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Volume 13 Issue 5, May 2017

The success of machine-learning techniques in handling big data sets has now been exploited in the classification of condensed-matter phases and phase transitions. Letter p431; Letter p435; News & Views p420 IMAGE: JUAN CARRASQUILLA COVER DESIGN: BETHANY VUKOMANOVIC

Editorial

  • Like all journals based on Nature's editorial philosophy, Nature Physics relies on a dedicated team of full-time editors. We briefly describe who they are and what they do.

    Editorial

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Thesis

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Books & Arts

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Research Highlights

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News & Views

  • A recent burst of activity in applying machine learning to tackle fundamental questions in physics suggests that associated techniques may soon become as common in physics as numerical simulations or calculus.

    • Lenka Zdeborová
    News & Views
  • There is growing evidence for the kinetics of homogeneous nucleation being a multi-step process. Colloid experiments and simulations now suggest that heterogeneous nucleation is no exception.

    • Rajesh Ganapathy
    • Ajay K. Sood
    News & Views
  • The spectroscopic observations of the very early stages of a supernova provide a glimpse into its environment prior to the explosion.

    • Norbert Langer
    News & Views
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Progress Article

  • Over the past decade, remarkable progress has occurred in the physics of closed quantum systems away from equilibrium, culminating in the recent experimental realization of so-called time crystals. This Progress Article surveys these developments.

    • R. Moessner
    • S. L. Sondhi
    Progress Article
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Letter

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Article

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Measure for Measure

  • Alberto Moscatelli surveys a series of experiments on the electron g-factor that marked the departure from the Dirac equation and contributed to the development of quantum electrodynamics.

    • Alberto Moscatelli
    Measure for Measure
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