Meetings/Workshops on Condensed Matter Physics and Materials in Sweden
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Effective Theories of Quantum Phases of Matter
06 May 2019 - 31 May 2019 • Stockholm, Sweden
Physical systems look different when observed at different resolutions: what appears as a continuum liquid to the naked eye becomes a cluster of jiggling atoms when observed at the resolution of an electron microscope. Effective field theory provides a description of physics in terms of degrees of freedom appropriate to a given resolution. Over the last couple of decades, physicists have developed effective field theory tools which, to a large extent, unify fields as diverse as atomic and condensed-matter physics, particle and nuclear physics, and cosmology. The ensuing interaction between different branches of physics has never been as fruitful as it is now. Numerous physical phenomena, first predicted and studied in high-energy physics, have found their realization in novel materials. On the other hand, the richness of condensed-matter physics provides a lasting source of inspiration for new developments in high-energy physics and cosmology. Extremely useful in general, the effective field theory tools become indispensable for quantum phases of matter whose low-energy physics is driven by collective excitations, either due to the presence of spontaneous symmetry breaking or due to strong microscopic interactions. The aim of this program is to give a new impulse to a further development of this exciting interdisciplinary field. We bring together leading practitioners working on effective theories of quantum phases of matter across several branches of physics. Our goal is to map out important open problems with broad relevance and look for new directions towards their solution, to reinvigorate existing collaborations and foster new connections.
Topological Quantum Matter: From Low-Temperature Physics to Non-Equilibrium Dynamics
23 Aug 2019 • Stockholm, Sweden
Recent advances in the band theory of crystalline materials have singled out topology as a key ingredient in the modern classification of matter, with major impact on measurable electronic properties. Topological band theory has also grown into an emerging paradigm in many areas of physics, and is now used to characterize metamaterials, including photonic, atomic, acoustic, and elastic systems, in both the quantum and classical regimes. As our understanding of topology in physics widens, the incorporation of out-of-equilibrium phenomena is gaining in importance.
Machine Learning for Quantum Matter
26 Aug 2019 - 28 Aug 2019 • Stockholm, Sweden
Machine learning has entered the field of quantum matter with applications covering quantum materials and the many-body problem. For example, interpretable and computationally-efficient machine learning models are able to capture the structure-property relationship in materials science. In case of the many-body problem, machine learning architectures provide versatile wavefunctions that lead to accurate results and prove to be more flexible than traditional methods. Conversely, methods in physics have also influenced the development of machine learning methods in the case of tensor networks. The workshop will feature talks by leading experts combined with the talks of younger participants to present a broad picture of the activities and best ideas on the use of ML methods in quantum matter.
Quantum Materials for Dark Matter Detection
09 Sep 2019 - 13 Sep 2019 • Stockholm, Sweden
In the search for dark matter (DM), one particular focus is on light and ultra-light dark matter, i.e. sub-GeV mass dark matter from a hidden dark sector with a new force interacting with the standard model or ultra-light DM with mass range from 10-22 eV to keV. The arguably most popular example of the latter class is the axion, invoked to solve the apparent absence of CP violation in Quantum Chromo Dynamics. Detection of these particles poses new challenges to potential sensor materials: very small energy depositions, magnetic properties and anisotropic response to particle interactions for example become crucial requirements. The challenge of finding suitable materials fits well with recent developments in solid state physics: Motivated by the exponential growth of computational power and the resulting data, we witness the rapid adoption of functional materials prediction within the framework of materials informatics. Here, methods adapted from computer science based on data-mining and machine learning are applied to identify materials with requested target properties.
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Last updated: 15 May 2019