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[PhD] Machine-learning approaches for simulation ... (No replies)
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A Master internship followed with a PhD position is available to start at the beginning of 2022. The position is located in the "Centre for Materials Elaboration and Structural Studies" located in Toulouse (France).
Project:
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 qualifications:
The position is opened to strong applicants with backgrounds in physics, chemistry and/or engineering. Research skills of particular interests include Machine-learning methodology, C++ programming, Molecular dynamics simulations and Software developments.
Supervision and teaching:
Retained candidate will benefit from the supervision of Dr. Julien Lam and Dr. Magali Benoit. Together, their research is focused on material modeling using many types of computational techniques. He/She will gain experience in a large number of research domains, especially machine-learning, atomistic simulations, statistical physics and computer programming. If interested, the hired researcher may also be trained in student supervision (Bachelor and Master) and teaching.
How to apply:
Application documents (CV, motivation letter) should be sent to [email protected] with the following title "PhD Application NucleFOX"
References:
-”Perspective: Machine learning potentials for atomistic simulations” J. Chem. Phys. 145, 17,
170901 (2016)
-”Measuring transferability issues in machine-learning force fields: The example of Gold-Iron in-
teractions 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)