arXiv subjects

Espen Sagvolden

Publications and source records attributed to Espen Sagvolden.

4 recordsLinked to original sources

arXiv2026

Tailoring the material properties, nanostructure and grain alignment of Alnico magnets through micromagnetic simulations

Alnico magnets have gained renewed interest in the search for rare-earth free permanent magnets due to their high thermal stability and magnetisation. However, the limited coercivity of these shape-anisotropy-based alloys constrains their performance. Starting from a reference Alnico sample, we realised a finite elements micromagnetic study of exchange-decoupled rods by varying their dimensions and interrod spacing across those observed experimentally. We computed the hysteresis properties by progressing from micromagnetic simulations of a small number of rods within the magnetostatic field of their neighbours to large systems treated statistically based on the distribution of orientations of the grains. We compared the coercivity of an isolated rod with that of the exchange-decoupled system to highlight the effect of magnetostatic interactions. We computed analytically the stray field acting on a single rod as a consequence of its surrounding rods in order to confirm the scaling of the coercivity with the packing fraction p. We explored how intrinsic material properties influence magnetic behaviour by examining materials with different magnetocrystalline anisotropy constants and saturation polarisation values. Results from several hundred simulations were used to train a multi-layer perceptron regressor and predict the magnetic properties as function of the dimensions of the rods, interrod spacing and orientation of the grains. With this approach, we highlight the underlying trends by which nanoscale structuring, intrinsic material properties and grain alignment can be tailored to improve the magnetic properties of Alnico alloys.

cond-mat.mtrl-sci↗

arXiv2019

Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges

High-throughput computational and experimental design of materials aided by machine learning have become an increasingly important field in material science. This area of research has emerged in leaps and bounds in the thermal sciences, in part due to the advances in computational and experimental methods in obtaining thermal properties of materials. In this paper, we provide a current overview of some of the recent work and highlight the challenges and opportunities that are ahead of us in this field. In particular, we focus on the use of machine learning and high-throughput methods for screening of thermal conductivity for compounds, composites and alloys as well as interfacial thermal conductance. These new tools have brought about a feedback mechanism for understanding new correlations and identifying new descriptors, speeding up the discovery of novel thermal functional materials.

cond-mat.mtrl-sci↗

arXiv2012

The choice of basic variables in current-density functional theory

The selection of basic variables in current-density functional theory and formal properties of the resulting formulations are critically examined. Focus is placed on the extent to which the Hohenberg--Kohn theorem, constrained-search approach and Lieb's formulation (in terms of convex and concave conjugation) of standard density-functional theory can be generalized to provide foundations for current-density functional theory. For the well-known case with the gauge-dependent paramagnetic current density as a basic variable, we find that the resulting total energy functional is not concave. It is shown that a simple redefinition of the scalar potential restores concavity and enables the application of convex analysis and convex/concave conjugation. As a result, the solution sets arising in potential-optimization problems can be given a simple characterization. We also review attempts to establish theories with the physical current density as a basic variable. Despite the appealing physical motivation behind this choice of basic variables, we find that the mathematical foundations of the theories proposed to date are unsatisfactory. Moreover, the analogy to standard density-functional theory is substantially weaker as neither the constrained-search approach nor the convex analysis framework carry over to a theory making use of the physical current density.

cond-mat.mtrl-sci↗

arXiv2006

Relevance of the slowly-varying electron gas to atoms, molecules, and solids

Under a certain scaling, the electron densities of finite systems become both large and slowly-varying, so that the gradient expansions of the density functionals for the Kohn-Sham kinetic and exchange energies become asymptotically exact to order $\nabla^2$. Neutral atoms of large $Z$ scale similarly, but a cusp correction at the nucleus requires generalizing the gradient expansion for exchange, producing the wrong gradient coefficient in the slowly-varying limit. Meta-generalized gradient approximations (meta-GGA's) recover both the slowly-varying and large-$Z$ limits. GGA correlation energies of large-Z atoms are found to be accurate.

quant-ph↗