The candidate will be based at CEA Marcoule but will work in collaboration with CEA Marcoule (supervisor J.-M. Delaye) and CEA Saclay (supervisors R. Pollet, T. Charpentier).
The objective of the project is to develop a tool based on molecular simulations combined with Machine Learning to estimate rapidly the distributions of hydrolysis and reformation energies of the chemical bonds on the surface of alumino silicate glasses (SiO2+Al2O3+CaO+Na2O).
The first step will consist in validating the classical force fields used to prepare the hydrated SiO2-Al2O3-Na2O-CaO systems 1 by comparison with ab initio calculations. In particular, metadynamics will be used to compare classical and ab initio elementary hydrolysis mechanisms 2.
The next step will consist in performing « Potential Mean Force » calculations using the classical force fields to estimate distributions of hydrolysis and reformation energies on large statistics in few glass compositions 3. Then by using Machine Learning and atomic structural descriptors, we will try to correlate local structural characteristics of the chemical bonds to the hydrolysis and reformation energies. Methods such as Kernel Ridge Regression, Random Forest or Dense Neural Network will be compared.
At the end, a generic tool will be available to rapidly estimate distributions of hydrolysis and reformation energies for a given glass composition.
1 T. Mahadevan, A. Baroni, M. Taron, S. Gin, J Du, J.-M. Delaye, Journal of Non-Crystalline Solids, 592 (2022) 121746.
2 R. Pollet, N. Nair, D. Marx, Inorganic Chemistry 50 (2011) 4791.
3 K. Damodaran, J.-M. Delaye, A.G. Kalinichev, S. Gin, Acta Materialia, 225 (2022) 117478.Offer Requirements Specific Requirements
Molecular modelling, Machine Learning, Python, C++, FortranContact Information