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Future development of the modern nanoelectronics and its flagships internet of things and artificial intelligence as well as many related applications is largely associated with memristive elements. This technology offers a broad spectrum…
X-ray Photoelectron Spectroscopy (XPS) is a powerful technique to probe chemical states and interfacial processes in battery materials, but a quantitative interpretation is often hindered by the complex, heterogeneous microstructures that…
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…
The optimization of composition and processing to obtain materials that exhibit desirable characteristics has historically relied on a combination of scientist intuition, trial and error, and luck. We propose a methodology that can…
Metal components are extensively used as current collectors, anodes, and interlayers in lithium-ion batteries. Integrating these functions into one component enhances the cell energy density and simplifies its design. However, this…
We introduce an electronic structure based representation for quantum machine learning (QML) of electronic properties throughout chemical compound space. The representation is constructed using computationally inexpensive ab initio…
The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow…
We present a computational framework that integrates machine learning with high-throughput \textit{ab initio} calculations to screen over 2.8 million compounds for metallic transport. We identify several intermetallic candidates with…
Quantitative predictions of the Li intercalation voltage and of the electronic properties of rechargeable battery cathode materials are a substantial challenge for first-principles theory due to the possibility of (1) strong correlations…
Ultrahigh energy density batteries based on alpha-Li_xBN2 (1<x<3) positive electrode materials are predicted using density functional theory calculations. The utilization of the reversible LiBN2 + 2 Li+ + 2 e- -> Li3BN2 electrochemical cell…
The phase transition layered Li$_{0.5}$NiO$_2$ to spinel Li(NiO$_2$)$_2$ is a potential degradation pathway in LiNiO$_2$-based lithium-ion battery cathodes. We investigated the mechanism of this phase transformation from first principles.…
Batteries are a key enabling technology for the decarbonization of transport and energy sectors. The safe and reliable operation of batteries is crucial for battery-powered systems. In this direction, the development of accurate and robust…
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…
Machine-learned interatomic potentials can offer near first-principles accuracy but are computationally expensive, limiting their application to large-scale molecular dynamics simulations. Inspired by quantum mechanics/molecular mechanics…
In the context of the development of NMR Fermi contact shift calculations for assisting structural characterization of battery materials, we propose an accurate, efficient, and robust approach based on the use of an all electron method. The…
Several electrochemical and electrical energy storage devices are reported every day, with the claim of outperforming the established ones. The use of newer materials and recent advanced techniques to synthesize and/or assemble them into a…
Accurate parameter dependent electro-chemical numerical models for lithium-ion batteries are essential in industrial application. The exact parameters of each battery cell are unknown and a process of estimation is necessary to infer them.…
This paper proposes the linearized physics-based model of a lithium-ion battery that can be incorporated into the optimization framework for power system economic studies. The proposed model is a linear approximation of the single particle…
Transition-metal compounds pose serious challenges to first-principles calculations based on density-functional theory (DFT), due to the inability of most approximate exchange-correlation functionals to capture the localization of valence…
We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular…