Description du sujet de thèse

Context of the PhD work

Crystalline metal alloys are a structural material par excellence due to their unique combination of ductility and strength, bending rather than cracking under large external loads. Metals are also highly sustainable as they are lightweight and easily recyclable, thus economical in raw materials.

The plastic deformation of metals is enabled by the collective motion of dislocations, line defects of plastic displacement typically constrained to move on low index crystal planes. Under repeated loading, dislocations form dense, tangled networks inside a metals microstructure, causing work hardening, loss of ductility and eventually fracture. Predicting how dislocation networks evolve and lead to component failure is a grand, open challenge of engineering.

Theoretical models of dislocation plasticity are essential as experimental observations are only indirect or destructive. Despite decades of effort to give a closed equation for dislocation microstructure evolution, current methods are physics-inspired but hand-tuned, lacking data-driven representations essential to harness machine learning tools which have shown great ability in analysis and prediction. Very similar issues have been encountered when constructing atomistic potentials for molecular simulations; the solution was the development of high-dimensional “descriptor” functions to represent atomic datasets.

Job Description

The PhD position, part of the ANR DaPreDis project (see below), will build a data-driven framework to represent dislocation networks, inspired by recent advances in machine learning for atomic systems.

A first part of the PhD work will be devoted to generation of 3D dislocation microstructures using state-of-the-art mesoscale and atomic-scale simulations (dislocation dynamics and molecular dynamics), then designing a representation for the comparison of dislocation data from both types of simulations.
The proposed data representation will have to respect the symmetries and invariances of the dislocation microstructures and comply with known physical laws.

In the second part of the thesis, data from DD simulations and ML predictions will be mined to reveal new physical mechanisms or correlations in an unbiased, data-driven manner. The developed data representations will be used to build new data-driven models to advance our understanding of critically important open problems in metal plasticity, in collaboration with experimental colleagues.

Contexte de travail

Presentation of Consortium

The ANR DaPreDis project (2024-2028) is a collaborative effort between Prof. Sylvain Queyreau, LSPM, Univ. Sorbonne Paris Nord, and Dr Thomas Swinburne, CNRS and CINaM, Aix-Marseille Université. The project will be hiring a postdoctoral scholar late 2025 on complementary aspects of the project. In addition to regular team meetings between Paris and Marseille, the project will support travel to international conferences and research visits to collaborators in the USA and Europe.

LPSM: The Laboratoire des Sciences des Procédés et des Matériaux (LSPM) is composed of researchers from backgrounds spanning process engineering, mechanics, physics and chemistry, conducting research in the broad field of material science and processing. The LSPM is particularly renowned for expertise in multiscale simulations and original experiments of metal deformation and microstructuration.
CINaM: Situated in the beautiful Luminy campus at the border of the Calanques national park, the Centre Interdisciplinaire de Nanosciences de Marseille (CINaM) conducts research on matter at the nanoscale, a broad field covering the growth and microstructural properties of crystalline solids, surface chemistry, catalysis, and the dynamics of living systems.

This position is located in a sector covered by the protection of scientific and technical potential (PPST) and therefore requires, in accordance with the regulations, that your arrival be authorized by the competent authority of the MESR

Le poste se situe dans un secteur relevant de la protection du potentiel scientifique et technique (PPST), et nécessite donc, conformément à la réglementation, que votre arrivée soit autorisée par l'autorité compétente du MESR.

Contraintes et risques

N.A.

Informations complémentaires

This position is located in a sector covered by the protection of scientific and technical potential (PPST) and therefore requires, in accordance with the regulations, that your arrival be authorized by the competent authority of the MESR.