Can exact conditions improve machine-learned density functionals? (bibtex)
by Jacob Hollingsworth, Li Li, Thomas E. Baker and Kieron Burke
Abstract:
Historical methods of functional development in density functional theory have often been guided by analytic conditions that constrain the exact functional one is trying to approximate. Recently, machine-learned functionals have been created by interpolating the results from a small number of exactly solved systems to unsolved systems that are similar in nature. For a simple one-dimensional system, using an exact condition, we find improvements in the learning curves of a machine learning approximation to the non-interacting kinetic energy functional. We also find that the significance of the improvement depends on the nature of the interpolation manifold of the machine-learned functional.
Reference:
Can exact conditions improve machine-learned density functionals? Jacob Hollingsworth, Li Li, Thomas E. Baker and Kieron Burke, The Journal of Chemical Physics 148, 241743 (2018).
Bibtex Entry:
@article{HLBB18,
	Pub-num 	   = {184},
	Title 		   = {Can exact conditions improve machine-learned density functionals?},
	Author 		   = {Jacob Hollingsworth and Li Li and Thomas E. Baker and Kieron Burke},
	Abstract 	   = {Historical methods of functional development in density functional theory have often been guided by analytic conditions that constrain the exact functional one is trying to approximate. Recently, machine-learned functionals have been created by interpolating the results from a small number of exactly solved systems to unsolved systems that are similar in nature. For a simple one-dimensional system, using an exact condition, we find improvements in the learning curves of a machine learning approximation to the non-interacting kinetic energy functional. We also find that the significance of the improvement depends on the nature of the interpolation manifold of the machine-learned functional.},
	Doi 		   = {10.1063/1.5025668},
	Year 		   = {2018},
	Month 		   = {June},
	Journal		   = {The Journal of Chemical Physics},
	Volume 		   = {148},
	Number 		   = {24},
	Pages 		   = {241743},
	keywords 	   = {ML, Machine Learning,}
}
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