MRS Meetings and Events


DS03.02.09 2023 MRS Fall Meeting

Machine Learning of Density Functionals for Accurate, Large-Scale Materials Simulations

When and Where

Nov 27, 2023
4:15pm - 4:30pm

Sheraton, Second Floor, Liberty B/C



Kyle Bystrom1,Stefano Falletta1,Boris Kozinsky1

Harvard University1


Kyle Bystrom1,Stefano Falletta1,Boris Kozinsky1

Harvard University1
We have recently developed the CIDER formalism for machine learning exchange-correlation functionals, with a particular emphasis on using nonlocal features to achieve hybrid density functional theory (DFT) accuracy at semilocal DFT cost for large solid-state simulations. In this talk, we will cover current directions being pursued to further improve CIDER functionals, including training full exchange-correlation functionals for applications to heterogeneous systems and improving the accuracy of CIDER functionals for band gap and charge transfer-related problems. We will also discuss how CIDER can be used to overcome cost-accuracy trade-offs for materials science applications where both large system sizes and hybrid DFT accuracy are required, such as the calculation of charged point defect properties in semiconductors.

Symposium Organizers

James Chapman, Boston University
Victor Fung, Georgia Institute of Technology
Prashun Gorai, National Renewable Energy Laboratory
Qian Yang, University of Connecticut

Symposium Support

Elsevier B.V.

Publishing Alliance

MRS publishes with Springer Nature