Machine Learned Interatomic Potentials for 2D Materials

Density Functional Theory (DFT) is the standard algorithm for many electronic structure calculations.  Like many methods, DFT sacrifices some scalability for accuracy.  This makes DFT levels of accuracy difficult to achieve for large systems like those in multilayer 2D heterostructures.  We have obtained DFT accuracy by training atomic cluster expansions (ACE) on multilayer graphene.

Date
Location
Amos Eaton 216
Speaker: Drake Clark from University of Minnesota
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