Materials Science (cond-mat.mtrl-sci)

Abstract: In organic proton-transfer ferroelectrics (OPTFe), molecules are linked together in a hydrogen-bonded network and proton transfer (PT) between molecules is the dominant mechanism of ferroelectric switching. Their fast switching frequencies make them attractive alternatives to conventional ceramic ferroelectrics, which contain rare and/or toxic elements, and require high processing temperatures. In this study, we mined the Cambridge Structural Database for potential OPTFes, uncovering all previously reported compounds, both tautomers and co-crystals, in addition to seven new candidate tautomers. The mining was based on identifying polar crystal structures with pseudo center-of-symmetry and viable PT paths. The spontaneous polarization and PT barriers were assessed using density functional theory.

Abstract: The discovery of two-dimensional (2D) van der Waals magnetic materials and their heterostructures provided an exciting platform for emerging phenomena with intriguing implications in information technology. Here, based on a multiscale modelling approach that combines first-principles calculations and a Heisenberg model, we demonstrate that interfacing a CrTe$_2$ layer with various Te-based layers enables the control of the magnetic exchange and Dzyaloshinskii-Moriya interactions as well as the magnetic anisotropy energy of the whole heterobilayer, and thereby the emergence of topological magnetic phases such as skyrmions and antiferromagnetic Neel merons. The latter are novel particles in the world of topological magnetism since they arise in a frustrated Neel magnetic environment and manifest as multiples of intertwined hexamer-textures. Our findings pave a promising road for proximity-induced engineering of both ferromagnetic and long-sought antiferromagnetic chiral objects in the very same 2D material, which is appealing for new information technology devices employing quantum materials.

Abstract: In quality control, microstructures are investigated rigorously to ensure structural integrity, exclude the presence of critical volume defects, and validate the formation of the target microstructure. For quenched, hierarchically-structured steels, the morphology of the bainitic and martensitic microstructures are of major concern to guarantee the reliability of the material under service conditions. Therefore, industries conduct small sample-size inspections of materials cross-sections through metallographers to validate the needle morphology of such microstructures. We demonstrate round-robin test results revealing that this visual grading is afflicted by pronounced subjectivity despite the thorough training of personnel. Instead, we propose a deep learning image classification approach that distinguishes steels based on their microstructure type and classifies their needle length alluding to the ISO 643 grain size assessment standard. This classification approach facilitates the reliable, objective, and automated classification of hierarchically structured steels. Specifically, an accuracy of 96% and roughly 91% is attained for the distinction of martensite/bainite subtypes and needle length, respectively. This is achieved on an image dataset that contains significant variance and labeling noise as it is acquired over more than ten years from multiple plants, alloys, etchant applications, and light optical microscopes by many metallographers (raters). Interpretability analysis gives insights into the decision-making of these models and allows for estimating their generalization capability.

Abstract: With a large band gap and a single Dirac cone responsible for the topological surface states, Bi2Se3 is widely regarded as a prototypical 3D topological insulator. Further applications of the bulk material has, however, been hindered by inherent structural defects that donate electrons and make the bulk conductive. Consequently, controlling these defects is of great interest for future technological applications, and while past literature has focused on adding external doping elements to the mixture, a complete study on undoped Bi2Se3 was still lacking. In this work, we use the self-flux method to obtain high-quality Bi2Se3 single-crystals in the entire concentration range available on the phase-diagram for the technique. By combining basic structural characterization with measurements of the resistivity, Hall effect and Shubnikov-de Haas (SdH) quantum oscillations, the effects of these impurities on the bulk transport are investigated in samples with electron densities ranging from 10^17 cm^-3 to 10^19 cm^-3, from Se-rich to Bi-rich mixtures, respectively, evidencing the transition into a degenerate semiconductor regime. We find that electron-donor impurities, likely Se vacancies, unavoidably shift the Fermi level up to 200 meV inside the conduction band. Other impurities, like interstitial Bi and Se, are shown to play a significant role as scattering centres, specially at low temperatures and in the decoherence of the SdH oscillations. Previous open questions on Bi2Se3, such as the upturn in resistivity below 30 K, the different scattering times in transport and quantum oscillations, and the presence of additional low mobility bands, are addressed. The results outlined here provide a concise picture on the bulk conducting states in flux-grown Bi2Se3 single crystals, enabling better control of the structural defects and electronic properties.

Abstract: The activity of metal catalysts depends sensitively on dynamic structural changes that occur during operating conditions. The mechanistic understanding underlying such transformations in small Pt nanoparticles (NPs) of $\sim1-5$ nm in diameter, commonly used in hydrogenation reactions, is currently far from complete. In this study, we investigate the structural evolution of Pt NPs in the presence of hydrogen using reactive molecular dynamics (MD) simulations and X-ray spectroscopy measurements. To gain atomistic insights into adsorbate-induced structural transformation phenomena, we employ a combination of MD based on first-principles machine-learned force fields with extended X-ray absorption fine structure (EXAFS) measurements. Simulations and experiments provide complementary information, mutual validation, and interpretation. We obtain atomic-level mechanistic insights into `order-disorder' structural transformations exhibited by highly dispersed heterogeneous Pt catalysts upon exposure to hydrogen. We report the emergence of previously unknown candidate structures in the small Pt NP limit, where exposure to hydrogen leads to the appearance of a `quasi-icosahedral' intermediate symmetry, followed by the formation of `rosettes' on the NP surface. Hydrogen adsorption is found to catalyze these shape transitions by lowering their temperatures and increasing the apparent rates, revealing the dual catalytic and dynamic nature of interaction between nanoparticle and adsorbate. Our study also offers a new pathway for deciphering the reversible evolution of catalyst structure resulting from the chemisorption of reactive species, enabling the determination of active sites and improved interpretation of experimental results with atomic resolution.

Abstract: The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical and biological processes at such interfaces. Classical molecular dynamics simulations have been applied extensively to simulate the response of inhomogeneous fluids directly, and as inputs to classical density functional theory, but are limited by the accuracy of the underlying empirical force fields. Here, we deploy neural network potentials (NNPs) trained to \emph{ab initio} simulations to accurately predict the inhomogeneous response of two widely different fluids: liquid water and molten NaCl. Although the advantages of NNPs is that they can be readily trained to model complex systems, one limitation in modeling the inhomogeneous response of liquids is the need for including the correct configurations of the system in the training data. Therefore, first we establish protocols, based on molecular dynamics simulations in external atomic potentials, to sufficiently sample the correct configurations of inhomogeneous fluids. We show that NNPs trained to inhomogeneous fluid configurations can predict several properties such as the density response, surface tension and size-dependent cavitation free energies in water and molten NaCl corresponding to \emph{ab initio} interactions, more accurately than with empirical force fields. This work therefore provides a first demonstration and framework for extracting the response of inhomogeneous fluids from first principles for classical density-functional treatment of fluids free from empirical potentials.