Machine-learning approaches for simulations of phase transitions in iron oxide nanoparticles

Envoyé par julien.lam 
Laboratory CEMES, CNRS and Université de Toulouse, 29 rue Jeanne Marvig, 31055 Toulouse Cedex, France

PhD supervisors: Julien Lam, Magali Benoit and Rémi Arras

Period: October 2022-October 2025

Funding: ANR JCJC NucleFOX

Description A key challenge in today’s nanotechnologies is the control of the structural properties during the nanoparticle synthesis. Reaching a targeted synthesis of nanoparticles requires a much better understanding of the involved complex mechanisms and in particular of crystal nucleation which corresponds to the initial structure formation. However, with the current state-of-the-art both in terms of experiments and simulations, controlling nucleation during nanoparticle synthesis remains a glass ceiling that needs to be overcome. In this project, we first introduce an original
simulation approach based on machine-learning that will allow us to perform large scale simulations while retaining the accuracy of quantum calculations. Then, prompted by the proposed numerical development, we will study the example of iron oxides nanoparticles which offers a rich playground for fundamental understanding while also being considered in numerous technological applications.

Your profile
- Master in physics, chemistry or materials science
- Good knowledge of statistical mechanics and computational physics
- Programming in C++/Python/Bash

What we offer?
- Funding for a Master internship starting Spring 2022
- Funding for a PhD thesis starting on October 2022
- Supervision by Julien Lam with the collaboration of Magali Benoit and Rémi Arras
- Working an innovative and exciting research institute (>60 permanent researchers)
- Training in soft skills (supervision, language learning and proposal writing)

How to apply? Application documents (CV, cover letter) should be emailed with the following
title ”Thesis Application NucleFOX”.

-”Perspective: Machine learning potentials for atomistic simulations”
J. Behler, J. Chem. Phys. 145, 17, 170901 (2016)
-”Measuring transferability issues in machine-learning force fields: The example of Gold-Iron inter-
actions with linearized potentials”
M. Benoit, J. Amodeo, S. Combettes, A. Roux, I. Khaled, J. Lam, Mach. Learn.: Sci. Technol. 2
025003 (2021)
-”Combining quantum mechanics and machine-learning calculations for anharmonic corrections to
vibrational frequencies”
J. Lam*, Saleh Abdul-Al, A-R Allouche*, J. Chem. Theory Comput. 13,3 (2020)
-“Out of equilibrium polymorph selection in nanoparticle freezing”
J. Amodeo, F. Pietrucci, J. Lam*, J. Phys. Chem. Lett. 11, 8060 (2020)