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dc.contributor.authorUrban, Alexander
dc.contributor.authorSeo, Dong-Hwa
dc.contributor.authorCeder, Gerbrand
dc.identifier.citationUrban , A , Seo , D-H & Ceder , G 2016 , ' Computational understanding of Li-ion batteries ' , npj Computational Materials , vol. 2 , 16002 .
dc.descriptionThis work was supported primarily by the U.S. Department of Energy (DOE) under Contract No. DE-FG02-96ER45571.en
dc.description.abstractOver the last two decades, computational methods have made tremendous advances, and today many key properties of lithium-ion batteries can be accurately predicted by first principles calculations. For this reason, computations have become a cornerstone of battery-related research by providing insight into fundamental processes that are not otherwise accessible, such as ionic diffusion mechanisms and electronic structure effects, as well as a quantitative comparison with experimental results. The aim of this review is to provide an overview of state-of-the-art ab initio approaches for the modelling of battery materials. We consider techniques for the computation of equilibrium cell voltages, 0-Kelvin and finite-temperature voltage profiles, ionic mobility and thermal and electrolyte stability. The strengths and weaknesses of different electronic structure methods, such as DFT+U and hybrid functionals, are discussed in the context of voltage and phase diagram predictions, and we review the merits of lattice models for the evaluation of finite-temperature thermodynamics and kinetics. With such a complete set of methods at hand, first principles calculations of ordered, crystalline solids, i.e., of most electrode materials and solid electrolytes, have become reliable and quantitative. However, the description of molecular materials and disordered or amorphous phases remains an important challenge. We highlight recent exciting progress in this area, especially regarding the modelling of organic electrolytes and solid-electrolyte interfaces.
dc.relation.ispartofnpj Computational Materialsen
dc.subjectQD Chemistryen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectMaterials Science(all)en
dc.subjectComputer Science Applicationsen
dc.subjectModelling and Simulationen
dc.subjectMechanics of Materialsen
dc.subjectSDG 7 - Affordable and Clean Energyen
dc.titleComputational understanding of Li-ion batteriesen
dc.typeJournal itemen
dc.contributor.institutionUniversity of St Andrews. School of Chemistryen
dc.description.statusPeer revieweden

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