Scope
Spectroscopy is central to the natural sciences and engineering as one of the primary methods for investigating the real world, particularly in characterising the properties of molecules and materials. Varying the energy of the spectroscopic probe gives access to the internal states and dynamics of physical systems. Experimental and theoretical spectroscopy methods go hand in hand to discover new materials and their functions. In addition, machine learning, which is currently revolutionizing the sciences and engineering, has the potential to trigger a paradigm shift, towards algorithm and data driven spectroscopy.
The EUSpecLab spectroscopy school at PSI (the third in a series of EUSpecLab training schools) is geared at PhD students in physics and materials science with basic knowledge in theoretical modelling of modern materials. The school will discuss contemporary research topics in condensed matter and how advanced experimental and theoretical spectroscopic methods are applied to understand complex phenomena and to discover new materials. Topics range from structure determination and ground state properties to advanced concepts like strongly correlated electrons, topology, structural, chemical or dynamic disorder, initial and final state effects and response to static or ultrafast external applied fields.
Topics
- Advanced spectroscopy
- Angle, spin and time resolved photoelectron spectroscopy (ARPES)
- Optical spectroscopy
- Pump-probe techniques
- Condensed matter physics
- Atomic and electronic structure
- Spin-orbit interactions, topology
- Electron-electron and electron-phonon interactions
- Strongly correlated electron systems
- Quantum materials
- Transition-metal dichalcogenides
- Transition metal oxides
- Molecules on surfaces
- Machine learning methods
- Materials discovery
- Atomistic simulations
- Machine learning potentials
- Experiment and Theory
- Large research facilities
Confirmed Invited Speakers
- Jörg Behler, RU Bochum (DE), Four Generations of Machine Learning Potentials
- Michele Ceriotti, EPFL Lausanne (CH), Modeling vibrational and electronic spectroscopies with atomic-scale machine learning
- J. Hugo Dil, EPFL Lausanne (CH), Spin-resolved ARPES: from topological materials to quantum interference
- Roberto Gunnella, U Camerino (IT), Photoelectron diffraction at low kinetic energy as a high sensitive tool in 2D van der Waals systems
- Nicola Marzari, EPFL Lausanne (CH), Materials cloud
- Claude Monney, U Fribourg (CH), Time-resolved ARPES: Observing photoinduced phase transitions and excited states
- Daniele Passerone, Empa Dübendorf (CH), Computational science in a materials and nano-science laboratory
- Nicholas C. Plumb, PSI (CH), Viewing quasiparticle behavior through the ARPES spectral function
- Milan Radovic, PSI (CH), Spectroscopy images exotic electronic systems and experiences interplay between different emerging phenomena in Transition Metal Oxides
- Nicolas Schmid, ZHAW Winterthur (CH), Machine learning in NMR spectroscopy
- Michael Schüler, U Fribourg (CH), Time-resolved ARPES
- Ari P. Seitsonen, CNRS Paris (F), Molecules on surfaces
- Vladimir N. Strokov, PSI (CH), Advanced topics in ARPES: Final-state effects and high-energy photoemission
- Alea M. Tokita, RU Bochum (DE), How to train a neural network potential
- Chiara Trovatello, Columbia U (US), Exploring the nonlinear optical response of 2D semiconductors
- Tian Xie, Microsoft Research (UK), MatterGen: a generative model for inorganic materials design
Contributions
All participants are invited to present a poster. Some of the contributions may be selected for an oral presentation.
