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Published July 2020 | Accepted Version + Published + Supplemental Material
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Ground-State Properties of the Hydrogen Chain: Dimerization, Insulator-to-Metal Transition, and Magnetic Phases

Abstract

Accurate and predictive computations of the quantum-mechanical behavior of many interacting electrons in realistic atomic environments are critical for the theoretical design of materials with desired properties, and they require solving the grand-challenge problem of the many-electron Schrödinger equation. An infinite chain of equispaced hydrogen atoms is perhaps the simplest realistic model for a bulk material, embodying several central themes of modern condensed-matter physics and chemistry while retaining a connection to the paradigmatic Hubbard model. Here, we report a combined application of cutting-edge computational methods to determine the properties of the hydrogen chain in its quantum-mechanical ground state. Varying the separation between the nuclei leads to a rich phase diagram, including a Mott phase with quasi-long-range antiferromagnetic order, electron density dimerization with power-law correlations, an insulator-to-metal transition, and an intricate set of intertwined magnetic orders.

Additional Information

© 2020 The author(s). Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. (Received 17 March 2020; revised 14 June 2020; accepted 13 July 2020; published 14 September 2020) We thank T. Giamarchi, N. Marzari, A. Rubio, and M. van Schilfgaarde for helpful discussions. This work was supported by the Simons Foundation as part of the Simons Collaboration on the Many-Electron Problem. The Flatiron Institute is a division of the Simons Foundation. Computations were carried out on facilities supported by the Scientific Computing Core at the Flatiron Institute (M. M. and H. S.) and by the U.S. Department of Energy, National Energy Research Scientific Computing Center (Z. H. C., P. H., M. M., and U. R.), on the Pauling cluster at the California Institute of Technology (Z. H. C., P. H., M. M., and U. R.), and on the Storm and SciClone Clusters at the College of William and Mary (F. M. and M. M.). M. M. acknowledges the IBM Research Cognitive Computing Cluster service for providing resources that have contributed to the research results reported within this paper. G. K. C. acknowledges support from the National Science Foundation under Grant No. OAC 1931258. F. M. acknowledges support from the National Natural Science Foundation of China under Grant No. 11674027. S. S. and C. G. acknowledge support from PRIN 2017BZPKSZ and computational resources from CINECA PRACE 2019204934. S. W., E. M. S., R. S., and N. C. acknowledge support from DOE under Grant No. DE-SC0008696 and the Swiss National Science Foundation. M. M., C. G., F. M., Z.-H. C., and R. S. contributed equally to this work.

Attached Files

Published - PhysRevX.10.031058

Accepted Version - 1911.01618.pdf

Supplemental Material - si.pdf

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Additional details

Created:
August 19, 2023
Modified:
October 20, 2023