Automating Energy Landscape Manipulation for Rare Earth Separation

The need for new rare earth and precious metal sources has been exacerbated by a growing population's need for better devices and geopolitical pressure related to their mining. Traditional mining fails to overcome demand, hence criticality, due to its reliance on monolithic (2D) approach to material processing. We developed a method of overcoming de-mixing enthalpy by expanding our view into a thermodynamic energy landscape and realizing that this space can be navigate through felicitous choice, and timing, of stress application. Diversification of the stress tensor beyond mechanical or thermal allows us to shift the energy barriers into navigable energy challenges. Herein, we propose to develop an in silico automation protocol that relies on a transfer function derived from thermodynamics and fluid mechanics. The developed code will be tested on a patent-pending processor developed at North Carolina State University through a DARPA funded project. The transfer function will allow for dynamic control of the system and shall be trained using obtained data for a more efficient separation.