Nature Computational Science, volume 3, issue 1, pages 12-24

Chemical reaction networks and opportunities for machine learning

Mingjian Wen ^{1, 2}

Evan Walter Clark Spotte-Smith ^{3, 4}

Samuel M Blau ²

Matthew J Mcdermott ^{3, 4}

Aditi S Krishnapriyan ^{5, 6, 7}

Kristin Persson ^{4, 8}

Hide authors affiliations Show authors affiliations: 8 affiliations

Chemical and Biomolecular Engineering, University of Houston, Houston, USA |

Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, USA |

Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, USA |

⁴

Materials Science and Engineering, University of California, Berkeley, Berkeley, USA |

⁵

Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, USA |

⁶

Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, USA |

⁷

Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, USA |

⁸

Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, USA |

Publication type: Journal Article

Publication date: 2023-01-16

Springer Nature

Journal: Nature Computational Science

Quartile SCImago

Quartile WOS

Impact factor: 11.2

ISSN: 26628457

DOI: 10.1038/s43588-022-00369-z

Copy DOI

Computer Science Applications

Computer Science (miscellaneous)

Computer Networks and Communications

Abstract

Chemical reaction networks (CRNs), defined by sets of species and possible reactions between them, are widely used to interrogate chemical systems. To capture increasingly complex phenomena, CRNs can be leveraged alongside data-driven methods and machine learning (ML). In this Perspective, we assess the diverse strategies available for CRN construction and analysis in pursuit of a wide range of scientific goals, discuss ML techniques currently being applied to CRNs and outline future CRN-ML approaches, presenting scientific and technical challenges to overcome. Chemical reaction networks are widely used to examine the behavior of chemical systems. While diverse strategies exist for chemical reaction network construction and analysis for a wide range of scientific goals, data-driven and machine learning methods must continue to capture increasingly complex phenomena to overcome existing unmet challenges.

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Top-30

Citations by journals

	1 2 3
Journal of Chemical Information and Modeling	Journal of Chemical Information and Modeling, 3, 10.34% Journal of Chemical Information and Modeling 3 publications, 10.34%
Nature Communications	Nature Communications, 3, 10.34% Nature Communications 3 publications, 10.34%
Journal of Physical Chemistry A	Journal of Physical Chemistry A, 2, 6.9% Journal of Physical Chemistry A 2 publications, 6.9%
Scientific Reports	Scientific Reports, 2, 6.9% Scientific Reports 2 publications, 6.9%
Journal of Materials Research	Journal of Materials Research, 1, 3.45% Journal of Materials Research 1 publication, 3.45%
Journal of the American Chemical Society	Journal of the American Chemical Society, 1, 3.45% Journal of the American Chemical Society 1 publication, 3.45%
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences	Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1, 3.45% Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 1 publication, 3.45%
Computational Materials Science	Computational Materials Science, 1, 3.45% Computational Materials Science 1 publication, 3.45%
Chemical Engineering Science	Chemical Engineering Science, 1, 3.45% Chemical Engineering Science 1 publication, 3.45%
Computational Biology and Chemistry	Computational Biology and Chemistry, 1, 3.45% Computational Biology and Chemistry 1 publication, 3.45%
Nano Letters	Nano Letters, 1, 3.45% Nano Letters 1 publication, 3.45%
Inorganic Chemistry Communication	Inorganic Chemistry Communication, 1, 3.45% Inorganic Chemistry Communication 1 publication, 3.45%
Proceedings of the National Academy of Sciences of the United States of America	Proceedings of the National Academy of Sciences of the United States of America, 1, 3.45% Proceedings of the National Academy of Sciences of the United States of America 1 publication, 3.45%
Combustion and Flame	Combustion and Flame, 1, 3.45% Combustion and Flame 1 publication, 3.45%
Molecular Diversity	Molecular Diversity, 1, 3.45% Molecular Diversity 1 publication, 3.45%
Nachrichten aus der Chemie	Nachrichten aus der Chemie, 1, 3.45% Nachrichten aus der Chemie 1 publication, 3.45%
New Generation Computing	New Generation Computing, 1, 3.45% New Generation Computing 1 publication, 3.45%
Ecological Frontiers	Ecological Frontiers, 1, 3.45% Ecological Frontiers 1 publication, 3.45%
Desalination	Desalination, 1, 3.45% Desalination 1 publication, 3.45%
Journal of Chemical Physics	Journal of Chemical Physics, 1, 3.45% Journal of Chemical Physics 1 publication, 3.45%
Nature Computational Science	Nature Computational Science, 1, 3.45% Nature Computational Science 1 publication, 3.45%
	1 2 3

Citations by publishers

	1 2 3 4 5 6 7 8
Springer Nature	Springer Nature, 8, 27.59% Springer Nature 8 publications, 27.59%
American Chemical Society (ACS)	American Chemical Society (ACS), 7, 24.14% American Chemical Society (ACS) 7 publications, 24.14%
Elsevier	Elsevier, 7, 24.14% Elsevier 7 publications, 24.14%
Cambridge University Press	Cambridge University Press, 1, 3.45% Cambridge University Press 1 publication, 3.45%
The Royal Society	The Royal Society, 1, 3.45% The Royal Society 1 publication, 3.45%
Proceedings of the National Academy of Sciences (PNAS)	Proceedings of the National Academy of Sciences (PNAS), 1, 3.45% Proceedings of the National Academy of Sciences (PNAS) 1 publication, 3.45%
Cold Spring Harbor Laboratory	Cold Spring Harbor Laboratory, 1, 3.45% Cold Spring Harbor Laboratory 1 publication, 3.45%
Wiley	Wiley, 1, 3.45% Wiley 1 publication, 3.45%
American Institute of Physics (AIP)	American Institute of Physics (AIP), 1, 3.45% American Institute of Physics (AIP) 1 publication, 3.45%
	1 2 3 4 5 6 7 8

We do not take into account publications without a DOI.
Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
Statistics recalculated weekly.

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Wen M. et al. Chemical reaction networks and opportunities for machine learning // Nature Computational Science. 2023. Vol. 3. No. 1. pp. 12-24.

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Wen M., Spotte-Smith E. W. C., Blau S. M., Mcdermott M. J., Krishnapriyan A. S., Persson K. Chemical reaction networks and opportunities for machine learning // Nature Computational Science. 2023. Vol. 3. No. 1. pp. 12-24.

RIS |

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RIS Copy

TY - JOUR

DO - 10.1038/s43588-022-00369-z

UR - https://doi.org/10.1038/s43588-022-00369-z

TI - Chemical reaction networks and opportunities for machine learning

T2 - Nature Computational Science

AU - Wen, Mingjian

AU - Spotte-Smith, Evan Walter Clark

AU - Blau, Samuel M

AU - Mcdermott, Matthew J

AU - Krishnapriyan, Aditi S

AU - Persson, Kristin

PY - 2023

DA - 2023/01/16 00:00:00

PB - Springer Nature

SP - 12-24

IS - 1

VL - 3

SN - 2662-8457

ER -

BibTex |

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BibTex Copy

@article{2023_Wen,

author = {Mingjian Wen and Evan Walter Clark Spotte-Smith and Samuel M Blau and Matthew J Mcdermott and Aditi S Krishnapriyan and Kristin Persson},

title = {Chemical reaction networks and opportunities for machine learning},

journal = {Nature Computational Science},

year = {2023},

volume = {3},

publisher = {Springer Nature},

month = {jan},

url = {https://doi.org/10.1038/s43588-022-00369-z},

number = {1},

pages = {12--24},

doi = {10.1038/s43588-022-00369-z}

}

MLA

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Wen, Mingjian, et al. “Chemical reaction networks and opportunities for machine learning.” Nature Computational Science, vol. 3, no. 1, Jan. 2023, pp. 12-24. https://doi.org/10.1038/s43588-022-00369-z.

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Publisher

Springer Nature

Journal

Nature Computational Science

Quartile SCImago

Quartile WOS

Impact factor

11.2

ISSN

26628457 (Electronic)

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