Abstract: Atomistic simulations based on density functional theory (DFT) have played a critical role in understanding the links between structures and properties of condensed phase. However, DFT calculations are infeasible for simulating large scale complex systems. Machine learning can bridge this gap by learning from DFT data, while providing near DFT accuracy at a reduced computational cost.

In this talk, I will provide an overview of the ML driven atomistic modeling. I will discuss the implication of the extending the DFT accuracy to larger systems sizes (> 1000 atoms) and longer time scales (i.e. ns ). I will end the talk, with a discussion on  a GPU accelerated  coarse-grained DFT predictions via deep kernel learning.