TREASURF - Research project

TREASURF is an interdisciplinary project focusing on the development of novel machine learning approaches for the prediction of surface functionalization of different families of metals and metal alloys by (femto)laser irradiation. The ability to predict the micro- or nanopatterns
induced by laser functionalization is a crucial challenge for an optimal use of surface properties. In this context, machine learning methods have been subject of a growing interest recently but they have to cope with of limited amounts of experimental data due to the very high acquisition costs. In the TREASURF project, we propose to address this problem by developing methods able to transfer the knowledge of a prediction model learned from a given metal or alloy to another, different but sharing certain properties. Re-training a new model is not a plausible hypothesis, mainly because of the difficulties involved in acquiring large quantities of data (laser irradiation + nanoscale imaging). This project is therefore situated in a difficult context of "frugal" learning. Our aim is to focus primarily on topographic predictions for two or more different alloy families. The project also envisages taking into account variability due to chemical changes to guide the transfer process. The advances made in this project will enable us to better characterize the impact of laser-matter interaction with the perspective of designing new surface functionalizations on various novel metal alloys, opening the door to new application prospects in numerous societal challenges related to health, energy, space, nuclear
or defense.