Comment on "Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning"

Abstract

A Comment on the Letter by M. Rupp et al., Phys. Rev. Lett. 108 058301 (2012).

1 page, 2 figures, see [arXiv:1109.2618v1]

References in corpus (2)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Cited by in corpus (17)

• Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
• Physics-inspired structural representations for molecules and materials
• Unified Representation of Molecules and Crystals for Machine Learning
• Machine-learned multi-system surrogate models for materials prediction
• Accelerated materials property predictions and design using motif-based fingerprints
• Accurate Force Field for Molybdenum by Machine Learning Large Materials Data
• Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
• Metrics for measuring distances in configuration spaces
• Wavelet Scattering Regression of Quantum Chemical Energies
• A Multi-scale Approach for Simulations of Kelvin Probe Force Microscopy with Atomic Resolution
• Machine learning enhanced global optimization by clustering local environments to enable bundled atomic energies
• Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions
• Completeness of Atomic Structure Representations
• Quantum chemical roots of machine-learning molecular similarity descriptors
• Ab initio machine learning of phase space averages
• Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties
• Comment on "Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four body interactions"