Insights into one-body density matrices using deep learning.

@article{Wetherell2020InsightsIO,
  title={Insights into one-body density matrices using deep learning.},
  author={Jack Wetherell and Andrea Costamagna and Matteo Gatti and Lucia Reining},
  journal={arXiv: Computational Physics},
  year={2020}
}
  • Jack Wetherell, Andrea Costamagna, +1 author Lucia Reining
  • Published 2020
  • Mathematics, Physics
  • arXiv: Computational Physics
  • The one-body reduced density matrix (1-RDM) of a many-body system at zero temperature gives direct access to many observables, such as the charge density, kinetic energy and occupation numbers. It would be desirable to express it as a simple functional of the density or of other local observables, but to date satisfactory approximations have not yet been found. Deep learning is the state-of the art approach to perform high dimensional regressions and classification tasks, and is becoming widely… CONTINUE READING

    Figures and Tables from this paper.

    References

    Publications referenced by this paper.
    SHOWING 1-7 OF 7 REFERENCES

    Principal Component Analysis

    • Heng Tao Shen
    • Mathematics, Computer Science
    • Encyclopedia of Database Systems
    • 2009

    American Nuclear Society, International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, Vol. 1 (Citeseer

    • J. W. Hines
    • 1996

    Principal component analysis,” in International Encyclopedia of Statistical Science, edited by M

    • I. Jolliffe
    • Lovric (Springer Berlin Heidelberg, Berlin, Heidelberg,
    • 2011

    The London, Edinburgh, and Dublin

    • K. Pearson
    • Philosophical Magazine and Journal of Science
    • 1901