Related papers: Structure-property relations of silicon oxycarbide…
Multicomponent transition metal carbides are promising for extreme-environment applications, but identifying compositions that are both synthesizable and hard remains challenging. We fine-tune the MACE machine-learned interatomic potential…
The simulation of borosilicate glasses is challenging due to the composition and temperature dependent coordination state of boron atoms. Here, we present a newly developed machine learning optimized classical potential for molecular…
Without our ability to model and manipulate the band structure of semiconducting materials, the modern digital computer would be impractically large, hot, and expensive. In the undergraduate QM curriculum, we studied the effect of spatially…
It has been a long-standing materials science challenge to establish structure-property relations in amorphous solids. Here we introduce a rotation-variant local structure representation that enables different predictions for different…
Polymer-derived ceramics combine the thermal stability of ceramics with the versatile properties of carbon domains, but modeling their atomic-scale evolution during processing remains elusive due to the limitations of traditional…
Rapidly determining structure-property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of…
We predict the existence of new two dimensional silicon carbide nanostructure employing ab initio density-functional theory calculations. These structures are composed of tetragonal and hexagonal rings with C-C and Si-C bonds arranged in a…
Silicon carbide (SiC) is an attractive semiconductor material for applications in power electronic devices. However, fabrication of a high-quality SiC/SiO${}_2$ interface has been a challenge. It is well-known that there is a great…
In this letter we propose a new methodology for crystal structure prediction, which is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials actively learning on-the-fly. Our methodology allows for an…
Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges…
The electronic structure in the new transition metal carbide Ti4SiC3 has been investigated by bulk-sensitive soft x-ray emission spectroscopy and compared to the well-studied Ti3SiC2 and TiC systems. The measured high-resolution Ti L, C K…
Two-dimensional (2D) materials have emerged as promising platforms for quantum technologies and optoelectronics, with defects playing a crucial role in their properties. We present a comprehensive density functional theory study of silicon…
Plasma-activated bonding of SiCN films offers high bonding strength at the hybrid-bonding interface, thereby enhancing mechanical reliability. Although experimental studies have shown that the interfacial bonding properties of SiCN films…
Bulk amorphous materials have been studied extensively and are widely used, yet their atomic arrangement remains an open issue. Although they are generally believed to be Zachariasen continuous random networks, recent experimental evidence…
Multiscale modelling is a new paradigm that has emerged in recent times to study the well-known problem of the process-structure-property relationship in the area of materials science and engineering. For obtaining the desired performance…
Semiconductivity and superconductivity are remarkable quantum phenomena that have immense impact on science and technology, and materials that can be tuned, usually by pressure or doping, to host both types of quantum states are of great…
We report the density-functional calculations that systematically clarify the stable forms of carbon-related defects and their energy levels in amorphous SiO$_2$ using the melt-quench technique in molecular dynamics. Considering the…
Carbon fiber and graphene-based nanostructures such as carbon nanotubes (CNTs) and defective structures have extraordinary potential as strong and lightweight materials. A longstanding bottleneck has been lack of understanding and…
In this review, we present our recent computational work on carbon-based nanostructured composites. These materials consist of carbon crystallites embedded in an amorphous carbon matrix and are modeled here through classical and…
The structural optimization and energetics studies of SiC graphitic-like structures have been investigated theoretically in the context of formations of stable graphitic-like layer structures, single- and multi-walled nanotubes using the…