Inverting the Kohn–Sham equations with physics-informed machine learning
Electronic structure theory calculations offer an understanding of matter at the quantum level, complementing experimental studies in materials science and chemistry. One of the most widely used methods, density functional theory, maps a set of real interacting electrons to a set of fictitious non-i...
Main Authors: | Vincent Martinetto, Karan Shah, Attila Cangi, Aurora Pribram-Jones |
---|---|
Format: | Article |
Language: | English |
Published: |
IOP Publishing
2024-01-01
|
Series: | Machine Learning: Science and Technology |
Subjects: | |
Online Access: | https://doi.org/10.1088/2632-2153/ad3159 |
Similar Items
-
Machine-learning Kohn–Sham potential from dynamics in time-dependent Kohn–Sham systems
by: Jun Yang, et al.
Published: (2023-01-01) -
Existence of a minimizer for the quasi-relativistic Kohn-Sham model
by: Carlos Argaez, et al.
Published: (2012-01-01) -
Bypassing the Kohn-Sham equations with machine learning
by: Felix Brockherde, et al.
Published: (2017-10-01) -
Shannon Entropy in Atoms: A Test for the Assessment of Density Functionals in Kohn-Sham Theory
by: Claudio Amovilli, et al.
Published: (2018-05-01) -
Simulation of attosecond transient soft x-ray absorption in solids using generalized Kohn–Sham real-time time-dependent density functional theory
by: C D Pemmaraju
Published: (2020-01-01)