Feasibility study of Mg storage in a bilayer silicene anode via application of an external electric field.

With the goal of developing a Si-based anode for Mg-ion batteries (MIBs) that is both efficient and compatible with the current semiconductor industry, the current research utilized classical Molecular Dynamics (MD) simulation in investigating the intercalation of a Mg2+ ion under an external electric field (E-field) in a 2D bilayer silicene anode (BSA). First principles density functional theory calculations were used to validate the implemented EDIP potentials. Our simulation shows that there exists an optimum E-field value in the range of 0.2-0.4 V Å-1 for Mg2+ intercalation in BSA. To study the effect of the E-field on Mg2+ ions, an exhaustive spread of investigations was carried out under different boundary conditions, including calculations of mean square displacement (MSD), interaction energy, radial distribution function (RDF), and trajectory of ions. Our results show that the Mg2+ ions form a stable bond with Si in BSA. The effects of E-field direction and operating temperature were also investigated. In the X-Y plane in the 0°-45° range, 15° from the X-direction was found to be the optimum direction for intercalation. The results of this work also suggest that BSA does not undergo drastic structural changes during the charging cycles with the highest operating temperature being ∼300 K.

Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels.

Three probabilistic methodologies are developed for predicting the long-term creep rupture life of 9-12 wt%Cr ferritic-martensitic steels using their chemical and processing parameters. The framework developed in this research strives to simultaneously make efficient inference along with associated risk, i.e., the uncertainty of estimation. The study highlights the limitations of applying probabilistic machine learning to model creep life and provides suggestions as to how this might be alleviated to make an efficient and accurate model with the evaluation of epistemic uncertainty of each prediction. Based on extensive experimentation, Gaussian Process Regression yielded more accurate inference ([Formula: see text] for the holdout test set) in addition to meaningful uncertainty estimate (i.e., coverage ranges from 94 to 98% for the test set) as compared to quantile regression and natural gradient boosting algorithm. Furthermore, the possibility of an active learning framework to iteratively explore the material space intelligently was demonstrated by simulating the experimental data collection process. This framework can be subsequently deployed to improve model performance or to explore new alloy domains with minimal experimental effort.

Machine learning assisted prediction of the Young's modulus of compositionally complex alloys.

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young's modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young's modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

Impact of iodine antisite (IPb) defects on the electronic properties of the (110) CH3NH3PbI3 surface.

The power conversion efficiency of perovskite solar cells can be significantly improved if recombination losses and hysteresis effects, often caused by the presence of structural and chemical defects present at grain boundaries and interfaces, can be minimized during the processing of photoactive layers. As a crucial first step to address this issue, we performed density functional theory calculations to evaluate the electronic structure of the energetically favored (110) perovskite surface in the presence of the widely reported IPb antisite defects. Our calculations indicate that the nature of trap states formed is different for the perovskite surface with exposed methylammonium (MAI) and lead iodide (PbI2) terminating groups. While, in MAI terminated surfaces, IPb antisite defects lead to shallow states close to the valence band, both deep and shallow states are created in the bandgap region in the PbI2 terminated surface. Furthermore, we determined contribution from individual atoms to the trap states and inferred that the trap states originate from the clusters of iodine atoms that are formed near the defect site. The exact nature of the defect state is strongly correlated with the atomic structure of these clusters and can be potentially tuned by controlling the processing conditions of the perovskite film.

A Systematic Study on the Structural and Optical Properties of Vertically Aligned Zinc Oxide Nanorods Grown by High Pressure Assisted Pulsed Laser Deposition Technique.

In this study, we synthesize high quality vertically aligned ZnO (VAZO) nanorods on silicon, sapphire, and indium tin oxide (ITO) substrates by using pulsed laser deposition (PLD) technique at high growth pressure (0.3 Torr). Systematic changes in structural and optical properties of VAZO nanorods are studied by varying the substrate temperature (500-600 °C) and number of pulsed laser shots during the deposition. ZnO nanoparticles deposited at high pressure act as nucleation sites, eliminating requirement of catalyst to fabricate VAZO nanorods. Two sharp ZnO peaks with high intensity correspond to the (0002) and (0004) planes in X-ray diffraction pattern confirm the growth of ZnO nanorods, oriented along the c-axis. Scanning Electron Microscopy (SEM) images indicate a regular arrangement of vertically aligned hexagonal closed pack nano-structures of ZnO. The vertical alignment of ZnO nanorods is also supported by the presence of E2 (high) and A1 (LO) modes in Raman spectra. We can tune the diameter of VAZO nanorods by changing growth temperature and annealing environments. Photoluminescence spectroscopy illustrates reduction in defect level peak intensities with increase in diameter of VAZO nanorods. This study signifies that high pressure PLD technique can be used more efficiently for controlled and efficient growth of VAZO nanorods on different substrates.