Related papers: Metric-driven search for structurally stable inorg…
The promise of enhanced performance has motivated the study of one dimensional nanomaterials, especially aligned carbon nanotubes (A-CNTs), for the reinforcement of polymeric materials. While early work has shown that CNTs have remarkable…
We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a ``combinatorial engine'' that constructs potential compounds by occupying all possible Wyckoff…
A general force-perturbation-based criterion for solid instability is proposed, which can predict instability including crease without priori knowledge of instability configuration. The crease instability is analyzed in detail, we found…
We have developed an efficient crystal structure prediction (CSP) method for desired chemical compositions, specifically suited for compounds featuring recurring molecules or rigid bodies. We applied this method to two metal chalcogenides:…
Crystal structures define how matter is organized at the atomic level. In the realm of crystalline inorganic materials, new structure types are rarely found, and most experimentally-realized structural motifs were established decades ago.…
We study a newly predicted layered-ternary compound Ti4SiN3 in its {\alpha}- and {\beta}-phases. We calculate their mechanical, electronic and optical properties and then compare these with those of other compounds M4AX3 (M = V, Ti, Ta; A =…
The thermodynamic stabilities of various phases of the nitrides of the platinum metal elements are systematically studied using density functional theory. It is shown that for the nitrides of Rh, Pd, Ir and Pt two new crystal structures, in…
Crystal structure prediction (CSP), which aims to predict the three-dimensional atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. However, given the composition in a unit…
Crystal symmetry plays a fundamental role in determining its physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization behavior, and mechanical strength. Almost all known crystalline…
We provide a short review of the progress made in the past decade with functional QCD in the description of the phase structure of QCD. We summarise the most important technical aspects of the framework, discuss strategies for truncations…
Metal-organic frameworks (MOFs) combine high porosity with structural fragility, raising important questions about their mechanical stability. We develop a rigidity-based framework in which spring networks parameterized by UFF4MOF are used…
Prediction of the stable crystal structure for multinary (ternary or higher) compounds with unexplored compositions demands fast and accurate evaluation of free energies in exploring the vast configurational space. The machine-learning…
Polynitrogen compounds have attracted great interest due to their potential applications as high energy density materials. Most recently, a rich variety of alkali polynitrogens (R_{x}N_{y}; R=Li, Na, and Cs) have been predicted to be stable…
Data mining is a recognized predictive tool in a variety of areas ranging from bioinformatics and drug design to crystal structure prediction. In the present study, an electronic structure implementation has been combined with structural…
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…
The phase diagram and equation of state of dense nitrogen are of interest in understanding the fundamental physics and chemistry under extreme conditions, including planetary processes, and in discovering new materials. We predict several…
Prediction of high-$T_{\rm{c}}$ superconductivity in hole-doped Li$_x$BC two decades ago has brought about an extensive effort to synthesize new materials with honeycomb B-C layers, but the thermodynamic stability of Li-B-C compounds…
With the advent of self-driving labs promising to synthesize large numbers of new materials, new automated tools are required for checking potential duplicates in existing structural databases before a material can be claimed as novel. To…
A fundamental challenge in materials science pertains to elucidating the relationship between stoichiometry, stability, structure, and property. Recent advances have shown that machine learning can be used to learn such relationships,…
The prediction of material structure from chemical composition has been a long-standing challenge in natural science. Although there have been various methodological developments and successes with computer simulations, the prediction of…