Machine Learning, Quantum Mechanics, and Chemical Compound Space

Ramakrishnan, Raghunathan and von Lilienfeld, Anatole. (2017) Machine Learning, Quantum Mechanics, and Chemical Compound Space. In: Reviews in Computational Chemistry, 30. UK, p. 388.

Full text not available from this repository.

Official URL: https://edoc.unibas.ch/63289/

Downloads: Statistics Overview


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.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Chemie > Physikalische Chemie (Lilienfeld)
UniBasel Contributors:von Lilienfeld, Anatole
Item Type:Book Section
Book Section Subtype:Further Contribution in a Book
Note:Publication type according to Uni Basel Research Database: Book item
Identification Number:
Last Modified:16 Apr 2018 08:31
Deposited On:16 Apr 2018 08:31

Repository Staff Only: item control page