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  • Review Article
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Computational predictions of energy materials using density functional theory

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.

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Figure 1: Procedure to screen for materials properties using density functional theory calculations.
Figure 2: Computational design of high-rate-capability Li-ion battery materials.
Figure 3: Prediction of superconductivity in FeB4.
Figure 4: Screening of copolymers for organic photovoltaics.
Figure 5: LiZnSb: a candidate thermoelectric material suggested by computation.

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Acknowledgements

This work was intellectually led by the Materials Project supported by the US Department of Energy Office of Science, Office of Basic Energy Sciences Department under Contract No. DE-AC02-05CH11231. Y.S. thanks the Battery Materials Research Program under the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Vehicle Technologies of the US Department of Energy.

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Jain, A., Shin, Y. & Persson, K. Computational predictions of energy materials using density functional theory. Nat Rev Mater 1, 15004 (2016). https://doi.org/10.1038/natrevmats.2015.4

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