Related papers: A symmetry-oriented crystal structure prediction m…

To accelerate development of innovative materials, their modelings and predictions with useful functionalities are of vital importance. Here, based on a recently developed crystal structure prediction method, we find a new family of stable…

Modern compound prediction methods can efficiently screen large numbers of crystal structure phases and direct the experimental search for new materials. One of the most challenging problems in alloy theory is the identification of stable…

Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of the inorganic crystals, we present a methodology that exploits Moment Tensor Potentials and active learning (based on maxvol…

We used ab initio random structure searching (AIRSS) to investigate polymorphism in C3N4 carbon nitride as a function of pressure. Our calculations reveal new framework structures, including a particularly stable chiral polymorph of space…

Solving black-box optimization problems with Ising machines is increasingly common in materials science. However, their application to crystal structure prediction (CSP) is still ineffective due to symmetry agnostic encoding of atomic…

In this study, we present a novel approach along with the needed computational strategies for efficient and scalable feature engineering of the crystal structure in compounds of different chemical compositions. This approach utilizes a…

Experimental progress finally reached the metallic solid hydrogen phase, which was predicted by Wigner and Huntington over 80 years ago. However, the different structures in the phase diagram are still been debated due to the difficulty of…

At ambient pressure, sodium, chlorine, and their only known compound NaCl, have well-understood crystal structures and chemical bonding. Sodium is a nearly-free-electron metal with the bcc structure. Chlorine is a molecular crystal,…

We present a theoretical study of classical Wigner crystals in two- and three-dimensional isotropic parabolic traps aiming at understanding and quantifying the configurational uncertainty due to the presence of multiple stable…

Crystal structure prediction is now playing an increasingly important role in discovery of new materials. Global optimization methods such as genetic algorithms (GA) and particle swarm optimization (PSO) have been combined with first…

Novel superhard materials, especially those with superior thermal and chemical stability, are needed to replace diamond. Carbon nitrides (C-N), which are likely to possess these charac- teristics and have even been expected to be harder…

A molecular solid C$_{50}$Cl$_{10}$ with possible crystalline structures, including the hexagonal-close-packed (hcp) phase, the face-centered cubic (fcc) phase, and a hexagonal monolayer, is predicted in terms of first-principles…

A key challenge for computational discovery of electrocatalytic materials is the reliable prediction of thermodynamic stability in aqueous environment and under different electrochemical conditions. In this work, we first evaluate the…

We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping algorithm. We demonstrate this method by producing a…

Structural search and feature extraction are a central subject in modern materials design, the efficiency of which is currently limited, but can be potentially boosted by machine learning (ML). Here, we develop an ML-based…

Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on…

Machine Learning (ML) techniques are revolutionizing the way to perform efficient materials modeling. Nevertheless, not all the ML approaches allow for the understanding of microscopic mechanisms at play in different phenomena. To address…

The existence of approximately 30 noncentrosymmetric metals (NCSM) suggests a contraindication between crystal structures without inversion symmetry and metallic behavior. Those containing oxygen are especially scarce. Here we propose and…

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either…

We present our findings of a large-scale screening for new synthesizable materials in five M-Sn binaries, M = Na, Ca, Cu, Pd, and Ag. The focus on these systems was motivated by the known richness of M-Sn properties with potential…