Argonne is one of five national laboratories and fourteen universities awarded three-year grants under a DOE Funding Opportunity titled “Data Science for Discovery in Chemical and Materials Sciences.” Argonne was awarded funding for two research projects. Total funding will be nearly $4.75 million over three years.
Lynda Soderholm, department head in the Chemical Sciences and Engineering division and Argonne Distinguished Fellow, leads one of Argonne’s new data science projects. Her collaborators include Stefan Wild and Prasanna Balaprakash from the Mathematics and Computer Science division and the Argonne Leadership Computing Facility, a DOE Office of Science User Facility, and Aurora Clark from Washington State University.
This team’s project entails a machine-learning approach to quantifying the energy drivers in chemical separations, such as liquid-liquid extraction, a common separation method. Chemical separations play a critical role in resource management by providing access to large quantities of resource-limited materials with high purity and enabling the cleanup of contaminated materials and chemicals for reuse. At present, molecular and mesoscale studies in chemical separations are limited to sampling of the reaction space by dividing the space into smaller, tractable problems. Recognizing the vastness and complexity of the reaction phase space, this project will turn to data science, machine learning and optimal design approaches to navigate the high-dimensional and interdependent features that define robust chemical separations.