Abstract: The microstructural complexity of Li-rich cathode materials has so far hampered understanding the critical link between size, morphology and structural defects with both capacity and voltage fadings that this family of materials exhibits. Li2MnO3 is used here as a model material to extract reliable structure–property
relationships that can be further exploited for the development of high-performing and long-lasting Li-rich oxides. A series of samples with microstructural variability have been prepared and thoroughly characterized using the FAULTS software, which allows quantification of planar defects and extraction of
average crystallite sizes. Together with transmission electron microscopy (TEM) and density functional theory (DFT) results, the successful application of FAULTS analysis to Li2MnO3 has allowed rationalizing the synthesis conditions and identifying the individual impact of concurrent microstructural features on
both voltage and capacity fadings, a necessary step for the development of high-capacity Li-ion cathode materials with enhanced cycle life.

Abstract: While spectroscopic data on small hydrocarbons in interstellar media in combination with crossed molecular beam (CMB) experiments have provided a wealth of information on astrochemically relevant species, much of the underlying mechanistic pathways of their formation remain elusive. Therefore, in this work, the chemical reaction mechanisms of C(³P_J) + C₆H₆and C⁺(²P) + C₆H₆systems using the quantum mechanical molecular dynamics (QMMD) technique at the PBE0-D3(BJ) level of theory is investigated, mimicking a CMB experiment. Both the dynamics of the reactions as well as the electronic structure for the purpose of the reaction network are evaluated. The method is validated for the first reaction by comparison to the available experimental data. The reaction scheme for the C(³P_J) + C₆H₆system covers the literature data,<italic>e.g.</italic>the major products are the 1,2-didehydrocycloheptatrienyl radical (C₇H₅) and benzocyclopropenyl radical (C₆H₅–CH), and it reveals the existence of less common pathways for the first time. The chemistry of the C⁺(²P_J) + C₆H₆system is found to be much richer, and we have found that this is because of more exothermic reactions in this system in comparison to those in the C(³P_J) + C₆H₆system. Moreover, using the QMMD simulation, a number of reaction paths have been revealed that produce three distinct classes of reaction products with different ring sizes. All in all, at all the collision energies and orientations, the major product is the heptagon molecular ion for the ionic system. It is also revealed that the collision orientation has a dominant effect on the reaction products in both systems, while the collision energy mostly affects the charged system. These simulations both prove the applicability of this approach to simulate crossed molecular beams, and provide fundamental information on reactions relevant for the interstellar medium.

Abstract: Dielectric properties of nano-confined water are important in several areas of science, i.e., it is relevant in the dielectric double layer that exists in practically all heterogeneous fluid-based systems. Molecular dynamics simulations are used to predict the in-plane dielectric properties of confined water in planar channels of width ranging from sub-nanometer to bulk. Because of suppressed rotational degrees of freedom near the confining walls, the dipole of the water molecules tends to be aligned parallel to the walls, which results in a strongly enhanced in-plane dielectric constant (epsilon (parallel to)) reaching values of about 120 for channels with height 8 angstrom < h < 10 angstrom. With the increase in the width of the channel, we predict that epsilon (parallel to) decreases nonlinearly and reaches the bulk value for h > 70 angstrom. A stratified continuum model is proposed that reproduces the h > 10 angstrom dependence of epsilon (parallel to). For sub-nanometer height channels, abnormal behavior of epsilon (parallel to) is found with two orders of magnitude reduction of epsilon (parallel to) around h similar to 7.5 angstrom, which is attributed to the formation of a particular ice phase that exhibits long-time (similar to mu s) stable ferroelectricity. This is of particular importance for the understanding of the influence of confined water on the functioning of biological systems.

Abstract: Research progress on single layer group III monochalcogenides has been increasing rapidly owing to their interesting physics. Herein, we investigate the dynamically stable single layer forms of XBi (X = Ge, Si or Sn) using density functional theory calculations. Phonon band dispersion calculations and ab initio molecular dynamics simulations reveal the dynamical and thermal stability of the considered monolayers. Raman spectra calculations indicate the existence of 5 Raman active phonon modes, 3 of which are prominent and can be observed in possible Raman measurements. The electronic band structures of the XBi single layers were investigated with and without the effects of spin-orbit coupling (SOC). Our results show that XBi single layers show semiconducting properties with narrow band gap values without SOC. However, only single layer SiBi is an indirect band gap semiconductor, while GeBi and SnBi exhibit metallic behaviors when adding spin-orbit coupling effects. In addition, the calculated linear elastic parameters indicate the soft nature of the predicted monolayers. Moreover, our predictions for the thermoelectric properties of single layer XBi reveal that SiBi is a good thermoelectric material with increasing temperature. Overall, it is proposed that single layer XBi structures can be alternative, stable 2D single layers with varying electronic and thermoelectric properties.

Abstract: Inspired by the recent successful growth of Ti2C and Ti3C2 monolayers, here, we investigate the structural, electronic, and mechanical properties of functionalized Ti2C and Ti3C2 monolayers by means of density functional theory calculations. The results reveal that monolayers of Ti2C and Ti3C2 are dynamically stable metals. Phonon band dispersion calculations demonstrate that two-surface functionalization of Ti2C and Ti(3)C(2)via chalcogenides (S, Se, and Te), halides (F, Cl, Br, and I), and oxygen atoms results in dynamically stable novel functionalized monolayer materials. Electronic band dispersions and density of states calculations reveal that all functionalized monolayer structures preserve the metallic nature of both Ti2C and Ti3C2 except Ti2C-O-2, which possesses the behavior of an indirect semiconductor via full-surface oxygen passivation. In addition, it is shown that although halide passivated Ti3C2 structures are still metallic, there exist multiple Dirac-like cones around the Fermi energy level, which indicates that semi-metallic behavior can be obtained upon external effects by tuning the energy of the Dirac cones. In addition, the computed linear-elastic parameters prove that functionalization is a powerful tool in tuning the mechanical properties of stiff monolayers of bare Ti2C and Ti3C2. Our study discloses that the electronic and structural properties of Ti2C and Ti3C2 MXene monolayers are suitable for surface modification, which is highly desirable for material property engineering and device integration.

Abstract: Two-dimensional Transition Metal Dichalcogenides (TMDs) possessing extraordinary physical properties at reduced dimensionality have attracted interest due to their promise in electronic and optical device applications. However, TMD monolayers can show a broad range of different properties depending on their crystal phase; for example, H phases are usually semiconductors, while the T phases are metallic. Thus, controlling phase transitions has become critical for device applications. In this study, the energetically low-lying crystal structures of pristine and Janus TMDs are investigated by using ab initio Nudged Elastic Band and molecular dynamics simulations to provide a general explanation for their phase stability and transition properties. Across all materials investigated, the T phase is found to be the least stable and the H phase is the most stable except for WTe2, while the T' and T '' phases change places according to the TMD material. The transition energy barriers are found to be large enough to hint that even the higher energy phases are unlikely to undergo a phase transition to a more stable phase if they can be achieved except for the least stable T phase, which has zero barrier towards the T ' phase. Indeed, in molecular dynamics simulations the thermodynamically least stable T phase transformed into the T ' phase spontaneously while in general no other phase transition was observed up to 2100 K for the other three phases. Thus, the examined T ', T '' and H phases were shown to be mostly stable and do not readily transform into another phase. Furthermore, so-called mixed phase calculations considered in our study explain the experimentally observed lateral hybrid structures and point out that the coexistence of different phases is strongly stable against phase transitions. Indeed, stable complex structures such as metal-semiconductor-metal architectures, which have immense potential to be used in future device applications, are also possible based on our investigation.

Abstract: The added value of supervised Machine Learning (ML) methods to determine the Absolute Configuration (AC) of compounds from their Vibrational Circular Dichroism (VCD) spectra was explored. Among all ML methods considered, Random Forest (RF) and Feedforward Neural Network (FNN) yield the best performance for identification of the AC. At its best, FNN allows near-perfect AC determination, with accuracy of prediction up to 0.995, while RF combines good predictive accuracy (up to 0.940) with the ability to identify the spectral areas important for the identification of the AC. No loss in performance of either model is observed as long as the spectral sampling interval used does not exceed the spectral bandwidth. Increasing the sampling interval proves to be the best method to lower the dimensionality of the input data, thereby decreasing the computational cost associated with the training of the models.