David Ceperley - Quantum Monte Carlo and Machine Learning Simulations of Dense Hydrogen
Recorded 26 May 2022. David Ceperley of the University of Illinois at Urbana-Champaign presents "Quantum Monte Carlo and Machine Learning Simulations of Dense Hydrogen" at IPAM's Monte Carlo and Machine Learning Approaches in Quantum Mechanics Workshop.
Abstract: We have developed Coupled-Electron QMC methods to simulate dense hydrogen, incorporating advanced wavefunctions and using reptation QMC for electronic energies and Path Integral MC for the proton distribution. Recent advances of the calculation of electronic energy gaps allow direct comparison with experimental measurements. Using QMC we have constructed a data-base of forces on the protons of dense hydrogen configurations and trained machine learnt force fields. Simulations of those force field predict novel solid hydrogen structures.
Learn more online at: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-monte-carlo-and-machine-learning-approaches-in-quantum-mechanics/?tab=schedule
Видео David Ceperley - Quantum Monte Carlo and Machine Learning Simulations of Dense Hydrogen канала Institute for Pure & Applied Mathematics (IPAM)
Abstract: We have developed Coupled-Electron QMC methods to simulate dense hydrogen, incorporating advanced wavefunctions and using reptation QMC for electronic energies and Path Integral MC for the proton distribution. Recent advances of the calculation of electronic energy gaps allow direct comparison with experimental measurements. Using QMC we have constructed a data-base of forces on the protons of dense hydrogen configurations and trained machine learnt force fields. Simulations of those force field predict novel solid hydrogen structures.
Learn more online at: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-monte-carlo-and-machine-learning-approaches-in-quantum-mechanics/?tab=schedule
Видео David Ceperley - Quantum Monte Carlo and Machine Learning Simulations of Dense Hydrogen канала Institute for Pure & Applied Mathematics (IPAM)
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2 июня 2022 г. 0:30:19
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