Searcharxiv⌕ Search

arXiv subjects

Konstantin Köster

Publications and source records attributed to Konstantin Köster.

4 recordsLinked to original sources

Computational Studies on O2-P2 Phase-Transition Dynamics in Layered-Oxide Sodium-Ion Cathode Materials

Sodium-ion batteries have gained much interest over the past years and especially layered oxides are highly considered as cathodes for the next generation of batteries. However, there are still significant challenges to overcome in these materials for practical applications mainly related to capacity degradation and voltage fading. A key influence factor for these challenges are phase transitions that occur by gliding of layers during operation of these materials. Until now there is limited atomistic-level understanding on such transitions as simulations of these processes are computationally demanding. In this work, we trained a classical pairwise Coulomb-Buckingham potential versus extensive \textit{ab initio} data using a genetic algorithm to study O2-P2 phase transitions in Na\textsubscript{\textit{x}}CoO\textsubscript{2}. Our density functional theory~(DFT) and classical potential calculations show that phase transition barriers decrease upon desodiation and are further lowered if dynamic conditions are considered through molecular dynamics simulations. Our developed classical potential is able to capture phase transitions and its related increase in the Na-ion diffusivity under standard lab conditions at the $\upmu$s timescale of molecular dynamics simulation. Furthermore, it is found that the phase transition occurs gradually \textit{via} various OP\textit{n} phases.

cond-mat.mtrl-sci↗

A Semi-Empirical Descriptor for Open Circuit Voltage

Layered transition metal oxides (TMO) are widely used as cathode materials in Na/Li batteries. The open-circuit voltage (OCV), which determines the energy density (together with capacity), is among the key physical and chemical factors influencing the performance of cathodes. The shape of the voltage profile is also influenced by the formation energy of intermediate phases during cycling. From a theoretical perspective, the formation energy (and voltage) are defined as internal energy differences between phases. Therefore, an accurate prediction of internal energy is crucial for the calculation of OCV. In this work, we present a theoretical framework that decomposes the internal energy of a given TMO into distinct contributions with clear physical significance. Specifically, we break down the energy into parameters that can be more easily calculated (compared to DFT) and obtained from experimental databases. From these parameters, we define a potential term that can be calculated for different compositions, and can be used for calculation of voltage profile

cond-mat.mtrl-sci↗

Simulating charging characteristics of lithium iron phosphate by electro-ionic optimization on a quantum annealer

The rapid evolution of quantum computing hardware opens up new avenues in the simulation of energy materials. Today's quantum annealers are able to tackle complex combinatorial optimization problems. A formidable challenge of this type is posed by materials with site-occupational disorder for which atomic arrangements with a low, or lowest, energy must be found. In this article, a method is presented for the identification of the correlated ground-state distribution of both lithium ions and redox electrons in lithium iron phosphate (LFP), a widely employed cathode material in lithium-ion batteries. The point-charge Coulomb energy model employed correctly reproduces the LFP charging characteristics. As is shown, grand-canonical transformation of the energy cost function makes the combinatorial distribution problem solvable on quantum annealing (QA) hardware. The QA output statistics follow a pseudo-thermal behavior characterized by a problem-dependent effective sampling temperature, which has bearings on the estimated scaling of the QA performance with system size. This work demonstrates the potential of quantum computation for the joint optimization of electronic and ionic degrees of freedom in energy materials.

cond-mat.mtrl-sci↗

Optimization of Coulomb Energies in Gigantic Configurational Spaces of Multi-Element Ionic Crystals

Most of the novel energy materials contain multiple elements occupying a single site in their lattice. The exceedingly large configurational space of these materials imposes challenges in determining their ground-state structures. Coulomb energies of possible configurations generally show a satisfactory correlation to computed energies at higher levels of theory and thus allow to screen for minimum-energy structures. Employing a second-order cluster expansion, we obtain an efficient Coulomb energy optimizer using Monte Carlo and Genetic Algorithms. The presented optimization package, GOAC (Global Optimization of Atomistic Configurations by Coulomb), can achieve a speed up of several orders of magnitude compared to existing software. Our code is able to find low-energy configurations of complex systems involving up to $10^{920}$ structural configurations. The GOAC package thus provides an efficient method for constructing ground-state atomistic models for multi-element materials with gigantic configurational spaces.

cond-mat.mtrl-sci↗