Neural network and ReaxFF comparison for Au properties

International Journal of Quantum Chemistry

Volumn 116, Issue 13, 2016, Pages 979-987

  Boes, Jacob R ^a   Groenenboom, Mitchell C ^b   Keith, John A ^b   Kitchin, John R ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b University of Pittsburgh   (United States)

Author keywords

Kohn Sham density functional theory; machine learning; neural networks; potential energy surfaces; reactive force fields

Indexed keywords

COMPUTATION THEORY; GOLD; LEARNING SYSTEMS; NEURAL NETWORKS; POTENTIAL ENERGY; POTENTIAL ENERGY SURFACES; QUANTUM CHEMISTRY;

ATOMISTIC POTENTIAL; COMPUTATIONAL COSTS; DATA POINTS; KOHN-SHAM DENSITY-FUNCTIONAL THEORY; MOLECULAR CLUSTERS; OVERFITTING; REACTIVE FORCE FIELD; SINGLE COMPONENTS;

DENSITY FUNCTIONAL THEORY;

EID: 84977837443     PISSN: 00207608     EISSN: 1097461X     Source Type: Journal    
DOI: 10.1002/qua.25115     Document Type: Article

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