Open Access

Physical Review Letters

, volume 108 , issue 5 , publication number 058301

Fast and accurate modeling of molecular atomization energies with machine learning.

Matthias Rupp ^{1, 2}

Alexandre Tkatchenko ^{2, 3}

KLAUS-ROBERT MÜLLER ^{1, 2}

Klause Muller ^{1, 2}

O Anatole Von Lilienfeld ^{2, 4}

O. Anatole von Lilienfeld ^{2, 4}

Hide authors affiliations Show authors affiliations: 4 affiliations

Machine Learning Group, Technical University of Berlin, Franklinstr 28/29, 10587 Berlin, Germany |

Institute of Pure and Applied Mathematics, University of California Los Angeles, Los Angeles, California 90095, USA |

Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany |

⁴

Argonne Leadership Computing Facility, Argonne National Laboratory, Argonne, Illinois 60439, USA |

Publication type: Journal Article

Publication date: 2012-01-31

American Physical Society (APS)

Physical Review Letters

scimago Q1

wos Q1

SJR: 2.856

CiteScore: 15.6

Impact factor: 9.0

ISSN: 00319007, 10797114

DOI: 10.1103/physrevlett.108.058301

Copy DOI

PubMed ID: 22400967

General Physics and Astronomy

Abstract

We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a nonlinear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross validation over more than seven thousand organic molecules yields a mean absolute error of ∼10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.

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Cite this

GOST |

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GOST Copy

Rupp M. et al. Fast and accurate modeling of molecular atomization energies with machine learning. // Physical Review Letters. 2012. Vol. 108. No. 5. 058301

GOST all authors (up to 50) Copy

Rupp M., Tkatchenko A., MÜLLER K., Muller K., Von Lilienfeld O. A., von Lilienfeld O. A. Fast and accurate modeling of molecular atomization energies with machine learning. // Physical Review Letters. 2012. Vol. 108. No. 5. 058301

RIS |

Cite this

RIS Copy

TY - JOUR

DO - 10.1103/physrevlett.108.058301

UR - https://doi.org/10.1103/physrevlett.108.058301

TI - Fast and accurate modeling of molecular atomization energies with machine learning.

T2 - Physical Review Letters

AU - Rupp, Matthias

AU - Tkatchenko, Alexandre

AU - MÜLLER, KLAUS-ROBERT

AU - Muller, Klause

AU - Von Lilienfeld, O Anatole

AU - von Lilienfeld, O. Anatole

PY - 2012

DA - 2012/01/31

PB - American Physical Society (APS)

IS - 5

VL - 108

PMID - 22400967

SN - 0031-9007

SN - 1079-7114

ER -

BibTex

Cite this

BibTex (up to 50 authors) Copy

@article{2012_Rupp,

author = {Matthias Rupp and Alexandre Tkatchenko and KLAUS-ROBERT MÜLLER and Klause Muller and O Anatole Von Lilienfeld and O. Anatole von Lilienfeld},

title = {Fast and accurate modeling of molecular atomization energies with machine learning.},

journal = {Physical Review Letters},

year = {2012},

volume = {108},

publisher = {American Physical Society (APS)},

month = {jan},

url = {https://doi.org/10.1103/physrevlett.108.058301},

number = {5},

pages = {058301},

doi = {10.1103/physrevlett.108.058301}

}

Publisher

American Physical Society (APS)

Journal

Physical Review Letters

scimago Q1

wos Q1

SJR

2.856

CiteScore

15.6

Impact factor

9.0

ISSN

00319007 (Print)

10797114 (Electronic)

Profiles

Alexandre Tkatchenko