Ground state charge density prediction in C-BN nanoflakes using rotation equivariant feature-free artificial neural networks

Ab initio methods have been the workhorse for the computational investigation of new materials during the past few decades. In spite of the improvements regarding the efficiency and scalability achieved by various implementations, the self-consistent solution of the Konhn-Sham equations remains chal...

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Veröffentlicht in:	Carbon (New York) 2021-04, Vol.174, p.276-283
Hauptverfasser:	Mitran, Tudor Luca, Nemnes, George Alexandru
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial intelligence Artificial neural networks Charge density Computer simulation DFT Feature-free Graphene nanoflakes Ground state Learning theory Machine learning Material properties Neural networks Regularization Rotation Rotation equivariance
Online-Zugang:	Volltext
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