A robust, user-friendly, and automated method to determine quantum conductance in quasi-one-dimensional systems is presented. The scheme relies upon an initial density-functional theory calculation in a specific geometry after which the ground-state eigenfunctions are transformed to a maximally-localised Wannier function (MLWF) basis. In this basis, our novel algorithms manipulate and partition the Hamiltonian for the calculation of coherent electronic transport properties within the Landauer-Buttiker formalism. Furthermore, we describe how short-ranged Hamiltonians in the MLWF basis can be combined to build model Hamiltonians of large (>10,000 atom) disordered systems without loss of accuracy. These automated algorithms have been implemented in the Wannier90 code [1], which is interfaced to a number of electronic structure codes such as Quantum-ESPRESSO, AbInit, Wien2k, SIESTA and FLEUR. We apply our methods to an Al atomic chain with a Na defect, an axially heterostructured Si/Ge nanowire and to a spin-polarised defect on a zigzag graphene nanoribbon.

-We calculate the thermoelectric figure of merit, zT = S 2 GT /(κ l + κe), for p-type Si nanowires with axial Ge heterostructures using a combination of first-principles density-functional theory, interatomic potentials, and Landauer-Buttiker transport theory. We consider nanowires with up to 8400 atoms and twelve Ge axial heterostructures along their length. We find that introducing heterostructures always reduces S 2 G, and that our calculated increases in zT are predominantly driven by associated decreases in κ l . Of the systems considered, ⟨111⟩ nanowires with a regular distribution of Ge heterostructures have the highest figure-of-merit: zT = 3, an order of magnitude larger than the equivalent pristine nanowire. Even in the presence of realistic structural disorder, in the form of small variations in length of the heterostructures, zT remains several times larger than that of the pristine case, suggesting that axial heterostructuring is a promising route to high-zT thermoelectric nanowires.

We investigated the structure of the low density regions of the inner crust of neutron stars using the Hartree-Fock-Bogoliubov (HFB) model to predict the proton content Z of the nuclear clusters and, together with the lattice spacing, the proton content of the crust as a function of the total baryonic density ρ b . The exploration of the energy surface in the (Z, ρ b ) configuration space and the search for the local minima require thousands of calculations. Each of them implies an HFB calculation in a box with a large number of particles, thus making the whole process very demanding. In this work, we apply a statistical model based on a Gaussian Process Emulator that makes the exploration of the energy surface ten times faster. We also present a novel treatment of the HFB equations that leads to an uncertainty on the total energy of ≈ 4 keV per particle. Such a high precision is necessary to distinguish neighbour configurations around the energy minima.

We investigated the role of a pairing correlation in the chemical composition of the inner crust of a neutron star with the extended Thomas–Fermi method, using the Strutinsky integral correction. We compare our results with the fully self-consistent Hartree–Fock–Bogoliubov approach, showing that the resulting discrepancy, apart from the very low density region, is compatible with the typical accuracy we can achieve with standard mean-field methods.

We present a new teaching and outreach activity based around the construction of a three-dimensional chart of isotopes using ® LEGO bricks 5

By using a machine learning algorithm, we present an improved nuclear mass table with a root mean square deviation of less than 200 keV. The model is equipped with statistical error bars in order to compare with available experimental data. We use the resulting model to predict the composition of the outer crust of a neutron star. By means of simple Monte Carlo methods, we propagate the statistical uncertainties of the mass model to the equation of state of the system.

We discuss advanced statistical methods to improve parameter estimation of nuclear models. In particular, using the Liquid Drop Model for nuclear binding energies, we show that the area around the global χ 2 minimum can be efficiently identified using Gaussian Process Emulation. We also demonstrate how Markov-chain Monte-Carlo sampling is a valuable tool for visualising and analysing the associated multidimensional likelihood surface.

We perform a systematic investigation of the chemical composition of the inner crust of a neutron star, using the extended Thomas-Fermi approximation, the Strutinsky integral correction for shell effects, and the BCS approximation for pairing. 15 Skyrme functionals were selected, which cover the range of values of important bulk properties of infinite nuclear matter, while also having pure neutron matter (PNM) equation of states (EoS) with varying degrees of stiffness. We find that a functional's low-density PNM EoS is correlated with the number of protons found in the inner crust's nuclear clusters and, in the lower-density region of the inner crust, with the pressure.