Orbital-free bond breaking via machine learning

Autor: Matthias Rupp, Klaus-Robert Müller, Leo Blooston, John C. Snyder, Kieron Burke, Katja Hansen
Jazyk: angličtina
Rok vydání: 2013
Předmět:
FOS: Computer and information sciences
Electron density
physics.chem-ph
FOS: Physical sciences
General Physics and Astronomy
Machine Learning (stat.ML)
02 engineering and technology
Bond breaking
Machine learning
computer.software_genre
Kinetic energy
01 natural sciences
Molecular dynamics
Engineering
Statistics - Machine Learning
Artificial Intelligence
Physics - Chemical Physics
0103 physical sciences
Physics::Atomic and Molecular Clusters
Computer Simulation
Physical and Theoretical Chemistry
Physics::Chemical Physics
010306 general physics
Computer Science::Databases
Chemical Physics (physics.chem-ph)
Physics
Condensed Matter - Materials Science
Chemical Physics
business.industry
Materials Science (cond-mat.mtrl-sci)
021001 nanoscience & nanotechnology
Diatomic molecule
stat.ML
cond-mat.mtrl-sci
Physical Sciences
Chemical Sciences
Quantum Theory
Artificial intelligence
0210 nano-technology
business
computer
Algorithms
Zdroj: Snyder, JC; Rupp, M; Hansen, K; Blooston, L; Müller, KR; & Burke, K. (2013). Orbital-free bond breaking via machine learning. Journal of Chemical Physics, 139(22). doi: 10.1063/1.4834075. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/5rc7m9t2
Snyder, JC; Rupp, M; Hansen, K; Blooston, L; Müller, K-R; & Burke, K. (2013). Orbital-free bond breaking via machine learning. Journal of Chemical Physics, 139(22). doi: 10.1063/1.4834075. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/0ch480mj
The Journal of chemical physics, vol 139, iss 22
The Journal of Chemical Physics
Popis: Using a one-dimensional model, we explore the ability of machine learning to approximate the non-interacting kinetic energy density functional of diatomics. This nonlinear interpolation between Kohn-Sham reference calculations can (i) accurately dissociate a diatomic, (ii) be systematically improved with increased reference data and (iii) generate accurate self-consistent densities via a projection method that avoids directions with no data. With relatively few densities, the error due to the interpolation is smaller than typical errors in standard exchange-correlation functionals. © 2013 AIP Publishing LLC.
Databáze: OpenAIRE
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