Starting date:

The PhD will start on October 1st, 2021 (with some flexibility).

Background:

Addressing climate change is among the most urging concerns in international policy.  To this respect, the implementation of efficient carbon capture has been proposed as a means of enabling the continued use of fossil fuels in the near term, while renewable energy sources gradually replace our existing infrastructure. Metal-organic frameworks (MOFs) are three-dimensional porous materials that are recently attracting much attention as possible good candidates for an efficient carbon capture. The goal of this project is to computationally design optimal MOFs for an energy efficient carbon-capture-and-release. Specifically, an efficient CCR mechanism will be achieved by employing a change in the affinity for the gas (and thus a change in its uptake) upon an electronic transition induced by external stimuli.

Methodology:

A method combining machine learning and electronic structure simulations (DFT, many-body perturbation theory or quantum chemistry methods) will be developed, tested and employed to provide the first candidate set for the optimal materials. A second step will be performed to further tune and improve the properties of these materials for the desired application. Finally, in order to compare with existing good performer MOFs, the adsorption properties such as the working capacity will be computed for the best performers selected from the previous steps.

Regarding the machine learning approach, the challenge is to develop a robust ML model that can provide highly predictive structure–property relationship using a small training set of high quality electronic structure simulations. The model will be developed on small molecules and then tested and used on databases of existing MOFs.

Candidate profile:
We look for highly motivated candidates with a Master degree in Physics or Chemistry (or similar disciplines) and prior experience with computer simulations and a strong interest in computer science and machine learning.

Students with some experience in machine learning and/or ab initio simulations are encouraged to apply.

A good knowledge of written and spoken English is essential to communicate with our external collaborators (US, Spain). The candidate should have some skills in programming languages (Fortran, C/C++, Python) and Linux. Basic knowledge of parallel computing will be appreciated. 

How to apply:

The deadline for sending your application is May 20th and interviews will be conducted before the end of May. Please send your application as soon as possible by email to: 

Roberta Poloni ([email protected])

Noel Jakse ([email protected])

Applications include: a concise but informative cover letter, CV, Master 1 and Master 2 (or equivalent) marks, names and contact of at least two references that can be joined for recommendation letters.

Working environment:

The PhD student will be located at SIMaP laboratory in Grenoble. SIMaP (https://simap.grenoble-inp.fr/) is a lab hosting scientists from different disciplines working on materials science using both experiments and simulations. The PhD is part of the multidisciplinary institute in artificial intelligence MIAI (https://miai.univ-grenoble-alpes.fr/en/multidisciplinary-institute-in-artificial-intelligence-academic-year-2020-2021-en-799001.htm). Two supervisors are located at SIMaP and a third one, Emilie Devijver, at LIG (https://www.liglab.fr/), the lab of informatics in Grenoble, which is located close to SIMaP, in the "campus universitaire". Grenoble, the capital of the Alpes, offers an international and simulating environment for both leisure (mountain sport) and science. Regular seminars are organized by MIAI, SPF38, and other research centers such as ESRF and ILL.