Materials Science (cond-mat.mtrl-sci)

Abstract: The recent experimental fabrication of monolayer and few-layer C60 polymers paves the way for synthesizing two-dimensional cluster-assembled materials. Compared to atoms with the SO(3) symmetry, clusters as superatoms (e.g., C60) have an additional rotational degree of freedom, greatly enriching the phase spaces of superatom-assembled materials. Using first-principles calculations, we find the energy barriers of cluster rotation in quasi-tetragonal monolayer C60 structures are rather low (about 10 meV/atom). The small rotational energy barriers lead to a series of tetragonal C60 polymorphs with energies that are close to the experimental quasi-tetragonal (expt-qT) phase. Similarly, several dynamically stable quasi-hexagonal monolayer C60 structures are found to have energies within 7 meV/atom above the experimental quasi-hexagonal phase. Our calculations demonstrate photo-excited electron-hole pairs and electrostatic doping of electrons can effectively modulate the relative energies of quasi-tetragonal C60 polymorphs. Particularly, the unstable monolayer expt-qT phase becomes dynamically stable when it is electrostatically doped with electrons. In contrast, the relative energies between different quasi-hexagonal polymorphs are insensitive to electrostatic doping of electrons.

Abstract: Characterizing ion adsorption and diffusion in porous carbons is essential to understand the performance of such materials in a range of key technologies such as energy storage and capacitive deionisation. Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful technique to get insights in these systems thanks to its ability to distinguish between bulk and adsorbed species and to its sensitivity to dynamic phenomena. Nevertheless, a clear interpretation of the experimental results is sometimes rendered difficult by the various factors affecting NMR spectra. A mesoscopic model to predict NMR spectra of ions diffusing in carbon particles is adapted to include dynamic exchange between the intra-particle space and the bulk electrolyte surrounding the particle. A systematic study of the particle size effect on the NMR spectra for different distributions of magnetic environments in the porous carbons is conducted. The model demonstrates the importance of considering a range of magnetic environments, instead of a single chemical shift value corresponding to adsorbed species, and of including a range of exchange rates (between in and out of the particle), instead of a single timescale, to predict realistic NMR spectra. Depending on the pore size distribution of the carbon particle and the ratio between bulk and adsorbed species, both the NMR linewidth and peak positions can be largely influenced by the particle size.

Abstract: We present the design, fabrication and discuss the performance of a new combined high-resolution Scanning Tunneling and thermopower Microscope (STM/SThEM). We also describe the development of the electronic control, the user interface, the vacuum system, and arrangements to reduce acoustical noise and vibrations. We demonstrate the microscope performance with atomic-resolution topographic images of Highly oriented pyrolytic graphite (HOPG) and local thermopower measurements in the semimetal Bi2Te3 sample. Our system offers a tool to investigate the relationship between electronic structure and thermoelectric properties at the nanoscale.

Abstract: There has been a longstanding debate whether the pyrite CoS$_2$ or its alloys with FeS$_2$ are half metallic. We argue using first principles calculations that there is a finite occupation of minority-spin states at the Fermi level throughout the series Co$_{1-x}$Fe$_x$S$_2$. Although the exchange-correlation functional influences the specifics of the electronic structure, we observe a similar trend with increasing Fe concentration in both LDA and GGA calculations. Specifically, even as band filling is decreased through Fe substitution, the lowest-lying conduction band in the minority-spin channel broadens such that these states keep getting lowered relative to the Fermi level, which is contrary to the expectations from a rigid band picture. Furthermore, the exchange splitting decreases as more Co atoms are replaced by Fe, and this again brings the minority-spin states closer to the Fermi level. These two mechanisms, in conjunction with the experimental observation that minority-spin bands cross the Fermi level in stoichiometric CoS$_2$, indicate that minority-spin charge carriers will always be present in Co$_{1-x}$Fe$_x$S$_2$.

Abstract: We present a general-purpose parameterization of the atomic cluster expansion (ACE) for magnesium. The ACE shows outstanding transferability over a broad range of atomic environments and captures physical properties of bulk as well as defective Mg phases in excellent agreement with reference first-principles calculations. We demonstrate the computational efficiency and the predictive power of ACE by calculating properties of extended defects and by evaluating the P-T phase diagram covering temperatures up to 3000 K and pressures up to 80 GPa. We compare the ACE predictions with those of other interatomic potentials, including the embedded-atom method, an angular-dependent potential, and a recently developed neural network potential. The comparison reveals that ACE is the only model that is able to predict correctly the phase diagram in close agreement with experimental observations.

Abstract: Altermagnetism is a recently identified magnetic symmetry class combining characteristics of conventional collinear ferromagnets and antiferromagnets, that were regarded as mutually exclusive, and enabling phenomena and functionalities unparalleled in either of the two traditional elementary magnetic classes. In this work we use symmetry and ab initio theory to explore X-ray magnetic circular dichroism (XMCD) in the altermagnetic class. Our results highlight the distinct phenomenology in altermagnets of this time-reversal symmetry breaking response, and its potential utility for element-specific spectroscopy and microscopy in altermagnets. As a representative material for our XMCD study we choose $\alpha$-MnTe with the compensated antiparallel magnetic order in which an anomalous Hall effect has been already demonstrated both in theory and experiment. The predicted magnitude of XMCD lies well within the resolution of existing experimental techniques.

Abstract: The penetration of atomic hydrogen through defect-free graphene was generally predicted to have a barrier of at least several eV, which is much higher than the 1 eV barrier measured for hydrogen-gas permeation through pristine graphene membranes. Herein, our density functional theory calculations show that ripples, which are ubiquitous in atomically thin crystals and mostly overlooked in the previous simulations, can significantly reduce the barriers for all steps constituting the mechanism of hydrogen-gas permeation through graphene membranes, including dissociation of hydrogen molecules, reconstruction of the dissociated hydrogen atoms and their flipping across graphene. Especially, the flipping barrier of hydrogen atoms from a cluster configuration is found to decrease rapidly down to <1 eV with increasing ripples' curvature. The estimated hydrogen permeation rates by fully considering the distribution of ripples with all realistic curvatures and the major reaction steps that occurred on them are quite close to the experimental measurements. Our work provides insights into the fundamental understanding of hydrogen-gas permeation through graphene membranes and emphasizes the importance of nanoscale non-flatness (ripples) in explaining many surface and transport phenomena (for example, functionalization, corrosion and separation) in graphene and other two-dimensional materials.

Abstract: Quaternary III-V semiconductors are one of the major promising material classes in optoelectronics. The bandgap and its character, direct or indirect, are the most important fundamental properties determining the performance and characteristics of optoelectronic devices. Experimental approaches screening a large range of possible combinations of III- and V-elements with variations in composition and strain are impractical for every target application. We present a combination of accurate first-principles calculations and machine learning based approaches to predict the properties of the bandgap for quaternary III-V semiconductors. By learning bandgap magnitudes and their nature at density functional theory accuracy based solely on the composition and strain features of the materials as an input, we develop a computationally efficient yet highly accurate machine learning approach that can be applied to a large number of compositions and strain values. This allows for a computationally efficient prediction of a vast range of materials under different strains, offering the possibility for virtual screening of multinary III-V materials for optoelectronic applications.

Abstract: The tribological characteristics of NbC/Nb ceramic/metal nano-laminate (CMNLs) coatings were studied using molecular dynamics atomistic simulations of nano-indentation and nano-scratching by penetrating and moving a spherical indenter into the models. The effect of individual metallic or ceramic layer thickness and penetration depth on the scratching behavior of the NbC/Nb nanolaminates were investigated. The results showed generally the individual metallic and ceramic layer thickness play a significant role. However, some punctures were seen on the top ceramic layer of some model which can significantly after the scratching behavior by reducing the effect the individual metallic and ceramic layer thickness on the scratching behavior. The reason for being punctured models is the thickness of the top ceramic layer is too low that the indenter can easily puncture the ceramic layer instead of pushing the atoms of ceramic. The least thickness that can resist the indenter can be defined as a critical thickness which is dependent on the indenter size and penetration depth. In the later part of this paper, machine learning has been employed to reduce the computational cost and it is shown that the machine learning based model can predict the friction coefficient with R- squared value 0.958.