The symposium will be held on July 13th (Wednesday), see the program below. (Other GEFES symposia @Bienal RSEF).

Florian Macquardt (Max Planck Institute for the Science of Light) will give a plenary talk on this topic on July 13th. Find more information in the Bienal webpage.

Artificial intelligence (AI) is emerging as a new paradigm for the advancement of a variety of research areas within the physical sciences, from statistical and quantum physics to high-energy physics and cosmology. Among them, condensed matter physics is one of the most suitable areas to boost the cross-fertilization between AI and physics. On one hand, it is expected that AI could contribute to the next-generation of fundamental and applied condensed matter physics breakthroughs. On the other hand, new AI algorithms and techniques could be developed based on condensed matter physics insights. This session will present the latest advances in the application of AI to condensed matter physics, with a lineup of speakers that will cover the current most relevant and active areas of this emerging topic.

Organizing Committee

Jorge Bravo-Abad, Universidad Autónoma de Madrid

Alexandre Dauphin, ICFO

Program (Wednesday, July 13th)

15:30-16:20 Eliska Greplova (TU Delft) (invited) A data-driven perspective on quantum matter
16:20-16:40 Sergio G. Rodrigo (Universidad de Zaragoza) Design of plasmonic superconducting transition-edge-sensors with neural networks
16:40-17:00 Daniel del Pozo  (Universitat de Barcelona) Transition metals oxidation state determination through Electron Energy-Loss Spectra and Support Vector Machines
17:00-18:00 Posters and Coffee
18:00-18:20 Borja Requena (ICFO) Certificates of quantum many-body properties assisted by machine learning
18:20-18:40 Pablo Serna (Universidad de Murcia) Machine learning and displacement transformations to locate the many body localization transition
18:40-19:00 Alejandro Jose Uria-Alvarez  (Universidad Autonónoma de Madrid) Deep learning for disordered topological insulators through their entanglement spectrum
19:00-19:20 Juan José García (Universidad Autonónoma de Madrid) Deep learning for the modeling and inverse design of radiative heat transfer


P1 Pedro Moronta (CSIC) Can random laser networks learn?
P2 Miguel Dalmau (Universidad Autónoma de Madrid & CSIC) Graph convolutional neural networks for accelerating property predictions of small organic molecules
2022-05-23T14:51:20+02:00Etiquetas: |