Machine-learning approaches for nanoparticle simulations

UMET-CNRS; Lille; Sur place; EUR 60 000 - 80 000; Plein temps

Résumé du poste

A leading educational institution in Lille is offering a research internship focused on the mechanisms of nanocrystal formation. The selected student will explore diverse research avenues, including machine-learning approaches for better modeling and simulation. Candidates should hold a Master's in relevant fields and possess knowledge in statistical mechanics and computational physics. The internship provides an excellent training opportunity under expert supervision.

Qualifications

• Master in physics, chemistry or materials science.
• Good knowledge of statistical mechanics and computational physics/chemistry.
• Previous experience in C++/Python/Bash would also be a plus.

Responsabilités

• Pursue research avenues based on machine-learning approaches.
• Focus on material specific studies for nanocrystal formation.
• Understand the formation mechanisms of nanocrystals.

Connaissances

Knowledge of statistical mechanics

Computational physics/chemistry

C++

Python

Bash

Formation

Master in physics, chemistry or materials science

Description du poste

Description

Research overview While nanocrystals in material science are ubiquitous, the mechanisms of their formation which spans from nucleation to crystal growth remain one of the most intriguing process in nature. From the fundamental point of view, the main challenge is related to the stochastic nature of the process and the very small number of atoms involved. Altogether, it leads to numerous technical difficulties that have hindered the ability to systemically study nucleation in the most complex systems. In this context, numerical simulations involving both statistical mechanics and molecular quantum mechanics have been pivotal for providing an atomistic view of the underlying processes. Recently, machine-learning approaches have enabled for more precise and more extensive use of these numerical simulations which are now increasingly converging with experimental measurements.

Project The student will have the opportunity to pursue numerous research avenues depending on their preferences. Indeed, further numerical developments based on machine-learning approaches can be envisaged for statistically sampling free energy barriers associated to nucleation or for better modeling the interactions between atoms fitted on electronic structure calculations. Meanwhile, it will also be possible to focus on a specific material and study different approaches leading to the nanocrystal formation including gas phase condensation, solvent mediated synthesis or deposition mechanisms.

Organization The internship will be carried out at the "Unité Matériaux et Transformations" which is located at the Université de Lille. The student will benefit from the supervision of Julien Lam who is a CNRS researcher expert in numerical simulations for atomistic simulations. Potential students are not required prior knowledge of computational materials science as they will be trained in a large number of research domains including molecular quantum mechanics, statistical physics, machine-learning, material science and computer programming.

Profile

• Master in physics, chemistry or materials science
• Good knowledge of statistical mechanics and computational physics / chemistry
• Previous experience in C++ / Python / Bash would also be a plus

Starting date

Dès que possible