Related papers: A symmetry-oriented crystal structure prediction m…
We report a corrected crystal structure for the CePt2In7 superconductor, refined from single crystal x-ray diffraction data. The corrected crystal structure shows a different Pt-In stacking along the c-direction in this layered material…
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…
We search for new superhard B-N-O compounds with an iterative machine learning (ML) procedure, where ML models are trained using sample crystal structures from evolutionary algorithm. We first use cohesive energy to evaluate the…
A growing body of theoretical and experimental evidence suggests that inert gases (He, Ne, Ar, Kr, Xe, Rn) become less and less inert under increasing pressure. Here we use the ab initio evolutionary algorithm to predict stable compounds of…
Due to the use of huge number of sensors and the increasing use of communication networks, cyber-physical systems (CPS) are becoming vulnerable to cyber-attacks. The ever-increasing complexity of CPS bring up the need for data-driven…
Crystallization of the amorphous phases into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to synthesis and development of new materials in the laboratory.…
The discovery of novel solid-state materials, such as electrocatalysts, super-ionic conductors, or photovoltaic materials, plays a critical role in addressing various global challenges. It has, for instance, the potential to significantly…
We develop a method combining machine learning (ML) and density functional theory (DFT) to predict low-energy polymorphs by introducing physics-guided descriptors based on structural distortion modes. We systematically generate crystal…
Crystalline structure prediction is an essential prerequisite for designing materials with targeted properties. Yet, it is still an open challenge in materials design and drug discovery. Despite recent advances in computational materials…
In an effort to develop a rapid, reliable, and cost-effective method for predicting the structure of single-phase high entropy alloys, a Graph Neural Network (ALIGNN-FF) based approach was introduced. This method was successfully tested on…
We study Crystal Structure Prediction, one of the major problems in computational chemistry. This is essentially a continuous optimization problem, where many different, simple and sophisticated, methods have been proposed and applied. The…
Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal structures, or mixtures thereof, in synthesized materials. Materials science experts excel at solving simple systems…
Accurate molecular crystal structure prediction is a fundamental goal in academic and industrial condensed matter research and polymorphism is arguably the biggest obstacle on the way. We tackle this challenge in the difficult case of the…
The compound-tunable embedding potential (CTEP) method is applied to study actinide substitutions in the niobate crystals YNbTiO$_6$ and CaNb$_2$O$_6$. Two one-center clusters centered on Ca and Y are built and 20 substitutions of Ca and Y…
Ab initio molecular dynamic method within the framework of density functional theory is applied to analyze the structural and electronic properties of crystalline molecular hydrogen at temperature 100\,K. Pressure, pair correlation function…
Possible crystalline modifications of chemical compounds at low temperatures correspond to local minima of the energy landscape. Determining these minima via simulated annealing is one method for the prediction of crystal structures, where…
The computational singular perturbation (CSP) method is an algorithm which iteratively approximates slow manifolds and fast fibers in multiple-timescale dynamical systems. Since its inception due to Lam and Goussis, the convergence of the…
The relative stability of three-dimensional icosahedral quasicrystals in multi-component systems has been investigated based on a coupled-mode Swift-Hohenberg model with two-length-scales. A recently developed projection method, which…
We present a detailed quantum mechanical non empirical DFT investigation of the energy-optimized geometries, phase stabilities and electronic properties of bulk Pt3N, PtN and PtN2 in a set of twenty different crystal structures. Structural…
The composition, crystal structure and precipitation crystallography of a newly found precipitate are characterized by a Cs-corrected scanning transmission electron microscopy (STEM). The composition of the plate-like precipitate could be…