Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network

Yao, Kun; Herr, John E.; Brown, Seth N.; Parkhill, John

doi:10.1021/acs.jpclett.7b01072

Cited by 126Machine Learning in Materials ScienceAdvanced Chemical Physics StudiesComputational Drug Discovery Methods

Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network

Yao, Kun, Herr, John E., Brown, Seth N., Parkhill, John·2017·The Journal of Physical Chemistry Letters

DOI 10.1021/acs.jpclett.7b01072

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Abstract

Neural networks are being used to make new types of empirical chemical models as inexpensive as force fields, but with accuracy similar to the ab initio methods used to build them. In this work, we present a neural network that predicts the energies of molecules as a sum of intrinsic bond energies. The network learns the total energies of the popular GDB9 database to a competitive MAE of 0.94 kcal/mol on molecules outside of its training set, is naturally linearly scaling, and applicable to molecules consisting of thousands of bonds. More importantly, it gives chemical insight into the relative strengths of bonds as a function of their molecular environment, despite only being trained on total energy information. We show that the network makes predictions of relative bond strengths in good agreement with measured trends and human predictions. A Bonds-in-Molecules Neural Network (BIM-NN) learns heuristic relative bond strengths like expert synthetic chemists, and compares well with ab initio bond order measures such as NBO analysis.

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Authors: Yao, K., Herr, J. E., Brown, S. N., & Parkhill, J.
Affiliations: University of Notre Dame (US)
Corresponding author: Parkhill, John
ORCID: Yao, Kun
Herr, John E.
Brown, Seth N.
Parkhill, John
Venue: The Journal of Physical Chemistry Letters
Publisher: American Chemical Society (ACS)
Published: 2017-06-05
Volume: 8
Issue: 12
Pages: 2689-2694
ISSN: 1948-7185 (ISSN-L)
DOI: 10.1021/acs.jpclett.7b01072
OpenAlex: W2620906374
Cited by: 126
References: 78
Citations by year: 2025: 8 · 2024: 12 · 2023: 13 · 2022: 11 · 2021: 15 · 2020: 17 · 2019: 21 · 2018: 24 · 2017: 5
FWCI: 7.74
Type: journal article
Language: en
Open access: closed
Locations: The Journal of Physical Chemistry Letters (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords: bond order analysis

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Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network

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