Machine Learning, Quantum Chemistry, and Chemical Space
Raghunathan Ramakrishnan, O. Anatole von Lilienfeld · Reviews in computational chemistry · 2017
A number of machine learning (ML) studies have appeared with the commonality that quantum mechanical properties are being predicted based on regression models defined in chemical compound space (CCS). The quantum mechanical framework is crucial for the unbiased exploration of CCS since it enables, at least in principle, the free variation of nuclear charges, atomic weights, atomic configurations, and electron number. This chapter first gives a brief tutorial summary of the employed ML model in Kernel Ridge Regression. A discussion on the various representations (descriptors) used to encode molecular species, in particular the molecular Coulomb-matrix (CM), sorted or its eigenvalues follows. The chapter also reviews quantum chemistry data of 134k molecules. The local, linearly scaling ML models for atomic properties such as forces on atoms, nuclear magnetic resonance (NMR) shifts, core-electron ionization energies, as well as atomic charges, dipole-moments, and quadrupole-moments for force-field predictions are finally discussed.