Every year, a committee of experts sits down with a tough job to do: from among all ICREA publications, they must find a handful that stand out from all the others. This is indeed a challenge. The debates are sometimes heated and always difficult but, in the end, a shortlist of 24 publications is produced. No prize is awarded, and the only additional acknowledge is the honour of being chosen and highlighted by ICREA. Each piece has something unique about it, whether it be a particularly elegant solution, the huge impact it has in the media or the sheer fascination it generates as a truly new idea. For whatever the reason, these are the best of the best and, as such, we are proud to share them here.


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  • Emergent quantum confinement effects in hybrid halide perovskites (2020)

    Goñi, Alejandro R. (CSIC - ICMAB)

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    Emergent quantum confinement effects in hybrid halide perovskites

    Beyond their excellent photovoltaic performance, hybrid organic–inorganic metal halide perovskites exhibit unique physical phenomena and emerging functionalities prompted by the interplay between organic and inorganic components. The discovery of intrinsic quantum confinement effects in the form of oscillations in the optical absorption of formamidinium lead triiodide (FAPbI3) thin films (see Fig. 1) is a vivid example of the surprising physical properties of these hybrid materials. The relevance of this work resides in that the discrete features can be interpreted as manifestation of intrinsic quantum confinement effects, unintentionally occurring in FAPbI3 thin films. As illustrated in the inset to Fig. 1, quantum confinement acts on the electronic band structure either by discretization of the energy spectrum due to full restriction of the movement of a particle confined in deep wells (infinite potential barrier model), or by leading to formation of mini-bands, in case the (finite) confining potential exhibits periodicity. These changes in the electronic landscape lead to peaks in the joint density of states, as probed in absorption.

    Writing in Nature ‘News & Views’, I make an important contribution to the interpretation of the observed quantum oscillations. I provide a simple but strong argument against ferroelectricity as the origin of such phenomenon. The oscillations are still apparent in the temperature range of the cubic phase of FAPbI3, for which ferroelectric order is strictly forbidden by symmetry. I also reinforce the interpretation based on phase polymorphism. I propose that a combination of strain build-up, changes in the surface energy and chemical bonding between perovskite and substrate can lead to quantum confinement by unintentional formation of inclusions of the perovskite phase surrounded by thin layers of a wide-gap, non-perovskitic phase. Understanding the mechanisms that lead to the quantum oscillations may suggest new routes for manipulating the electronic band structure of hybrid perovskites at the nanoscale to enhance optoelectronic performance by exploiting the confinement-induced discretization of the energy spectrum.

  • Let there be Glyght! (2020)

    Gorostiza Langa, Pau (IBEC)
    Rovira Virgili, Carme (UB)

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    Let there be Glyght!

    Glycine receptors (GlyRs) are essential for maintaining excitatory/inhibitory balance in neuronal circuits that control reflexes and rhythmic motor behaviors. We have developed Glyght, a GlyR ligand controlled with light. It is selective over other Cys-loop receptors, is active in vivo, and displays an allosteric mechanism of action. The photomanipulation of glycinergic neurotransmission opens new avenues to understanding inhibitory circuits in intact animals and to developing drug-based phototherapies. Last but not least, Glyght constitutes a novel molecular scaffold for glycine receptor pharmacology, and offers the opportunity to expand its limited molecular toolbox.


  • Building machine scientists (2020)

    Guimerà Manrique, Roger (URV)

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    Building machine scientists

    Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world. Think, for example, of Newton's law of gravitation and how its mathematical analysis has enabled us to predict astronomical phenomena with great accuracy and, perhaps more importantly, to understand central forces in general and, ultimately, the relationship between symmetry and conservation laws. With the data revolution, we may now be in a position to uncover new mathematical models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need "machine scientists" that are able to extract these closed-form mathematical models automatically from data.

    In a series of papers, ICREA professor Roger Guimerà and colleagues at Universitat Rovira i Virgili have developed a Bayesian machine  scientist. The Bayesian machine scientist assigns model plausibilities rigorously, and establishes its prior expectations about the models by learning from a large empirical corpus of mathematical expressions. It also explores the space of all possible closed-form mathematical models in ways that provide guarantees of eventually finding the correct one, if it exists.

    For systems for which models have been proposed before, the Bayesian machine scientist is able to uncover new models that are more plausible and more predictive than the old ones, without being more complex. The machine scientist is also able to uncover accurate, closed-form mathematical models for systems for which no closed-form model was known before. In particular, Guimerà and coworkers have applied the Bayesian machine scientist to a 90 year-old problem in turbulence. For this problem they provide closed-form solutions and, despite the fact that many partial solutions have been proposed, they find that the original approach proposed in the 1930s is the most plausible one so far, outperforming even the models proposed in the last few years.

  • Dark Galaxies: finally found exactly where we predicted (2020)

    Jiménez Tellado, Raúl (UB)

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    Dark Galaxies: finally found exactly where we predicted

    The existence of dark galaxies, those that should have none or negligible starlight, was predicted by us almost 25 years ago. At that time, none of these dark galaxies had been found and its existence reminded very doubtful as it seemed very difficult to stop the processes that trigger star formation. However, blind surveys in the radio, at rest wavelengths of 21cm, that search for the forbidden spin flip transtion of the hidrogen atom, have recently demonstrated that these galaxies do exist.They contain only hydrogen and almost no visible stars. In fact, their abundance is in perfect agreement with our theoretical predictions. Not only that, we have shown that their physical origin is the one proposed by us: the high spin of their host dark matter halo makes the baryons settle into a disk that is too difusse as to allow star formation to proceed as it does in our Milky Way. This origin is intimately related to the nature of dark matter, as primordial tidal forces in the hierarchical cold dark matter model (LCDM) do predict the right abundance for these galaxies. In this respect, dark galaxies are a confirmation of the current cosmological model.

  • Evidence of spectacular four-top-quark production at the LHC (2020)

    Juste, Aurelio (IFAE)

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    Evidence of spectacular four-top-quark production at the LHC

    During 2015-2018, the Large Hadron Collider (LHC) at CERN collided protons at a center-of-mass energy of 13 TeV, the highest energy ever reached by a particle accelerator.  One of the main goals of the ATLAS experiment at the LHC is to challenge the predictions of the Standard Model (SM), our most successful theory of elementary particles. To this end, a promising direction is the study of the top quark, the heaviest elementary particle known, with a mass close to that of a gold atom.

    The production of two top quarks and two antitop quarks (“four-top-quark” production) is a very rare process in the SM, happening only once every 1012 collisions. However, new particles beyond the SM can significantly enhance this rate. Once produced, each top quark decays into a W boson and a bottom quark, with the W boson decaying into a charged lepton (electron, muon, or tau) and a neutrino, or a quark-antiquark pair. This results in some of the most spectacular signatures ever produced at the LHC.

    The ATLAS Collaboration has recently reported strong evidence for the production of four top quarks a milestone reached by studying events with two same-charge leptons or three leptons, plus additional jets originating from the bottom quarks. The signal significance amounts to 4.3 standard deviations (s.d.), for an expected significance of 2.4 s.d. in the SM. This means that the measured rate is somewhat above the SM prediction, although still consistent with it within 1.7 s.d.

    Since 2015, researchers at IFAE, under A. Juste’s leadership, are playing a major role in the search for four-top-quark production in ATLAS. The team has not only contributed to the recent result, but is also completing a search for this process in a complementary channel featuring only one lepton or two opposite-charge leptons. The combination of both searches is expected to yield the observation of this process. Additional data from the next LHC run, to start in 2022, along with further developments in the analysis techniques, will improve the precision of this challenging measurement, and hopefully allow drawing definite conclusions on whether the breakdown of the SM is finally in sight.

  • Novel approach of using Unsupervised Machine Learning in Physics (2020)

    Lewenstein, Maciej Andrzej (ICFO)
    Acín Dal Maschio, Antonio (ICFO)

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    Novel approach of using Unsupervised Machine Learning in Physics

    A team of ICFO researchers reports in PRL an entirely new anomaly detection method capable of training a system in very few iterations. Machine Learning (ML) has the main goal of analyzing and interpreting data structures and patterns in order to learn from them, reason and carry out a decision-making task that is completely independent from human reasoning and engagement. Even though this field of study started in the mid 1900s, recent developments in the area have revolutionized the way on how we can process and find correlations in complex data.

    Contrary to supervised learning, unsupervised learning seeks to discover patterns or classify information in large data sets into categories without prior knowledge. That is, it does not have labeled outputs, which means that it basically infers the natural structure that a dataset may have and extracts categorized information from it. This learning has proved to be very efficient for identifying phases and phase transitions of many-body systems. In a study recently published in Physical Review Letters, ICFO researchers Korbinian Kottmann and Patrick Huembeli, led by ICREA Professors at ICFO Antonio Acín and Maciej Lewenstein report on a method that uses an unsupervised machine learning technique based on anomaly detection to automatically map out the phase diagram of a quantum many body system given unlabeled data.

    The following example is very illustrative of what they have achieved. In machine learning the most common and known classification task example is to discriminate, for instance, images of cats and dogs. In the study, the method the researchers use anomaly detection, which handles the classification task of discriminating dogs and everything that is not a dog, approaching the system is an entirely different perspective. The idea is to train a special neural network called an autoencoder to efficiently compress and reproduce images of dogs. If the network is later fed with images of cats, the network does not know how to efficiently compress the features of the cat image and it is possible to tell from the higher reconstruction loss that it is not a dog.