Reconstructing the quantum critical fan of strongly correlated systems via quantum correlationsFrérot, Irénée;Roscilde, Tommaso
doi: 10.1038/s41467-019-08324-9pmid: 30718513
Abstract: Albeit occurring at zero temperature, quantum critical phenomena are known to have a huge impact on the finite-temperature phase diagram of strongly correlated systems -- an aspect which gives experimental access to their observation. In particular the existence of a gapless, zero-temperature quantum critical point is known theoretically to induce the existence of an extended region in parameter space -- the so-called quantum critical fan -- characterized by power-law temperature dependences of all observables, with exponents related to those of the quantum critical point. Identifying experimentally the quantum critical fan and its crossovers to the other regions (renormalized classical, quantum disordered) remains nonetheless a big challenge. Focusing on paradigmatic models of quantum phase transitions, here we show that quantum correlations - captured by the quantum variance of the order parameter (I. Frérot and T. Roscilde, Phys. Rev. B {\bf 94}, 075121 (2016)) - exhibit the temperature scaling associated with the quantum critical regime over an extended parameter region, much broader than that revealed by ordinary correlations, and with well-defined crossovers to the other regimes. The link existing between the quantum variance and the dynamical order-parameter susceptibility paves the way to an experimental reconstruction of the quantum critical fan using \emph{e.g.} spectroscopy on strongly correlated quantum matter.
Shaping dynamical folding and misfolding pathways in mechanical metamaterialsStern, Menachem;Jayaram, Viraaj;Murugan, Arvind
doi: 10.1038/s41467-018-06720-1pmid: 30327460
Abstract: The design of desired behaviors in mechanical metamaterials has produced remarkable advances but has generally neglected two aspects - the inevitable presence of undesired behaviors and the role of dynamics in avoiding such behaviors. Inspired by similar hurdles in molecular self-assembly and protein folding, we derive design principles to shape dynamical folding and misfolding pathways in disordered mechanical systems. We show that such pathways, i.e., sequences of states traversed at a finite rate, are determined by the bifurcation structure of configuration space which, in turn, can be tuned using imperfections such as stiff joints. We apply these ideas to completely eliminate the exponentially many ways of misfolding a self-folding sheet by making some creases stiffer than others. Our approach also shows how folding at different rates can controllably target different desired behaviors.
Special temperatures in frustrated ferromagnetsBovo, L.;Twengström, M.;Petrenko, O. A.;Fennell, T.;Gingras, M. J. P.;Bramwell, S. T.;Henelius, P.
doi: 10.1038/s41467-018-04297-3pmid: 29784922
Abstract: The description and detection of unconventional magnetic states such as spin liquids is a recurring topic in condensed matter physics. While much of the efforts have traditionally been directed at geometrically frustrated antiferromagnets, recent studies reveal that systems featuring competing antiferromagnetic and ferromagnetic interactions are also promising candidate materials. We find that this competition leads to the notion of special temperatures, analogous to those of gases, at which the competing interactions balance, and the system is quasi-ideal. Although induced by weak perturbing interactions, these special temperatures are surprisingly high and constitute an accessible experimental diagnostic of eventual order or spin liquid properties. The well characterised Hamiltonian and extended low-temperature susceptibility measurement of the canonical frustrated ferromagnet Dy$_2$Ti$_2$O$_7$ enables us to formulate both a phenomenological and microscopic theory of special temperatures for magnets. Other members of this new class of magnets include kapellasite Cu$_3$Zn(OH)$_6$Cl$_2$ and the spinel GeCo$_2$O$_4$.
Chemical Shifts in Molecular Solids by Machine LearningParuzzo, Federico M.;Hofstetter, Albert;Musil, Félix;De, Sandip;Ceriotti, Michele;Emsley, Lyndon
doi: 10.1038/s41467-018-06972-xpmid: 30374021
Abstract: The calculation of chemical shifts in solids has enabled methods to determine crystal structures in powders. The dependence of chemical shifts on local atomic environments sets them among the most powerful tools for structure elucidation of powdered solids or amorphous materials. Unfortunately, this dependency comes with the cost of high accuracy first-principle calculations to qualitatively predict chemical shifts in solids. Machine learning methods have recently emerged as a way to overcome the need for explicit high accuracy first-principle calculations. However, the vast chemical and combinatorial space spanned by molecular solids, together with the strong dependency of chemical shifts of atoms on their environment, poses a huge challenge for any machine learning method. Here we propose a machine learning method based on local environments to accurately predict chemical shifts of different molecular solids and of different polymorphs within DFT accuracy (RMSE of 0.49 ppm ( 1 H), 4.3ppm ( 13 C), 13.3 ppm ( 15 N), and 17.7 ppm ( 17 O) with $R^2$ of 0.97 for 1 H, 0.99 for 13 C, 0.99 for 15 N, and 0.99 for 17 O). We also demonstrate that the trained model is able to correctly determine, based on the match between experimentally-measured and ML-predicted shifts, structures of cocaine and the drug 4-[4-(2-adamantylcarbamoyl)-5-tert-butylpyrazol-1-yl]benzoic acid in an chemical shift based NMR crystallography approach.
A general approach for the synthesis of two-dimensional binary compoundsShivayogimath, Abhay;Thomsen, Joachim Dahl;Mackenzie, David M. A.;Geisler, Mathias;Kling, Jens;Balogh, Zoltan Imre;Crovetto, Andrea;Whelan, Patrick R.;Bøggild, Peter;Booth, Timothy J.
doi: 10.1038/s41467-019-11075-2pmid: 31273207
Abstract: Only a few of the vast range of potential two-dimensional materials have been isolated or synthesised to date. Typically, 2D materials are discovered by mechanically exfoliating naturally occurring bulk crystals to produce atomically thin layers, after which a material-specific vapour synthesis method must be developed to grow interesting candidates in a scalable manner. Here we show a general approach for synthesising thin layers of two-dimensional binary compounds. We apply the method to obtain high quality, epitaxial MoS2 films, and extend the principle to the synthesis of a wide range of other materials - both well-known and never-before isolated - including transition metal sulphides, selenides, tellurides, and nitrides. This approach greatly simplifies the synthesis of currently known materials, and provides a general framework for synthesising both predicted and unexpected new 2D compounds.
Prioritizing network communitiesZitnik, Marinka;Sosic, Rok;Leskovec, Jure
doi: 10.1038/s41467-018-04948-5pmid: 29959323
Abstract: Uncovering modular structure in networks is fundamental for systems in biology, physics, and engineering. Community detection identifies candidate modules as hypotheses, which then need to be validated through experiments, such as mutagenesis in a biological laboratory. Only a few communities can typically be validated, and it is thus important to prioritize which communities to select for downstream experimentation. Here we develop CRank, a mathematically principled approach for prioritizing network communities. CRank efficiently evaluates robustness and magnitude of structural features of each community and then combines these features into the community prioritization. CRank can be used with any community detection method. It needs only information provided by the network structure and does not require any additional metadata or labels. However, when available, CRank can incorporate domain-specific information to further boost performance. Experiments on many large networks show that CRank effectively prioritizes communities, yielding a nearly 50-fold improvement in community prioritization.
Non-Hermitian Quantum Sensing: Fundamental Limits and Non-Reciprocal ApproachesLau, Hoi-Kwan;Clerk, Aashish A.
doi: 10.1038/s41467-018-06477-7pmid: 30333486
Abstract: Unconventional properties of non-Hermitian systems, such as the existence of exceptional points, have recently been suggested as a resource for sensing. The impact of noise and utility in quantum regimes however remains unclear. In this work, we analyze the parametric-sensing properties of linear coupled-mode systems that are described by effective non-Hermitian Hamiltonians. Our analysis fully accounts for noise effects in both classical and quantum regimes, and also fully treats a realistic and optimal measurement protocol based on coherent driving and homodyne detection. Focusing on two-mode devices, we derive fundamental bounds on the signal power and signal-to-noise ratio for any such sensor. We use these to demonstrate that enhanced signal power requires gain, but not necessarily any proximity to an exceptional point. Further, when noise is included, we show that non-reciprocity is a powerful resource for sensing: it allows one to exceed the fundamental bounds constraining any conventional, reciprocal sensor. We analyze simple two-mode non-reciprocal sensors that allow this parametrically-enhanced sensing, but which do not involve exceptional point physics.
Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistryBartel, Christopher J.;Millican, Samantha L.;Deml, Ann M.;Rumptz, John R.;Tumas, William;Weimer, Alan W.;Lany, Stephan;Stevanović, Vladan;Musgrave, Charles B.;Holder, Aaron M.
doi: 10.1038/s41467-018-06682-4pmid: 30301890
Abstract: The Gibbs energy, G, determines the equilibrium conditions of chemical reactions and materials stability. Despite this fundamental and ubiquitous role, G has been tabulated for only a small fraction of known inorganic compounds, impeding a comprehensive perspective on the effects of temperature and composition on materials stability and synthesizability. Here, we use the SISSO (sure independence screening and sparsifying operator) approach to identify a simple and accurate descriptor to predict G for stoichiometric inorganic compounds with ~50 meV/atom (~1 kcal/mol) resolution, and with minimal computational cost, for temperatures ranging from 300-1800 K. We then apply this descriptor to ~30,000 known materials curated from the Inorganic Crystal Structure Database (ICSD). Using the resulting predicted thermochemical data, we generate thousands of temperature-dependent phase diagrams to provide insights into the effects of temperature and composition on materials synthesizability and stability and to establish the temperature-dependent scale of metastability for inorganic compounds.