No abstract

No abstract

The ability to spatially modulate the electronic properties of solids has led to landmark discoveries in condensed matter physics as well as new electronic applications. Although crystals of strongly correlated metals exhibit a diverse set of electronic ground states, few approaches to spatially modulating their properties exist. Here we demonstrate spatial control over the superconducting state in mesoscale samples of the canonical heavy-fermion superconductor CeIrIn5. We use a focused ion beam (FIB) to pattern crystals on the microscale, which tailors the strain induced by differential thermal contraction into specific areas of the device. The resulting non-uniform strain fields induce complex patterns of superconductivity due to the strong dependence of the transition temperature on the strength and direction of strain. Electrical transport and magnetic imaging of devices with different geometry show that the obtained spatial modulation of superconductivity agrees with predictions based on finite element simulations. These results present a generic approach to manipulating electronic order on micrometer length scales in strongly correlated matter.Heavy fermion materials exhibit a rich competition between metallic, superconducting, and magnetically ordered ground states. The ability to locally control electronic properties within these materials would enable the design of new correlated states both for fundamental research and for applications. Alternative approaches to achieve spatially modulated correlations involve modulating the

We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy and scope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners. Expected final online publication date for the Annual Review of Biophysics, Volume 50 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

† Authors contributed equally to this work.URu2Si2 exhibits a clear phase transition at THO = 17.5 K to a low-temperature phase known as "hidden order" (HO). Even the most basic information needed to construct a theory of this state-such as the number of components in the order parameter-has been lacking. Here we use resonant ultrasound spectroscopy (RUS) and machine learning to determine that the order parameter of HO is one-dimensional (singlet), ruling out a large class of theories based on twodimensional (doublet) order parameters. This strict constraint is independent of any microscopic mechanism, and independent of other symmetries that HO may break. Our technique is general for second-order phase transitions, and can discriminate between nematic (singlet) versus loop current (doublet) order in the high-Tc cuprates, and conventional (singlet) versus the proposed px + ipy (doublet) superconductivity in Sr2RuO4. The machine learning framework we develop should be readily adaptable to other spectroscopic techniques where missing resonances confound traditional analysis, such as NMR.