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  • Perspective
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Discovering and understanding materials through computation

Abstract

Materials modelling and design using computational quantum and classical approaches is by now well established as an essential pillar in condensed matter physics, chemistry and materials science research, in addition to experiments and analytical theories. The past few decades have witnessed tremendous advances in methodology development and applications to understand and predict the ground-state, excited-state and dynamical properties of materials, ranging from molecules to nanoscopic/mesoscopic materials to bulk and reduced-dimensional systems. This issue of Nature Materials presents four in-depth Review Articles on the field. This Perspective aims to give a brief overview of the progress, as well as provide some comments on future challenges and opportunities. We envision that increasingly powerful and versatile computational approaches, coupled with new conceptual understandings and the growth of techniques such as machine learning, will play a guiding role in the future search and discovery of materials for science and technology.

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Fig. 1: A bird’s-eye view of computational materials science.
Fig. 2: Quantum excitations in materials.
Fig. 3: Material simulations.

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Acknowledgements

We acknowledge support from the Center for Computational Study of Excited-State Phenomena in Energy Materials (C2SEPEM) at the Lawrence Berkeley National Laboratory, which is funded by the US Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division under contract number DE-AC02-05CH11231, as part of the Computational Materials Sciences Program.

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S.G.L. led the project. All authors contributed to the content and writing of the manuscript.

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Correspondence to Steven G. Louie.

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Peer review information Nature Materials thanks Emanuela Zaccarelli and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Louie, S.G., Chan, YH., da Jornada, F.H. et al. Discovering and understanding materials through computation. Nat. Mater. 20, 728–735 (2021). https://doi.org/10.1038/s41563-021-01015-1

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