Karsten Albe

We investigate magnetron-sputtered In2 (OxS1− x) 3 compounds acting as an alternative buffer system to the solution-grown CdS or Zn (O, S) buffer layers in Cu (In, Ga) Se2 (CIGS) thin-film solar cells. The influence of the oxygen content on the solar cell performance, microstructure of the mixed systems, bandgap, and band offsets to CIGS is investigated experimentally and also characterized by calculations based on density functional theory. Samples in a series with different chemical compositions ranging from In2S3 to In2O3 are either directly deposited from ceramic targets or from a pure In2S3 target by reactive sputtering by adding O2 in the Ar sputtering gas. The binary compounds In2S3 and In2O3 sputtered at 220 C substrate temperature from ceramic targets exhibit a crystalline structure, whereas the ternary In2 (O, S) 3 compounds are either nanocrystalline in the case of In2 (O0. 25S0. 75) 3 or amorphous …

This dataset supports the paper" Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO2". The following files are provided:-The training database for the simple (artificial and MD) and the SiO2 structures--> The training data is provided in two different formats. In the" simple_training_dump" and" SiO2_training_dump" files, the dump files from the MD trajectories are provided. In the" simple_training_extracted" and" SiO2_training_extracted" files 1,000,000 extracted atomic environments in a numpy format are stored.-The holdout dataset for the simple structures-The snapshots of the SiO2 shock simulation

A general purpose machine-learning interatomic potential (MLIP) for the Cu-Zr system is presented based on the atomic cluster expansion formalism [R. Drautz, Phys. Rev. B 99, 014104 (2019)]. By using an extensive set of Cu-Zr training data generated withdensity functional theory, this potential describes a wide range of properties of crystalline as well as amorphous phases within the whole compositional range. Therefore, the machine learning interatomic potential (MLIP) can reproduce the experimental phase diagram and amorphous structure with considerably improved accuracy. A massively different short-range order compared to classica interatomic potentials is found in glassy Cu-Zr samples, shedding light on the role of the full icosahedral motif in the material. Tensile tests of B2-CuZr inclusions in an Cu 50 Zr 50 amorphous matrix reveal the occurrence of martensitic phase transformations in this crystal …

Silicon oxycarbides show outstanding versatility due to their highly tunable composition and microstructure. Consequently, a key challenge is a thorough knowledge of structure-property relations in the system. In this work, we fit an atomic cluster expansion potential to a set of actively learned DFT training data spanning a wide configurational space. We demonstrate the ability of the potential to produce realistic amorphous structures and rationalize the formation of different morphologies of the turbostratic free carbon phase. Finally, we relate the materials stiffness to its composition and microstructure, finding a delicate dependence on Si-C bonds that contradicts commonly assumed relations to the free carbon phase.

We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry.

Silicon–oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si–O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si–O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length …

Reversible field-induced phase transitions define antiferroelectric perovskite oxides and lay the foundation for high-energy storage density materials, required for future green technologies. However, promising new antiferroelectrics are hampered by transition´s irreversibility and low electrical resistivity. Here, we demonstrate an approach to overcome these problems by adjusting the local structure and defect chemistry, delivering NaNbO3-based antiferroelectrics with well-defined double polarization loops. The attending reversible phase transition and structural changes at different length scales are probed by in situ high-energy X-ray diffraction, total scattering, transmission electron microcopy, and nuclear magnetic resonance spectroscopy. We show that the energy-storage density of the antiferroelectric compositions can be increased by an order of magnitude, while increasing the chemical disorder transforms the …

Polymer‐derived silicon oxycarbide ceramics (SiCO) have been considered as potential anode materials for lithium‐ and sodium‐ion batteries. To understand their electrochemical storage behavior, detailed insights into structural sites present in SiCO are required. In this work, the study of local structures in SiCO ceramics containing different amounts of carbon is presented. 13C and 29Si solid‐state MAS NMR spectroscopy combined with DFT calculations, atomistic modeling, and EPR investigations, suggest significant changes in the local structures of SiCO ceramics even by small changes in the material composition. The provided findings on SiCO structures will contribute to the research field of polymer‐derived ceramics, especially to understand electrochemical storage processes of alkali metal/ions such as Na/Na+ inside such networks in the future.