Machine learning based energy-free structure predictions of molecules, transition states, and solids

Dominik Lemm; Guido Falk von Rudorff; O. Anatole von Lilienfeld

doi:10.1038/s41467-021-24525-7

Machine learning based energy-free structure predictions of molecules, transition states, and solids

Dominik Lemm
, Guido Falk von Rudorff
, O. Anatole von Lilienfeld (Corresponding author)

Computational Materials Physics

Publications: Contribution to journal › Article › Peer Reviewed

Original language	English
Article number	4468
Number of pages	10
Journal	Nature Communications
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41467-021-24525-7
Publication status	Published - 22 Jul 2021

Funding

O.A.v.L. acknowledges support from the Swiss National Science Foundation (407540_167186 NFP 75 Big Data). This project has received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreements #952165 and #957189. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 772834). This result only reflects the author's view and the EU is not responsible for any use that may be made of the information it contains. This work was partly supported by the NCCR MARVEL, funded by the Swiss National Science Foundation. Some calculations were performed at sciCORE (http://scicore.unibas.ch/) scientific computing center at the University of Basel.

Austrian Fields of Science 2012

103006 Chemical physics

Keywords

BOND COVALENT RADII
GAUSSIAN-TYPE BASIS
FORCE-FIELD
ORBITAL METHODS
DISTANCE GEOMETRY
BASIS-SETS
CONFORMATIONAL ENERGIES
ELEMENTS
MMFF94
OPTIMIZATION

Access to Document

10.1038/s41467-021-24525-7Licence: CC BY 4.0

https://phaidra.univie.ac.at/o:1604041Licence: CC BY 4.0
https://arxiv.org/abs/2102.02806Licence: Other

Cite this

@article{ee23cc48b9e24209a53e37e9b74fb359,

title = "Machine learning based energy-free structure predictions of molecules, transition states, and solids",

keywords = "BOND COVALENT RADII, GAUSSIAN-TYPE BASIS, FORCE-FIELD, ORBITAL METHODS, DISTANCE GEOMETRY, BASIS-SETS, CONFORMATIONAL ENERGIES, ELEMENTS, MMFF94, OPTIMIZATION",

author = "Dominik Lemm and \{von Rudorff\}, \{Guido Falk\} and \{von Lilienfeld\}, \{O. Anatole\}",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s).",

year = "2021",

month = jul,

day = "22",

doi = "10.1038/s41467-021-24525-7",

language = "English",

volume = "12",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "NATURE PUBLISHING GROUP",

number = "1",

}

TY - JOUR

T1 - Machine learning based energy-free structure predictions of molecules, transition states, and solids

AU - Lemm, Dominik

AU - von Rudorff, Guido Falk

AU - von Lilienfeld, O. Anatole

PY - 2021/7/22

Y1 - 2021/7/22

KW - BOND COVALENT RADII

KW - GAUSSIAN-TYPE BASIS

KW - FORCE-FIELD

KW - ORBITAL METHODS

KW - DISTANCE GEOMETRY

KW - BASIS-SETS

KW - CONFORMATIONAL ENERGIES

KW - ELEMENTS

KW - MMFF94

KW - OPTIMIZATION

UR - https://www.scopus.com/pages/publications/85111111888

U2 - 10.1038/s41467-021-24525-7

DO - 10.1038/s41467-021-24525-7

M3 - Article

SN - 2041-1723

VL - 12

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 4468

ER -

Machine learning based energy-free structure predictions of molecules, transition states, and solids

Funding

Austrian Fields of Science 2012

Keywords

Access to Document

Other files and links

Fingerprint

TREX: Targeting Real chemical accuracy at the EXascale

QML: Quantum Machine Learning: Chemical Reactions with Unprecedented Speed and Accuracy

BIG-MAP: Battery Interface Genome - Materials Acceleration Platform

Cite this

Machine learning based energy-free structure predictions of molecules, transition states, and solids

Funding

Austrian Fields of Science 2012

Keywords

Access to Document

Other files and links

Fingerprint

Projects

TREX: Targeting Real chemical accuracy at the EXascale

QML: Quantum Machine Learning: Chemical Reactions with Unprecedented Speed and Accuracy

BIG-MAP: Battery Interface Genome - Materials Acceleration Platform

Cite this