The Camille and Henry Dreyfus Foundation has selected faculty member Nick Jackson as one of seven award recipients in its 2021 program for Machine Learning in the Chemical Sciences and Engineering. The support will enable Jackson to move in a new research direction with the computational technique that he developed, called electronic course graining (ECG).
The Dreyfus program for Machine Learning in the Chemical Sciences and Engineering was initiated in 2020 and provides funding for innovative projects in any area of Machine Learning (ML) consistent with the Foundation’s broad objective to advance the chemical sciences and engineering. The Foundation anticipates that these projects will contribute new fundamental chemical insight and innovation in the field. Funding for the seven awards totals $799,470.
Jackson is a theoretical chemist who joined the Illinois chemistry faculty in January as a member of the Center for Theoretical Chemistry. His lab’s research goals focus on characterizing and designing the next generation of soft materials using advanced theory and simulation with an emphasis on soft materials that exhibit useful applications in semiconducting, energy storage, energy generation, and bioelectronic technologies. Using a broad toolset, his research integrates molecular quantum mechanics, computational statistical mechanics, and machine learning, with a strong focus on the development of new multiscale simulation methodologies that help bring quantum-chemical accuracy to the mesoscale.
The ECG technique substantially boosts the scale over which one can do electronic property calculations of condensed phase systems.
“Effectively making electronic calculations bigger and faster and more available,” Jackson explained.
The Dreyfus Foundation award will support further development of that area of research.
“In particular, it will help us move in the direction of electronic structure predictions at length scales inaccessible via traditional particle-based coarse-graining,” Jackson said.