Event Videos
Christian Mendl: Real time evolution with neural-network quantum states by approximating the implicit midpoint method
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Yusuke Nomura: Application of machine learning beyond benchmarks reveals Dirac-type nodal spin liquid in the J1-J2 Heisenberg model
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James Spencer: Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Network
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Roberto Car: Molecular simulation with the deep potential method
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Frank Noé: PauliNet – deep learning the electronic Schrödinger equation
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Kieron Burke: Adventures in Machine Learning of density functionals
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Marivi Fernandez Serra: Machine learning accurate exchange and correlation functionals of the electronic density
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Markus Holzmann: Iterative backflow renormalization: a non-linear network for quantum many-body wave functions in continuous space
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Weinan E: Developing meta-universal generalized density functional theory models and solving ground state Schrodinger equation for many-electron systems
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