11 research outputs found
Operator quantum machine learning: Navigating the chemical space of response properties
The identification and use of structure property relationships lies at the
heart of the chemical sciences. Quantum mechanics forms the basis for the
unbiased virtual exploration of chemical compound space (CCS), imposing
substantial compute needs if chemical accuracy is to be reached. In order to
accelerate predictions of quantum properties without compromising accuracy, our
lab has been developing quantum machine learning (QML) based models which can
be applied throughout CCS. Here, we briefly explain, review, and discuss the
recently introduced operator formalism which substantially improves the data
efficiency for QML models of common response properties
FCHL revisited:Faster and more accurate quantum machine learning
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 [Faber et al. 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 an MAE binding energy error of less than
0.1 kcal/mol/molecule after training on 3,200 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
Ab initio machine learning in chemical compound space
Chemical compound space (CCS), the set of all theoretically conceivable
combinations of chemical elements and (meta-)stable geometries that make up
matter, is colossal. The first principles based virtual sampling of this space,
for example in search of novel molecules or materials which exhibit desirable
properties, is therefore prohibitive for all but the smallest sub-sets and
simplest properties. We review studies aimed at tackling this challenge using
modern machine learning techniques based on (i) synthetic data, typically
generated using quantum mechanics based methods, and (ii) model architectures
inspired by quantum mechanics. Such Quantum mechanics based Machine Learning
(QML) approaches combine the numerical efficiency of statistical surrogate
models with an {\em ab initio} view on matter. They rigorously reflect the
underlying physics in order to reach universality and transferability across
CCS. While state-of-the-art approximations to quantum problems impose severe
computational bottlenecks, recent QML based developments indicate the
possibility of substantial acceleration without sacrificing the predictive
power of quantum mechanics