The National Science Foundation recently awarded a 3-year, $300K grant entitled, “SusChEM: machine learning blueprints for greener chelants.” McGowan Institute for Regenerative Medicine affiliated faculty member Eric Beckman, PhD, Bevier Professor of Engineering in the Chemical Engineering Department at the University of Pittsburgh and a Co-Director of the Mascaro Center for Sustainable Innovation, is the project co-principal investigator along with principal investigator John Keith, PhD, R.K. Mellon Faculty Fellow in Energy, Department of Chemical Engineering. The award begins August 1, 2017, and extends through July 31, 2020.
From the project abstract: Chelating agents have recently been identified as a key category of chemical products that are ripe for greener design. It is hypothesized that identifying better alternatives will require far broader explorations of chemical compound space than what is possible with conventional trial and error experimentation. In this project, accurate quantum chemistry calculations will be used to train state-of-the-art machine learning methods that will allow prediction of structures of greener chelating agents.
The machine learning method that will be developed promises a novel route to rapidly predict properties of chelant/metal complexes, not only with higher accuracy but with six orders of magnitude less computational time than conventional predictive quantum chemistry methods (e.g. Kohn-Sham Density Functional Theory). With this computational tool, it will be possible to rapidly screen through about 100,000 hypothetical chelant structures to predict those that would bind strongly to different metal ions. The project will also screen these complexes to see which have high propensities to degrade in reasonable timeframes, and which have low probabilities of being toxic. The top candidates from this novel screening approach will then be experimentally synthesized and tested. This will validate if quantum chemistry-based machine learning would be a transformative tool for environmental sustainability and green chemical design by being a more predictive supplement and/or alternative to conventional QSAR models.”