Materials Science (cond-mat.mtrl-sci)

Abstract: Automatic crystal orientation determination and orientation mapping are important tools for research on polycrystalline materials. The most common methods of automatic orientation determination rely on detecting and indexing individual diffraction reflections. These methods, however, have not been used for orientation mapping of quasicrystalline materials. The paper describes necessary changes to existing software designed for orientation determination of periodic crystals so that it can be applied to quasicrystals. The changes are implemented in one of such programs. The functioning of the modified program is illustrated by an example orientation map of an icosahedral polycrystal.

Abstract: BaAgAs is a ternary Dirac semimetal which can be tuned across a number of topological orders. In this study we have investigated the bulk physical properties of BaAgAs using density functional theory based computations. Most of the results presented in this work are novel. The optimized structural parameters are in good agreement with previous results. The elastic constants indicate that BaAgAs is mechanically stable and brittle in nature. The compound is moderately hard and possesses fair degree of machinability. There is significant mechanical/elastic anisotropy in BaAgAs. The Debye temperature of the compound is medium and the phonon thermal conductivity and melting temperature are moderate as well. The bonding character is mixed with notable covalent contribution. The electronic band structure calculations reveal clear semimetallic behavior with a Dirac node at the Fermi level. BaAgAs has a small ellipsoidal Fermi surface centered at the G-point of the Brillouin zone. The phonon dispersion curves show dynamical stability. There is a clear phonon band gap between the acoustic and the optical branches. The energy dependent optical constants conform to the band structure calculations. The compound is an efficient absorber of the ultraviolet light and has potential to be used as an anti-reflection coating. Optical anisotropy of BaAgAs is moderate. The computed repulsive Coulomb pseudopotential is low indicating that the electronic correlations in this compound are not strong.

Abstract: The recent successes of emerging photovoltaics (PV) such as organic and perovskite solar cells are largely driven by innovations in material science. However, closing the gap to commercialization still requires significant innovation to match contradicting requirements such as performance, longevity and recyclability. The rate of innovation, as of today, is limited by a lack of design principles linking chemical motifs to functional microscopic structures, and by an incapacity to experimentally access microscopic structures from investigating macroscopic device properties. In this work, we envision a layout of a Digital Twin for PV materials aimed at removing both limitations. The layout combines machine learning approaches, as performed in materials acceleration platforms (MAPs), with mathematical models derived from the underlying physics and digital twin concepts from the engineering world. This layout will allow using high-throughput (HT) experimentation in MAPs to improve the parametrization of quantum chemical and solid-state models. In turn, the improved and generalized models can be used to obtain the crucial structural parameters from HT data. HT experimentation will thus yield a detailed understanding of generally valid structure-property relationships, enabling inverse molecular design, that is, predicting the optimal chemical structure and process conditions to build PV devices satisfying a multitude of requirements at the same time. After motivating our proposed layout of the digital twin with causal relationships in material science, we discuss the current state of the enabling technologies, already being able to yield insight from HT data today. We identify open challenges with respect to the multiscale nature of PV materials and the needed volume and diversity of data, and mention promising approaches to address these challenges.

Abstract: Ion implantation has been a key technology for the controlled surface modification of materials in microelectronics and generally, for tribology, biocompatibility, corrosion resistance and many more. To form shallow junctions in Ge is a challenging task. In this work the formation and accumulation of shallow damage profiles was studied by in-situ spectroscopic ellipsometry (SE) for the accurate tracking and evaluation of void and damage fractions in crystalline Ge during implantation of 200-keV Sb ions with a total fluence up to 1E16 cm-2 and an ion flux of 2.1E12 cm-2 s-1. The consecutive stages of damage accumulation were identified using optical multi-layer models with quantitative parameters of the thickness of modified layers as well as the volume fractions of amorphized material and voids. The effective size of damaged zones formed from ion tracks initiated by individual bombarding ions can be estimated by numerical simulation compared with the dynamics of damage profiles measured by ion beam analysis and ellipsometry. According to our observations, the formation of initial partial disorder was followed by complete amorphization and void formation occurring at the fluence of about 1E15 cm-2, leading to a high volume fraction of voids and a modified layer thickness of approx. 200 nm by the end of the irradiation process. This agrees with the results of numerical simulations and complementary scanning electron microscopy (SEM) measurements. In addition, we found a quasi-periodic time dependent behavior of amorphization and void formation represented by alternating accelerations and decelerations of different reorganization processes, respectively.