Related papers: Data-Driven Prediction of Complex Crystal Structur…
We investigate the pressure-temperature ($p$-$T$) phase diagram of elemental lithium (Li) up to multiterapascal (TPa) pressures using ab-initio random structure search (AIRSS) and density functional theory (DFT). At zero temperature, beyond…
We discover many new crystalline solid materials with fast single crystal Li ion conductivity at room temperature, discovered through density functional theory simulations guided by machine learning-based methods. The discovery of new solid…
Lithium - the lightest alkali metal - exhibits unexpected structures and electronic behaviour at high pressures. As the heavier alkalis, Li is bcc at ambient pressure and transforms first to fcc (at 7.5 GPa). The post-fcc high-pressure form…
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…
The lithium-carbon binary system possesses a broad range of chemical compounds, which exhibit fascinating chemical bonding characteristics that give rise diverse and technologically important properties. While lithium carbides with various…
Evolutionary crystal structure prediction proved to be a powerful approach for studying a wide range of materials. Here, we present a specifically designed algorithm for the prediction of the structure of complex crystals consisting of…
Predicting phase stabilities of crystal polymorphs is central to computational materials science and chemistry. Such predictions are challenging because they first require searching for potential energy minima and then performing arduous…
The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy…
Crystal structure search is a long-standing challenge in materials design. We present a dataset of more than 100,000 structural relaxations of potential battery anode materials from randomized structures using density functional theory…
Evolutionary structure searches are used to predict a new class of compounds in the lithium--rich region of the lithium/hydrogen phase diagram under pressure. First principles computations show that LimH, 4<m<9, are stabilized with respect…
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…
Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through…
Prediction of stable crystal structures at given pressure-temperature conditions, based only on the knowledge of the chemical composition, is a central problem of condensed matter physics. This extremely challenging problem is often termed…
Crystalline materials, with symmetrical and periodic structures, exhibit a wide spectrum of properties and have been widely used in numerous applications across electronics, energy, and beyond. For crystalline materials discovery,…
We have developed an efficient and reliable methodology for crystal structure prediction, merging ab initio total-energy calculations and a specifically devised evolutionary algorithm. This method allows one to predict the most stable…
We investigate crystalline thermodynamic stable lithium silicides phases (LixSiy) with density functional theory (DFT) and a force-field method based on modified embedded atoms (MEAM) and compare our results with experimental data. This…
Controlling nanostructure from molecular, crystal lattice to the electrode level remains as arts in practice, where nucleation and growth of the crystals still require more fundamental understanding and precise control to shape the…
Determining the stability of chemical compounds is essential for advancing material discovery. In this study, we introduce a novel deep neural network model designed to predict a crystal's formation energy, which identifies its stability…
Lithium thiophosphates (LPS) with the composition (Li$_2$S)$_x$(P$_2$S$_5$)$_{1-x}$ are among the most promising prospective electrolyte materials for solid-state batteries (SSBs), owing to their superionic conductivity at room temperature…
Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A…