Multiscale modeling of phase transformations

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Team leader : Héléna Zapolsky

Team members : H. Zapolsky (PR), G. Demanges (MCF), D. Ledue (PR), P.E. Berche (MCF), D. Blavette(PR), R. Patte (IR) 

We use and develop several types of simulations and numerical models to study the microstructural evolution, segregation phenomena at the grain boundaries (JG) or magnetic structures of nanoparticles through a multi-scale approach. Atomistic calculations (Atomic Density Function, Quasiparticle Method (QA), Monte-Carlo) and the Phase Field (PF) approach are used to study phase transformations in different materials during aging or under external stress. The richness of the team is based on its broad expertise in numerical models and methods as well as in the diversity of materials and phenomena studied. The current orientations of the team will be pursued and new emerging axes, such as the application of the "machine learning" approach to develop interaction potentials used in atomistic models or recognition of structural defects from 3D simulation data, will be developed. 
 

1. Quasiparticule method:
   
   One of the strong points of the team is the development of a new modeling method, the QA approach. This approach is starting to be proven and several groups internationally are collaborating with us on this subject (Empa Zurich, KTH Sweden, Prof. Du's group from Changsha University China, Pacific Northwester Laboratory and others). In the context of these collaborations, we apply for example this model to model the structure and intergranular diffusion in Cu/W multilayers, the effect of stress on phase transformations in steels, the precipitation of Guinier-Preston zones in Al-based alloys, and the segregation of solutes at grain boundaries and moving austenite/ferrite interfaces. 

    

2. Machine learning : 
   
   We are developing the so-called "Machine-Learning" (ML) approaches in synergy with the QA approach for the study of JG structures (in collaboration with the SRMP at CEA Saclay). The first step is to develop more realistic interaction potentials for the QA approach by means of ML, which will allow to apply this method to the study of JGs in real materials. In addition we wish to propose new ML approaches, for the analysis of structural defects in the vicinity of JGs obtained numerically by the QA approach or to process tomographic atom probe data (the detection of nanoclusters formed under irradiations, structure of nanoprecipitates at interfaces,...) in interaction with the "Nuclear Materials" and "Fundamentals of phase transformations and microstructures" theme teams.

    

3. Phase field: 
   
   The activity in mesoscopic modeling in phase field is a strong point of our activities, in particular on the theme of solidification and faceted growth. In metallurgy, the formation of dendrites during the solidification process plays an essential role in the microstructural characteristics and thus the mechanical properties of many commercial alloys. An efficient approach to model this phenomenon is the Phase Field approach. Nevertheless, the specific case of so-called ≪ faceted solidification ≫ remains imperfectly treated by the CP approach. Therefore, we wish to propose new 3D anisotropy functions for the anisotropic surface energies of the considered materials and the integration of faceted CP models into existing approaches to describe solidification under real conditions and for real materials.  

    

4. Magnetic materials:

   A part of our activities concerns the study of magnetic materials, with a particular interest for :

   - magnetic phases, magnetic phase transitions and magneto electric coupling in frustrated multiferroic antiferromagnetic oxides, in particular the compound CuCrO2. This work is carried out by means of numerical Monte Carlo, Phase Field and density functional theory simulations (collaboration with Y. Kvashnin, University of Uppsala)

   - correlations between nanostructures and exchange anisotropy properties in MF/AFM nanoplots. Specifically, we study the effect of frustration and grain boundaries on the magnetic patterns, including the appearance of disordered magnetic phases and magnetic domains in the AFM layer. This work is carried out by means of Monte Carlo simulations at the atomic scale. 

   Nous utilisons et développons plusieurs types de simulations et modèles numériques afin d'étudier l'évolution microstructurale, phénomènes de ségrégation aux joints de grains (JG) ou structures magnétiques de nanoparticules au travers d'une approche multi-échelles. Les calculs atomistiques (Atomic Density Function, Méthode de Quasiparticules (QA), Monte-Carlo) et l’approche de Champ de Phases (CP) sont utilisés pour étudier les transformations de phases dans différents matériaux en cours de vieillissement ou sous des contraintes externes. La richesse de l’équipe repose sur son expertise assez large dans des modèles et méthodes numériques ainsi que dans la diversité des matériaux et des phénomènes étudiés. Les orientations actuelles de l’équipe seront poursuivies et de nouveaux axes émergeants, tels que l’application de l’approche de « machine learning » pour développer les potentiels d’interaction utilisés dans des modèles atomistiques ou reconnaissance des défauts structuraux à partir des données de simulation en 3D, seront développés.