Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session L18: Atomistic Simulations via Machine Learning Potentials
8:00 AM–11:00 AM,
Wednesday, March 17, 2021
Sponsoring
Unit:
DCOMP
Chair: Annabella Selloni, Princeton University
Abstract: L18.00003 : Machine learned exchange and correlation functionals in density functional theory: progress and applications*
9:12 AM–9:48 AM
Live
Presenter:
Marivi Fernandez
(State Univ of NY - Stony Brook)
Authors:
Marivi Fernandez
(State Univ of NY - Stony Brook)
Sebastian Dick
(State Univ of NY - Stony Brook)
It is, however, possible to approximate the exact functional, providing a balance between accuracy and computational cost.
In Kohn-Sham DFT, this balance depends on the choice of exchange and correlation functional, which only exists
in approximate form. Increasing the non-locality of this functional and climbing the figurative Jacob's ladder of DFT, one can systematically reduce the amount of approximation involved and thus approach the exact functional.
In this talk I will review our framework to create density functionals by using supervised machine learning. These functionals learn a meaningful representation of the physical information contained in the training data. I will show that these machine-learned functionals can be designed to lift the accuracy of local and semilocal functionals to that provided by more accurate methods while maintaining their baseline efficiency. In the second part of the talk I will address how machine learning methods can help to understand what
properties and conditions must the approximate Kohn-Sham exchange and correlation potential satisfy in order to obtain not only the exact energy but also the exact electronic density distribution.
Applications of these functionals to real and model systems will be presented.
*This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Awards DE-SC0001137 and DE-SC0019394, as part of the CCS and CTC Programs.
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