Deep neural network predicts molecular wavefunctions in atomic orbital basis from which quantum properties are derived at force-field efficiency.
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A machine-learning interatomic potential fitted to DFT data simulates ns-scale crystallization and phase separation in Ge-rich GeSbTe alloys at 600 K, producing metastable cubic GeTe and amorphous GeSb/Ge phases.
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Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions
Deep neural network predicts molecular wavefunctions in atomic orbital basis from which quantum properties are derived at force-field efficiency.
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On phase separation and crystallization of Ge-rich GeSbTe alloys from atomistic simulations with a machine learning interatomic potential
A machine-learning interatomic potential fitted to DFT data simulates ns-scale crystallization and phase separation in Ge-rich GeSbTe alloys at 600 K, producing metastable cubic GeTe and amorphous GeSb/Ge phases.