code for free energy basin-hopping and the SuiteSparse package for sparse Cholesky factorisation was refined, validated and tested. Tests were performed for both Lennard-Jones clusters and selected biomolecules represented using the AMBER potential. Significant acceleration of the vibrational frequency calculations was achieved, with negligible loss of accuracy, relative to the standard diagonalisation procedure. For the larger systems, exploiting sparsity reduces the computational cost by factors of 10 to 30. The acceleration of these computational energy landscape methods opens up the possibility of investigating much larger and more complex systems than previously accessible. A wide array of new applications are now computationally feasible.

In recent years, we have seen that global, national, and local governments have put sustainability goals on their agendas. Thus, at different levels and in different sectors, efforts are underway to promote a ‘green shift’, including the energy sector. As a result, landscapes of renewable energy sources are emerging in areas that have sufficient “empty landscapes” (LABLAB, 2023) – namely sparsely populated spaces that lie outside the administrative boundaries of cities. However, the discipline of spatial planning rarely discusses changing landscapes in the hinterlands and the resulting consequences for embedded communities. The city as an energy consumer is treated in isolation from its counterpart, the hinterland as an energy producer. In this context, it is unclear what interrelationships are present between the formation of ‘new energy landscapes’ (Pasqualetti and Stremke, 2018; LABLAB, 2023), the urban ‘hinterland’ (Brenner, 2016; Westlund, 2018; Brenner and Katsikis, 2020), and the realities of embedded communities there.The geographies in the Swedish province of Jämtland belonging to the territory of the indigenous Southern Sápmi offer a suitable basis for a study that could fill this research gap. Thus, the objective of this thesis is to raise awareness of potentially conflicting interests between cities - striving to become more ‘sustainable’ - and the emergence of ‘new energy landscapes’ in the ‘hinterlands’ by including two different perspectives: an urban economic lens on the hinterland and a non-urban lens taken from the lived everyday lives of Sápmi communities embedded in new energy landscapes. This thesis poses three research questions to which it aims to find answers by using qualitative semi-structured, problem-centred interviews. It thus follows an interpretative abductive research approach. Through the analysis of the empirical data, the thesis shows that a joint discussion of the two discourses (‘hinterland’ and ‘new energy landscapes’) can help to gain a new understanding of urbanisation processes by including the perspective of non-urban communities in questions of urban sustainability. Furthermore, the thesis serves as an eye-opener for spatial planners to incorporate indigenous knowledge and lived experiences into the field of urban studies.

Semicrystalline polymers are an important class of materials for their prevalence in today's markets and their desirable properties. These properties depend on the early stages of the polymer crystallization process where a crystal nucleates from the polymer melt. This nucleation process is conventionally understood via an extension of Classical Nucleation Theory to polymers (CNTP). However, recent experimental and simulation evidence points to nucleation mechanisms that do not agree with the predictions of CNTP. Specifically, these experiments suggest a previously unrecognized role of nematic phases in mediating the melt"“crystal transtion. To explain these observations, several new theories of nucleation alternate to CNTP have emerged in the literature, all of which suggest specific modifications to the free energy landscape (FEL) near-equilibrium. To address these theoretical controversies, this dissertation aimed to study the equilibrium phase behavior of polymers via Monte Carlo (MC) simulations. Simulating equilibrium phase behavior of polymer melts is not a trivial task due to the large free energy barriers involved. Throughout this research, we employed a combination of strategies to speed up these molecular simulations. First, we employed a domain decomposition to divide the simulation box into multiple independent simulations that execute independent MC trajectories in parallel. The novel GPU-accelerated MC algorithm successfully and accurately simulated the phase behavior of bead spring chains. Additionally, it sped up MC simulations of Lennard Jones chains by up to 10 times. In its current form, the GPU-accelerated algorithm did not achieve significant speedups to improve outcomes of simulating large polymer melts with detailed potentials. We recommended various strategies to improving the current algorithm. This reality motivated the use of biased MC simulations to study the phase behavior of polymers more expediently without the need for GPU acceleration. Specifically, the latter part of the Dissertation employed Wang Landau MC (WLMC) simulations to build phase diagrams and expanded ensemble density of states (EXEDOS) simulations to construct FELs. Phase diagrams from WLMC simulations divided volume-temperature space into melt, nematic and crystal phases. Then, FELs from EXEDOS simulations at equilibrium provided direct access to the relative stability and minimum free energy paths between coexistant states. By employing a two-dimensional EXEDOS sampling in both crystal and nematic order for hard bead semiflexible oligomers with a stepwise bending stiffness, we built FELs that show that the crystalline transition cooperatively and simultaneously formed crystal and nematic order. This nucleation mechanism was not in agreement with predictions from CNTP or newer theoretical formulations. To investigate the sensitivity of the phase behavior to the employed polymer model, we then employed WLMC simulations to build phase diagrams for a number of different polymer models to ascertain their impact on the resulting nucleation mechanism. We found that the phase behavior was sensitive to the form of the bending stiffness potential used. Chains with a stepwise bending stiffness yielded the previously mentioned cooperative and simultaneous crystal and nematic ordering. In contrast, chains with a harmonic bending stiffness potential crystallized via a two-step nucleation process, first forming a nematic phase that nucleates the crystal. The latter nucleation mechanism was in line with predictions from new theories of nucleation that incorporate the nematic phase as a precursor. Furthermore, we found that it is important to correct for excluded volume differences when comparing chains with soft and hard beads or chains with differing bending stiffnesses.