Sökning: "Igor Abrikosov"
Visar resultat 1 - 5 av 26 avhandlingar innehållade orden Igor Abrikosov.
1. Development and applications of theoretical algorithms for simulations of materials at extreme conditions
Sammanfattning : Materials at extreme conditions exhibit properties that differ substantially from ambient conditions. High pressure and high temperature expose anharmonic, non-linear behavior, and can provoke phase transitions among other effects. Experimental setups to study that sort of effects are typically costly and experiments themselves are laborious. LÄS MER
2. Spin splitting in open quantum dots and related systems
Sammanfattning : This thesis addresses electron spin phenomena in semi-conductor quantum dots/anti-dots from a computational perspective. In the first paper (paper I) we have studied spin-dependent transport through open quantum dots, i.e., dots strongly coupled to their leads, within the Hubbard model. LÄS MER
3. Configurational and Magnetic Interactions in Multicomponent Systems
Sammanfattning : This thesis is a theoretical study of configurational and magnetic interactions in multicomponent solids. These interactions are the projections onto the configurational and magnetic degrees of freedom of the underlying electronic quantum mechanical system, and can be used to model, explain and predict the properties of materials. LÄS MER
4. Effects of disorder in metallic systems from First-Principles calculations
Sammanfattning : In this thesis, quantum-mechanical calculations within density-functional theory on metallic systems are presented. The overarching goal has been to investigate effects of disorder. In particular, one of the properties investigated is the bindingenergy shifts for core electrons in binary alloys using different theoretical methods. LÄS MER
5. Combining ab‐initio and machine learning techniques for theoretical simulations of hard nitrides at extreme conditions
Sammanfattning : In this thesis I focus on combining the high accuracy of first-principles calculations with modern machine learning methods to make large scale investigations of industrially relevant nitride systems reliable and computationally viable. I study the electronic, thermodynamic and mechanical properties of two families of compounds: Ti1−xAlxN alloys at the operational conditions of industrial cutting tools and ReNx systems at crushing pres-sures comparable to inner earth core conditions. LÄS MER
