ANR PRC FASTRAIN

Dates:
October 2023 – October 2027

Project coordinator:
Etienne JANOD (PMN team)

Partner laboratories :

• Rennes Institute of Physics
• GREMAN Tours and ESRF Grenobles

IMN staff involved:
Laurent CARIO, Benoit CORRAZE, Julien TRANCHANT, Olivier HERNANDEZ, Jean-Yves MEVELLEC, Bernard HUMBERT and Florent PAWULA

Controlling ultrafast phase transitions in quantum materials by the athemic deformation wave route

Controlling the out-of-equilibrium state of a material by means of a light pulse opens up fascinating possibilities for reaching another macroscopic phase ultra-fast, which may be different from those following a thermal process.

The FASTRAIN project thus aims to understand the physical mechanisms of ultrafast phase transitions in quantum materials caused by a universal non-thermal mechanism, where dynamic strain waves are photoinduced directly into the material and trigger a phase transformation. This little-explored mechanism is, however, potentially present in all photoinduced transitions involving volumetric and/or ferroelastic deformation. In this project, we will focus on Mott insulators, a large class of correlated quantum materials that have been widely studied for half a century. We plan to demonstrate and rationalize the crucial role of deformation wave mechanisms on photo-induced transitions between Mott insulator and metal, which are inherently coupled to volume change. In this project, we will clarify the respective impact of symmetry breaking and volume change on the multiscale dynamics along the photoinduced transition pathway. In addition, we will explore the link between local precursors and macroscopic phase transformation. Finally, we will clarify the conditions favoring insulator-to-metal conversion, which can be complete in granular thin films and limited in massive crystals. The ideas developed in FASTRAIN will have an impact on other fields, notably the class of quantum materials exhibiting a phase transition involving elastic deformations. It will also shed light on our understanding and assess the ultimate performance of future innovative devices, such as hardware neural networks for artificial intelligence based on Mott insulators.