Related papers: Data-Driven Prediction of Complex Crystal Structur…
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…
In a recently published article Mayo et al.[Chemistry of Materials 2017, 29, 5787] presented the ground state crystal structures of various experimentally unknown Li-Sn intermetallic compounds at ambient pressure (~0 GPa) and 0 K…
Evolutionary crystal structure prediction searches have been employed to explore the ternary Li-F-H system at 300 GPa. Metastable phases were uncovered within the static lattice approximation, with LiF$_3$H$_2$, LiF$_2$H, Li$_3$F$_4$H,…
Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual…
The Li-Sn binary system has been the focus of extensive research because it features Li-rich alloys with potential applications as battery anodes. Our present re-examination of the binary system with a combination of machine learning and ab…
Large-scale computational surveys are increasingly used to map the landscape of stable crystalline materials. We report a high-throughput energy screening of inorganic crystals that enumerates binary and ternary compositions up to a…
LiNi0.5Mn1.5O4, which has a spinel framework structure, is a promising candidate for the cathode material of next-generation lithium-ion batteries with high energy density. We investigate the structural transition in LixNi0.5Mn1.5O4 (0 <= x…
Reliable and robust methods of predicting the crystal structure of a compound, based only on its chemical composition, is crucial to the study of materials and their applications. Despite considerable ongoing research efforts, crystal…
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…
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…
Polynomial machine learning potentials (MLPs) based on polynomial rotational invariants have been systematically developed for various systems and applied to efficiently predict crystal structures. In this study, we propose a robust…
Understanding ionic transport in halide solid electrolytes is essential for advancing next-generation solid-state batteries. This work demonstrates the effectiveness of fine-tuning the Crystal Hamiltonian Graph Network (CHGNet) universal…
Understanding the phase stability of elemental lithium (Li) is crucial for optimizing its performance in lithium-metal battery anodes, yet this seemingly simple metal exhibits complex polymorphism that requires proper accounting for quantum…
Understand the nature of liquid structure and its evolution under different conditions is a major challenge in condensed physics and materials science. Here, we report a pressure-induced structure change spanning a wide pressure range in…
We propose an approach for exploiting machine learning to approximate electronic fields in crystalline solids subjected to deformation. Strain engineering is emerging as a widely used method for tuning the properties of materials, and this…
Crystal structures of $\textrm{CLi}_4$ compounds are explored through \emph{ab} \emph{initio} evolutionary methodology. Phase transition from metal to semimetal and semiconductor, and eventually to insulator with increasing pressure are…
Oxide Li-conducting solid-state electrolytes (SSEs) offer excellent chemical and thermal stability but typically exhibit lower ionic conductivity than sulfides and chlorides. This motivates the search for new oxide materials with enhanced…
The physical and chemical characteristics of cathodes used in batteries are derived from the lithium-ion phosphate cathodes crystalline arrangement, which is pivotal to the overall battery performance. Therefore, the correct prediction of…
Li2FeSiO4 is an important alternative cathode for next generation Li-ion batteries due to its high theoretical capacity (330 mA h/g). However, its development has faced significant challenges arising from structural complexity and poor…
We present a high-throughput computational screening for fast lithium-ion conductors to identify promising materials for application in all solid-state electrolytes. Starting from more than 30,000 Li-containing experimental structures…