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Abstract
We introduce a representation of any atom in any chemical environment for the automatized
generation of universal kernel ridge regression-based quantum machine learning (QML)
models of electronic properties, trained throughout chemical compound space. The representation
is based on Gaussian distribution functions, scaled by power laws and explicitly accounting
for structural as well as elemental degrees of freedom. The elemental components help
us to lower the QML model's learning curve, and, through interpolation across the
periodic table, even enable "alchemical extrapolation" to covalent bonding between
elements not part of training. This point is demonstrated for the prediction of covalent
binding in single, double, and triple bonds among main-group elements as well as for
atomization energies in organic molecules. We present numerical evidence that resulting
QML energy models, after training on a few thousand random training instances, reach
chemical accuracy for out-of-sample compounds. Compound datasets studied include thousands
of structurally and compositionally diverse organic molecules, non-covalently bonded
protein side-chains, (H2O)40-clusters, and crystalline solids. Learning curves for
QML models also indicate competitive predictive power for various other electronic
ground state properties of organic molecules, calculated with hybrid density functional
theory, including polarizability, heat-capacity, HOMO-LUMO eigenvalues and gap, zero
point vibrational energy, dipole moment, and highest vibrational fundamental frequency.
Abbreviated Title:
The Journal of Chemical Physics
Publisher:
AIP Publishing
ISSN
(Print):
0021-9606
ISSN
(Electronic):
1089-7690
Publication date Created:
June
28 2018
Publication date
(Print):
June
28 2018
Volume: 148
Issue: 24
Page: 241717
Affiliations
[1
]Institute of Physical Chemistry and National Center for Computational Design and Discovery
of Novel Materials, Department of Chemistry, University of Basel, Basel, Switzerland