Machine Learning, Quantum Mechanics, and Chemical Compound Space

Abstract

We review recent studies dealing with the generation of machine learning models of molecular and solid properties. The models are trained and validated using standard quantum chemistry results obtained for organic molecules and materials selected from chemical space at random.

References in corpus (1)

• Quantum Energy Regression using Scattering Transforms

Cited by in corpus (2)

• Machine learning of molecular properties: locality and active learning
• A deep learning approach to the structural analysis of proteins