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PhD in Theoretical Physical Chemistry (H/F) : Development of a new potential for clays and organic molecules from a machine learning approach

This offer is available in the following languages:
Français - Anglais

Date Limite Candidature : jeudi 14 juillet 2022

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General information

Reference : UMR5306-PIEMIG-002
Date of publication : Tuesday, May 24, 2022
Scientific Responsible name : Pierre Mignon
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 October 2022
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

In the context of environmental contamination by emerging pollutants, the interactions of organic emerging contaminants with finely divided minerals control to a large extent their fate in surface and ground waters, their accumulation, degradation and reuse in various human activities cycles. In particular clays are naturally abundant environmental solids with large adsorption capacities, used as low cost material for pollutant removal in water treatment plants. They have been the subject of numerous studies investigating adsorption mechanisms of organics both from experimental [1–3] and/or theoretical [3–5] approaches. Theoretical insights, supporting experimental observations, provide precise description of molecular interactions driving adsorption processes, however actually a harsh compromise has to be done between accuracy and size/time model exploration.
The aim of the PhD project is to develop a novel atomic potential to describe molecular interactions between smectite clays and organic contaminants from a machine learning approach. Promising machine learning potentials (MLP), combining the accuracy and flexibility of electronic structure calculations and the speed of classical potentials, have already been shown to give reliable results for multiple systems [6,7]. The MLP will be developed in a progressive way by increasing system complexity, performing ab-initio molecular dynamics calculations for the training dataset and regular tests. A double model validation will be made by confrontation with experimental measurements performed by the experienced collaborative partner in Grenoble University.

1. Aristilde, L.; Lanson, B.; Charlet, L. Langmuir 2013, 29, 4492–4501.
2. Aristilde, L.; Lanson, B.; Miéhé-Brendlé, J.; Marichal, C.; Charlet, L. J. Colloid Interface Sci. 2016, 464, 153–159.
3. Corbin, G.; Vulliet, E.; Lanson, B.; Rimola, A.; Mignon, Minerals 2021.
4. Mignon, P.; Navarro-Ruiz, J.; Rimola, A.; Sodupe, M. ACS Earth Space Chem. 2019, 3, 1023–1033.
5. Mignon, P.; Sodupe, M. J. Phys. Chem. C 2013, 117, 49, 26179–26189.
6. Pinheiro, M.; Ge, F.; Ferré, N.; Dral, P.O.; Barbatti, M. Chem. Sci. 2021, 12, 14396–14413.
7. Behler, J. Angew. Chem. Int. Ed. 2017, 56, 12828–12840.
8. Lam, J.; Abdul-Al, S.; Allouche, A.-R. J. Chem. Theory Comput. 2020, 16, 1681–1689.
9. Laurens, G.; Rabary, M.; Lam, J.; Peláez, D.; Allouche, A.-R. Theor Chem Acc 2021, 140, 66.

Work Context

Theoretical Physical Chemistry Team of the Light Mater Institut, UMR 5306, Université Lyon1 in collaboration with the Institute of Earth Sciences (ISTerre, UMR 5275), Université Grenoble-Alpes. Supervisors : Pierre Mignon et Abdul Rahman Allouche.

Constraints and risks

The successful candidate shall be enrolled on the PHAST PhD programme of Lyon University. A few occasional trips to Grenoble will take place + participation in international conferences.

Additional Information

The candidate must hold a Master 2 or engineering degree in Physics, Chemistry-Physics, Materials, Nanosciences. The position requires solid knowledge in Physics and Chemistry with, if possible, experience in atomistic simulations and programming skills (Python, C, bash) will be highly appreciated. We are looking for a young researcher who will be able to get involved in his project, curious, having a certain autonomy and a strong motivation to develop skills in programming and data analysis in the field of atomistic simulations. Good oral and written communication skills are also required.
Applications must include a detailed CV (background, experience in the field or other, etc.), one or more reference letters (persons likely to be contacted), a one-page summary of the master's thesis, Master's 1 and 2 marks. In addition, a one-page cover letter is required.

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