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Presentation

L’IRSAMC (The Institute of Research on Complex Atomic and Molecular Systems) is a federation of four laboratories (LCAR, LCPQ, LPCNO, LPT), in physics and fundamental chemistry whose research activities are supported both by the Université Paul Sabatier, the CNRS and INSA

Publications of 4 research laboratories

  • Hal-LCAR. - Laboratory Collisions Clusters Reactivity, from 1990 until todays
  • Hal-LCPQ. - Quantum Chemistry and Physics Laboratory, from 2007 until todays
  • Hal-LPCNO. - Physics and Chemistry of Nano Objects Laboratory, from 2006 until todays
  • Hal-LPT.- Theoretical Physics Laboratory, from 2003 until todays

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  • Better visibility of the scientific productivity
  • Open access, accessible everywhere
  • Possibility of establishing lists of publications

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  • The deposit of a document requires the agreement of its authors, and it must respect editor policy.
  • If no agreement has been spent, deposit only the bibliographical note
  • Beware ! Once a document is put online, it cannot be withdrawn. New versions may be added.

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Last submission

[tel-03157002] Machine learning and quantum phases of matter

 (05/03/2021)  
My PhD thesis presents three applications of machine learning to condensed matter theory. Firstly, I will explain how the problem of detecting phase transitions can be rephrased as an image classification task, paving the way to the automatic mapping of phase diagrams. I tested the reliability of this approach and showed its limits for models exhibiting a many-body localized phase in 1 and 2 dimensions. Secondly, I will introduce a variational representation of quantum many-body ground-states in the form of neural-networks and show our results on a constrained model of hardcore bosons in 2d using variational and projection methods. In particular, we confirmed the phase diagram obtained independently earlier and extends its validity to larger system sizes. Moreover we also established the ability of neural-network quantum states to approximate accurately solid and liquid bosonic phases of matter. Finally, I will present a new approach to quantum error correction based on the same techniques used to conceive the best Go game engine. We showed that efficient correction strategies can be uncovered with evolutionary optimization algorithms, competitive with gradient-based optimization techniques. In particular, we found that shallow neural-networks are competitive with deep neural-networks.

[hal-03156675] Nanoparticle Ripening : A Versatile Approach for the Size and Shape Control of Metallic Iron Nanoparticles

 (05/03/2021)  

[hal-02165756] Chemical Ordering in Bimetallic FeCo Nanoparticles: From a Direct Chemical Synthesis to Application As Efficient High-Frequency Magnetic Material

 (04/03/2021)  

[hal-02468242] Spin adaptation with determinant-based selected configuration interaction

 (03/03/2021)  

[tel-03151448] Auto-assemblage des nanoparticules métalliques orienté par des polymères peptidiques

 (25/02/2021)  

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