Computational predictions of energy materials using density functional theory

Computational predictions of energy materials using density functional theory

TitleComputational predictions of energy materials using density functional theory
Publication TypeJournal Article
Year of Publication2016
AuthorsAnubhav Jain, Yongwoo Shin, Kristin A Persson
JournalNature Reviews Materials
Volume1
Issue1
Date Published01/2016
Abstract

 In the search for new functional materials, quantum mechanics is an exciting starting point. The fundamental laws that govern the behaviour of electrons have the possibility, at the other end of the scale, to predict the performance of a material for a targeted application. In some cases, this is achievable using density functional theory (DFT). In this Review, we highlight DFT studies predicting energy-related materials that were subsequently confirmed experimentally. The attributes and limitations of DFT for the computational design of materials for lithium-ion batteries, hydrogen production and storage materials, superconductors, photovoltaics and thermoelectric materials are discussed. In the future, we expect that the accuracy of DFT-based methods will continue to improve and that growth in computing power will enable millions of materials to be virtually screened for specific applications. Thus, these examples represent a first glimpse of what may become a routine and integral step in materials discovery. 

DOI10.1038/natrevmats.2015.4
Short TitleNat Rev Mater
Refereed DesignationRefereed