Machine Learning in Complex Organic Mixtures: Applying Domain Knowledge Allows for Meaningful Performance with Small Data Sets-Reference-Cited by-同舟云学术

Machine Learning in Complex Organic Mixtures: Applying Domain Knowledge Allows for Meaningful Performance with Small Data Sets

Published:2024-07-31 Issue:32 Volume:146 Page:22563-22569
ISSN:0002-7863
Container-title:Journal of the American Chemical Society
language:en
Short-container-title:J. Am. Chem. Soc.

Author:

Le Katelyn¹,Radović Jagoš R.²,MacCallum Justin L.¹^ORCID,Larter Stephen R.³,Van Humbeck Jeffrey F.¹^ORCID

Affiliation:

1. Department of Chemistry, University of Calgary, Calgary, Alberta T2N 1N4, Canada

2. Center for Petroleum Geochemistry (UH-CPG), Department of Earth and Atmospheric Sciences, University of Houston, Houston, Texas 77204-5007, United States

3. Department of Earth, Energy, and Environment, University of Calgary, Calgary, Alberta T2N 1N4, Canada

Funder

Canada Research Chairs

Canada First Research Excellence Fund

Alberta Innovates

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/jacs.4c06595

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5. Elemental Composition Analysis of Processed and Unprocessed Diesel Fuel by Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry