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Reference : UMR5253-GUIMAU-017
Workplace : MONTPELLIER
Date of publication : Wednesday, May 13, 2020
Type of Contract : FTC Scientist
Contract Period : 12 months
Expected date of employment : 1 September 2020
Proportion of work : Full time
Remuneration : 2500
Desired level of education : PhD
Experience required : Indifferent
The crystalline porous hybrid solids known as Metal-Organic Frameworks (MOFs) have emerged over the last 5 years as promising candidates for the capture/degradation of a series of Chemical Warfare agents (CWAs) including sarin, soman and VX among others. High-Throughput computational studies have been reported allowing the identification of a few MOF candidates with optimal performances for the capture of single CWA molecules. Our group also participated to this computational effort with the identification of Zr- and Ti-MOFs with predicted high affinity and large uptake for a series of CWAs. The objective of this project is to extend this study by considering the best MOFs identified so far and predict their selective CWA adsorption performances with respect to two contaminants e.g. water and hydrocarbons. A special attention will be also paid on the competitive adsorption of diverse CWA molecules. These simulations using force field Monte Carlo techniques will be also applied to CWA simulants. The second objective of this project will be to rationalize the resulting database using Machine Learning technique in order to establish structure/performance relationships and anticipate materials with even improved performances. These simulations will guide the experimental work that will be performed in collaboration. The application of a similar strategy will be equally applied to Toxic Industrial Compounds.
- Monte Carlo simulations to predict the adsorption performances of MOFs with respect to toxic molecules
- Application of Machine Learning to rationalise the database and anticipate materials with even improved performances
Strong background in forcefield-based simulations including Monte Carlo
or/and in Machine Learning techniques
Work performed in the DAMP group https://www.icgm.fr/damp in strong interactions with national and international collaborators expert in the field of synthesis of novel MOFs.
Constraints and risks
no major risks except exposition to computer screen
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