Modeling the relationships between synthesis parameters and material morphology of pCAM via machine learning and generative AI

IFP Energies nouvelles (IFPEN) est un acteur majeur de la recherche et de la formation dans les domaines de l’énergie, du transport et de l’environnement. De la recherche à l’industrie, l’innovation technologique est au cœur de son action, articulée autour de quatre priorités stratégiques : Mobilité Durable, Energies Nouvelles, Climat / Environnement / Economie circulaire et Hydrocarbures Responsables.

Dans le cadre de la mission d’intérêt général confiée par les pouvoirs publics, IFPEN concentre ses efforts sur :

• l’apport de solutions aux défis sociétaux de l’énergie et du climat, en favorisant la transition vers une mobilité durable et l’émergence d’un mix énergétique plus diversifié ;
• la création de richesse et d’emplois, en soutenant l’activité économique française et européenne et la compétitivité des filières industrielles associées.

Partie intégrante d’IFPEN, l’école d’ingénieurs IFP School prépare les générations futures à relever ces défis.

Modeling the relationships between synthesis parameters and material morphology of pCAM via machine learning and generative AI

The rapid increase in the popularity of electrified vehicles has led to a significant rise in demand for batteries, with lithium-ion technology currently dominating the market due to its superior performance and high energy density. The performance of lithium-ion batteries depends heavily on the properties of their cathode active material, which, in turn, are influenced by the properties of the precursor of the cathode active material (pCAM).

The pCAM is typically produced through a reactive precipitation process in mixed stirred reactors, which must be optimized to achieve the desired properties. The synthesis of these materials involves numerous operating parameters, making it challenging to determine their specific impact on material properties. During this internship, we propose to develop a model that links process parameters to product properties using machine learning and/or generative AI.

Description

The main goal of this internship is to develop a machine learning framework for the analysis and simulation of the pCAM synthesis process.

The work will be divided into the following tasks:

• Conduct a literature review to select a suitable framework.
• Prepare the training and validation data sets.
• Perform data mining with unsupervised learning: outlier detection, variance analysis, clustering, etc.
• Make predictions using supervised learning: model selection, fine-tuning, and analysis.
• Explore the use of generative AI: can generative AI match or surpass the performance and analytical power of conventional machine learning methods?

Required profile

• Master’s degree in chemical engineering with an experience in python scripting, programming skills, interested in developing machine-learning tools

or

• Master's degree in computer science with an interest in other scientific fields

Additional information

Duration of the internship: 6 months
Workplace: IFPEN Lyon, Rond-point de l'échangeur de Solaize, 69360 Solaize
Transport: public transportation / personal vehicle
Paid internship 1130€/month (gross)