Related papers: Exploring and machine learning structural instabil…
We demonstrate a machine learning-based approach which predicts the properties of crystal structures following relaxation based on the unrelaxed structure. Use of crystal graph singular values reduces the number of features required to…
A two-dimensional phononic crystal (PC) can exhibit longitudinal-mode negative energy refraction on its lowest (acoustical) frequency pass band. The effective elastodynamic properties of a typical PC are calculated and it is observed that…
In the search for MgB2-like phonon-mediated superconductors we have carried out a systematic density functional theory study of the Ca-B system, isoelectronic to Mg-B, at ambient and gigapascal pressures. A remarkable variety of candidate…
High-throughput screening of large hypothetical databases of metal-organic frameworks (MOFs) can uncover new materials, but their stability in real-world applications is often unknown. We leverage community knowledge and machine learning…
Slow dynamic nonlinearity is widely observed in brittle materials with complex heterogeneous or cracked microstructures. It is seen in rocks, concrete and cracked glass blocks. Unconsolidated structures show the behavior as well: aggregates…
In this article, we present our results on bilayers assembled upon strategic placement of Cd$_6$Se$_6$ clusters. These bilayers are studied for their stability and electronic structure with the help of density functional theory and are…
The prediction of crystal properties is essential for understanding structure-property relationships and accelerating the discovery of functional materials. However, conventional approaches relying on experimental measurements or density…
Principles of design to create dynamically stable transition metal, lanthanide, and actinide based low-dimensional borides are presented. A charge transfer analysis of donor metal atoms to electron deficient honeycombed B lattices allows to…
Two mechanisms of changes from 2D to 3D (D = dimensionality) involving 2D C(sp2) trigonal paving to C(sp3) tetrahedral stacking are proposed through puckering of the 2D layers on one hand and interlayer insertion of extra C on the other…
Crystal structure prediction has traditionally relied on prototype-based seeding, approaches that often bias sampling toward known low-energy basins and overlook metastable polymorphs with unconventional symmetries. Here, we introduce…
DFT is a widely used method to compute properties of materials, which are often collected in databases and serve as valuable starting points for further studies. In this article, we present the Materials Cloud Three-Dimensional Structure…
Recently, the two dimensional (2D) materials have become a potential candidates for various technological applications in spintronics and optoelectronics. In the present study, the structural, electronic, and phase stability of 2D layered…
We review the theory of second--order (ferro--)elastic phase transitions, where the order parameter consists of a certain linear combination of strain tensor components, and the accompanying soft mode is an acoustic phonon. In…
The unique structure of two-dimensional (2D) Dirac crystals, with electronic bands linear in the proximity of the Brillouin-zone boundary and the Fermi energy, creates anomalous situations where small Fermi-energy perturbations are known to…
Low-dimensional chaotic systems such as the Lorenz-63 model are commonly used to benchmark system-agnostic methods for learning dynamics from data. Here we show that learning from noise-free observations in such systems can be achieved up…
The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending…
A simple model of harmonic vibrations in topologically disordered systems, such as glasses and supercooled liquids, is studied analytically by extending Euclidean Random Matrix Theory to include vector vibrations. Rather generally, it is…
Two dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties. While several thousand 2D materials have been…
Two-dimensional (2D) photonic crystals offer strong control over the propagation of light through their bands. Theoretical methods for computing the band structure in 2D are well-established and fast because 2D photonic crystals are…
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…