Research Project in SPECTRE team, Laboratoire CEMCA, UMR CNRS 6521, University of Brest, France.
Title of the project : Data mining in inorganic chemistry: the case study of the Ni(cyclam) catalysts
Contact: alexandre.lebon@univ-brest.fr, Phone: +33 2 98 01 65 79
Context
Greenhouse effect is fed with the large amount of emitted CO2 in the atmosphere mainly because of human activity. Nowadays, the consumption of fossil fuels is still as high as 80% of the entire energy consumption. Various cycles are envisaged, where CO2 is transformed into fuels. Within these scenarios renewable energies are supposed to feed very large electrochemical cells through the use of solar or wind energies. Large electrochemical cells are used to reduce CO2 into valuable products (e.g.
methanol and ethylene) or mixture of gas (e.g. syngas: CO + H2). In the mark of such a research catalyst based on the Ni(cyclam), where cyclam stands for tetra-aza-1,4,8,11-cyclodecane as a ligand complexing a Ni(II) cation, is a recognized catalyst that selectively transforms CO2 into CO. Ni(cyclam), originally characterized in the mid 80’s, is still highly investigated. Recent advances include its grafting on electrodes to achieve a more efficient heterogeneous catalytic system.
Project:
The project is aiming at understanding at the molecular level the mechanism implied during the transformation of carbon dioxide into carbon monoxide by Ni(cyclam). A complete set of data has already been acquired after high throughput calculations by varying the ligand structure around the nickel ion. Structural and electronic indicators are however missing to relate the experimental findings (i.e. electrochemical and spectroscopic data). Complementary data must be collected and a data mining protocol should be established to find strong correlation between the experimental data and the indicators as well as between the indicators and thermodynamical values extracted from the simulation. The simulations were carried out so far with an
implicit model of solvation where the solvent is replaced by a continuum dielectric constant. The development of a methodology, where data acquisition is coupled to an unsupervised machine learning approach, is the final objective of the project. The case of Ni(cyclam) with different ligand structure and different isomers will serve as a case study. Other mechanisms of activation could eventually benefit from such a study.
Research environment
Chimie Electrochimie Moléculaire et Chimie Analytique (CEMCA, UMR CNRS 6521) is a recognized laboratory in coordination chemistry and electrochemistry. The Postdoctoral fellow will work in the team SPECTRE specialized in issues related to catalysis of electrochemical reactions by inorganic complex, chemical reactivity and spectroscopy. CEMCA benefits from the vicinity of the supercomputing center DATARMOR, where state of the art simulation package are installed: VASP 5.4, VASP 6.2 and Gamess US. Computing facility are also available in the laboratory, two 48 core cluster can be used.
Profile of the candidate
Applicant must have some know-how in the practice of DFT calculations, background in theoretical chemistry either in conceptual DFT or quantum chemical topology is a plus, be skilled in programming with python, but the applicant can also be a physicist with good skills in data mining. The applicant applies with the help of the supervisor to the Region Brittany for the Bienvenue+ program. The grant is valid for 2 years. More information can be found at the link:
https://bienvenue.bretagne.bzh/fr/financer-son-projet/bienvenue/