Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise simulations.
Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning.
Accurate approximations to density functionals have recently been obtained via machine learning (ML).
Kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions in a one-dimensional box as a functional of their density.
Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density.
The accurate prediction of molecular energetics in chemical compound space is a crucial ingredient for rational compound design.