Fig. 1 | Nature Communications

Fig. 1

From: Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Fig. 1

Synergy of quantum chemistry and machine learning. a Forward model: ML predicts chemical properties based on reference calculations. If another property is required, an additional ML model has to be trained. b Hybrid model: ML predicts the wavefunction. All ground state properties can be calculated and no additional ML is required. The wavefunctions can act as an interface between ML and QM

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