Abstract
Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve complicated quantum-chemical equations and realizing efficient computing of molecular electronic properties.
Stacked Generalization Approach to Improve Prediction of Molecular Atomization Energies
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