Details

Autor(en) / Beteiligte

• Hollingsworth, Jacob
• Baker, Thomas E.
• Burke, Kieron

Titel

Can exact conditions improve machine-learned density functionals?

Ist Teil von

• The Journal of chemical physics, 2018-06, Vol.148 (24), p.241743-241743

Ort / Verlag

United States

Erscheinungsjahr

2018

Link zum Volltext

Quelle

AIP Scitation Journals Complete

Beschreibungen/Notizen

• 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.