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Electron charge density distribution of materials is one of the key quantities in computational materials science as theoretically it determines the ground state energy and practically it is used in many materials analyses. However, the…

Computational Physics · Physics 2019-11-13 Sheng Gong , Tian Xie , Taishan Zhu , Shuo Wang , Eric R. Fadel , Yawei Li , Jeffrey C. Grossman

The predictive performance screening of novel compounds can significantly promote the discovery of efficient, cheap, and non-toxic thermoelectric materials. Large efforts to implement machine-learning techniques coupled to materials…

Applied Physics · Physics 2020-12-07 Patrizio Graziosi , Chathurangi Kumarasinghe , Neophytos Neophytou

Ion-conducting solid electrolytes are widely used for a variety of purposes. Therefore, designing highly ion-conductive materials is in strongly demand. Because of advancement in computers and enhancement of computational codes, theoretical…

Computational Physics · Physics 2020-03-23 Masayuki Karasuyama , Hiroki Kasugai , Tomoyuki Tamura , Kazuki Shitara

We present a high-accuracy procedure for electronic structure calculations of strongly correlated materials. To address limitations in current electronic structure methods, we employ density functional theory in combination with the…

Various methods going beyond density-functional theory (DFT), such as DFT+U, hybrid functionals, meta-GGAs, GW, and DFT-embedded dynamical mean field theory (eDMFT), have been developed to describe the electronic structure of correlated…

Strongly Correlated Electrons · Physics 2020-07-16 Subhasish Mandal , Kristjan Haule , Karin M. Rabe , David Vanderbilt

Searching by chemical and structural analogy is one of the most commonly used and successful approaches to materials discovery. However, formulating this task for algorithmic implementation raises the question of how we define similar…

Materials Science · Physics 2026-02-27 Matthew A. H. Walker , Zibo Zhou , Junayd Ul Islam , Keith T. Butler

Using a computational crystal structure search in the Li-Fe-B ternary system, we predict a stable phase of Li3Fe8B8, featuring 1D channels that enable rapid Li-ion transport. Ab initio molecular dynamics simulations show that the Li-ion…

Fast ionic conduction is a defining property of solid electrolytes for all-solid-state batteries. Previous studies have suggested that liquid-like cation motion associated with fast ionic transport can disrupt crystalline symmetry, thereby…

The growing demand for efficient, high-capacity energy storage systems has driven extensive research into advanced materials for lithium-ion batteries. Among the various candidates, Wadsley-Roth (WR) niobates have emerged as a promising…

We present a generalizable scale-bridging computational framework that enables predictive modeling of insertion-type electrode materials from atomistic to device scales. Applied to sodium manganese hexacyanoferrate, a promising cathode…

Materials Science · Physics 2026-05-29 Yuan-Chi Yang , Eric Woillez , Quentin Jacquet , Ambroise van Roekeghem

The discovery of novel materials for thermoelectric energy conversion has potential to be accelerated by data-driven screening combined with high-throughput calculations. One way to increase the efficacy of successfully choosing a candidate…

Quantum mechanical methods like Density Functional Theory (DFT) are used with great success alongside efficient search algorithms for studying kinetics of reactive systems. However, DFT is prohibitively expensive for large scale…

Computational Physics · Physics 2022-09-02 Mathias Schreiner , Arghya Bhowmik , Tejs Vegge , Peter Bjørn Jørgensen , Ole Winther

With the rapid development of energy storage technology, high-performance solid-state electrolytes (SSEs) have become critical for next-generation lithium-ion batteries. These materials require high ionic conductivity, excellent…

Materials Science · Physics 2025-02-17 Hongwei Du , Jian Hui , Lanting Zhang , Hong Wang

Use of dielectric barrier discharge (DBD) as an ion source for sensitive chemical analysis is uncommon because barrier discharges generate excess noise due to spatial and temporal instability. This design uses contacted, crossed…

Instrumentation and Detectors · Physics 2016-02-22 Stephen L. Coy , Evgeny V. Krylov , Gary A. Eiceman , Isik Kanik

In optoelectronic applications, metal halide perovskites (MHPs) are compelling materials because of their highly tuneable and intensely competitive optical properties. Colloidal synthesis enables the controlled formation of various…

The thermodynamic description of dislocation glide in crystals depends crucially on the shape of the Peierls barrier that the dislocation has to overcome when moving in the lattice. While the height of this barrier can be obtained…

Materials Science · Physics 2014-09-04 R. Gröger

Based on molecular dynamics simulations of a lithium metasilicate glass we study the potential of bond valence sum calculations to identify sites and diffusion pathways of mobile Li ions in a glassy silicate network. We find that the bond…

Materials Science · Physics 2015-06-25 Christian Muller , Egbert Zienicke , Stefan Adams , Junko Habasaki , Philipp Maass

Future lithium-based batteries are expected to use solid electrolytes to achieve higher energy density and fast charge capabilities. The majority of solid electrolytes are thermodynamically unstable against layered oxide cathodes. Here, the…

Organic semiconductors are indispensable for today's display technologies in form of organic light emitting diodes (OLEDs) and further optoelectronic applications. However, organic materials do not reach the same charge carrier mobility as…

Soft Condensed Matter · Physics 2023-10-12 Patrick Reiser , Manuel Konrad , Artem Fediai , Salvador Léon , Wolfgang Wenzel , Pascal Friederich

Layered two-dimensional materials like graphene are highly appealing for lithium battery applications owing to their high surface-volume ratios. However, a critical issue that limits their practical applications is the confined motion of…

Materials Science · Physics 2019-01-23 Andrey Kistanov , Devesh Kripalani , Yongqing Cai , Sergey Dmitriev , Kun Zhou , Yong-Wei Zhang
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