Dr. Marilu Perez Garcia
Critical Materials Institute
Purifying critical materials needed for high-tech equipment and machinery from raw or recycled sources often requires a solvent extraction step. Tailoring a solvent extractant for specific systems could save significant time and resources. However, tailoring extractants for different feed compositions is prohibitively expensive. Focusing on the ligand metal ion binding site, our team uses machine learning (ML) and access to large amounts of empirical data to predict absolute aqueous stability constants for a given ligand. To train the ML algorithm, we combine data related to the ligands, the metal ions, and data obtained from ab initio and molecular mechanics calculations. The prediction software is coupled with HostDesigner to quickly generate and rank theoretical ligands by selectivity between ions of interest. This software, currently under development, will enable rapid down selection of promising targets, guide empirical research, and help drive innovation in solvent extraction science.
This seminar will be held exclusively on Zoom (955 5209 1021). Please visit the Physics Seminars page for a link.