Enabling Large Scale DFT Simulation with GPU Acceleration and Machine Learning
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Author / Producer
Date
2016
Publication Type
Doctoral Thesis
ETH Bibliography
yes
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Publication status
published
External links
Editor
Contributors
Examiner : VandeVondele, Joost
Examiner : Schulthess, Thomas C.
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Journal / series
Volume
Pages / Article No.
Publisher
ETH Zurich
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Edition / version
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Subject
DICHTEFUNKTIONALE (QUANTENCHEMIE U. QUANTENMECHANIK); MOLEKULARE DYNAMIK (MOLEKÜLPHYSIK); NUMERISCHE METHODEN IN DER PHYSIK (NUMERISCHE MATHEMATIK); MASCHINELLES LERNEN (KÜNSTLICHE INTELLIGENZ); PARALLELE NUMERIK (NUMERISCHE MATHEMATIK); DENSITY FUNCTIONALS (QUANTUM CHEMISTRY AND QUANTUM MECHANICS); MOLECULAR DYNAMICS (MOLECULAR PHYSICS); NUMERICAL METHODS IN PHYSICS (NUMERICAL MATHEMATICS); MACHINE LEARNING (ARTIFICIAL INTELLIGENCE); PARALLEL COMPUTING (NUMERICAL MATHEMATICS)
Organisational unit
02160 - Dep. Materialwissenschaft / Dep. of Materials
Notes
Dissertation. ETH Zürich. 2016. No. 23879.