Development of hybrid coarse-grained atomistic models for rapid assessment of local structuring of polymeric semiconductors

Decades of work in the field of computational study of semiconducting polymers using atomistic models illustrate the challenges of generating equilibrated models for this class of materials. While adopting a...

Multi-Terminal Nanotube Junctions: Modeling and Structure-Property Relationship

Carbon nanotube based multi-terminal junction configurations are of great interest because of the potential aerospace and electronic applications. Multi-terminal carbon nanotube junction has more than one carbon nanotube meeting at a point to create a 2D or 3D structure. Accurate atomistic models of such junctions are essential for characterizing their thermal, mechanical and electronic properties via computational studies. In this work, computational methodologies that uses innovative Computer-Aided Design (CAD) based optimization strategies and remeshing techniques are presented for generating such topologically reliable and accurate models of complex multi-terminal junctions (called 3-, 4-, and 6-junctions). This is followed by the prediction of structure-property relationship via study of thermal conductivity and mechanical strength using molecular dynamics simulations. We observed high degradation in the thermal and mechanical properties of the junctions compared to pristine structures which is attributed to high concentration of non-hexagonal defects in the junction. Junctions with fewer defects have better thermal transport capabilities and higher mechanical strengths, suggesting that controlling the number of defects can significantly improve inherent features of the nanostructures.

Pushing the boundaries of lithium battery research with atomistic modelling on different scales

Computational modelling is a vital tool in the research of batteries and their component materials. Atomistic models are key to building truly physics-based models of batteries and form the foundation of the multiscale modelling chain, leading to more robust and predictive models. These models can be applied to fundamental research questions with high predictive accuracy. For example, they can be used to predict new behaviour not currently accessible by experiment, for reasons of cost, safety, or throughput. Atomistic models are useful for quantifying and evaluating trends in experimental data, explaining structure-property relationships, and informing materials design strategies and libraries. In this review, we showcase the most prominent atomistic modelling methods and their application to electrode materials, liquid and solid electrolyte materials, and their interfaces, highlighting the diverse range of battery properties that can be investigated. Furthermore, we link atomistic modelling to experimental data and higher scale models such as continuum and control models. We also provide a critical discussion on the outlook of these materials and the main challenges for future battery research.

Stability and molecular pathways to the formation of spin defects in silicon carbide

Spin defects in wide-bandgap semiconductors provide a promising platform to create qubits for quantum technologies. Their synthesis, however, presents considerable challenges, and the mechanisms responsible for their generation or annihilation are poorly understood. Here, we elucidate spin defect formation processes in a binary crystal for a key qubit candidate—the divacancy complex (VV) in silicon carbide (SiC). Using atomistic models, enhanced sampling simulations, and density functional theory calculations, we find that VV formation is a thermally activated process that competes with the conversion of silicon (VSi) to carbon monovacancies (VC), and that VV reorientation can occur without dissociation. We also find that increasing the concentration of VSi relative to VC favors the formation of divacancies. Moreover, we identify pathways to create spin defects consisting of antisite-double vacancy complexes and determine their electronic properties. The detailed view of the mechanisms that underpin the formation and dynamics of spin defects presented here may facilitate the realization of qubits in an industrially relevant material.

Crack Length Effect on the Fracture Behavior of Single-Crystals and Bi-Crystals of Aluminum

Molecular dynamics simulations of cracked nanocrystals of aluminum were performed in order to investigate the crack length and grain boundary effects. Atomistic models of single-crystals and bi-crystals were built considering 11 different crack lengths. Novel approaches based on fracture mechanics concepts were proposed to predict the crack length effect on single-crystals and bi-crystals. The results showed that the effect of the grain boundary on the fracture resistance was beneficial increasing the fracture toughness almost four times for bi-crystals.

First-Principles Simulation of Dielectric Function in Biomolecules

The dielectric spectra of complex biomolecules reflect the molecular heterogeneity of the proteins and are particularly important for the calculations of electrostatic (Coulomb) and electrodynamic (van der Waals) interactions in protein physics. The dielectric response of the proteins can be decomposed into different components depending on the size, structure, composition, locality, and environment of the protein in general. We present a new robust simulation method anchored in rigorous ab initio quantum mechanical calculations of explicit atomistic models, without any indeterminate parameters to compute and gain insight into the dielectric spectra of small proteins under different conditions. We implement this methodology to a polypeptide RGD-4C (1FUV) in different environments, and the SD1 domain in the spike protein of SARS-COV-2. Two peaks at 5.2–5.7 eV and 14.4–15.2 eV in the dielectric absorption spectra are observed for 1FUV and SD1 in vacuum as well as in their solvated and salted models.