Combinatorics

Physics

Mathematics

Atom (system on chip)

Gaussian function

Kernel (algebra)

Politics

Algorithm

Statistical physics

Statistics

Machine learning

Gaussian

Mathematical analysis

Political science

Embedded system

Artificial intelligence

Monte Carlo method

Representation (politics)

Computer science

Molecular dynamics

Gaussian process

Quantum mechanics

Discretization

Kriging

Quantum

The Journal of Chemical Physics

We introduce the FCHL19 representation for atomic environments in molecules or condensed-phase systems. Machine learning models based on FCHL19 are able to yield predictions of atomic forces and energies of query compounds with chemical accuracy on the scale of milliseconds. FCHL19 is a revision of our previous work [F. A. Faber et al., J. Chem. Phys. 148, 241717 (2018)] where the representation is discretized and the individual features are rigorously optimized using Monte Carlo optimization. Combined with a Gaussian kernel function that incorporates elemental screening, chemical accuracy is reached for energy learning on the QM7b and QM9 datasets after training for minutes and hours, respectively. The model also shows good performance for non-bonded interactions in the condensed phase for a set of water clusters with a mean absolute error (MAE) binding energy error of less than 0.1 kcal/mol/molecule after training on 3200 samples. For force learning on the MD17 dataset, our optimized model similarly displays state-of-the-art accuracy with a regressor based on Gaussian process regression. When the revised FCHL19 representation is combined with the operator quantum machine learning regressor, forces and energies can be predicted in only a few milliseconds per atom. The model presented herein is fast and lightweight enough for use in general chemistry problems as well as molecular dynamics simulations.

FCHL revisited: Faster and more accurate quantum machine learning