Finding a selective and efficient extractant is one of the main challenges of hydrometallurgy. A comprehensive screening is impossible by the synthesis/test method due to the high number of possible molecules. Instead, more and more studies use quantum calculations to evaluate the complexes stabilities. Still, some important parameters such as solubility are lacking in this model.
This project thus aims to develop an AI based tool that provides solubility values from the molecular structure of any ligand. The study will first focus on 3 solvants: water, used as a reference as AI tools already exist, 3 M nitric acid to mimic nuclear industry applications and n-octanol, organic solvent used to measure the partition coefficient logP. The methodology follows 4 steps:
1) Bibliography on existing AI tools for solubility prediction yielding the choice of the most promising method(s)
2) Bibliography on existing databases to be complemented by the student's in-lab solubility experiments
3) Code generation and training of the neural network on the step 2 databases
4) Checking the accuracy of the predictions on molecules not included in the databases by comparing the calculated results with in-lab experiments