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Cited by 48Machine Learning in Materials ScienceComputational Drug Discovery MethodsHistory and advancements in chemistry

Machine-learning models for combinatorial catalyst discovery

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Landrum, Gregory A, Penzotti, Julie E, Putta, Santosh·2005·Measurement Science and Technology
DOI 10.1088/0957-0233/16/1/035
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Abstract

A variety of machine learning algorithms, including hierarchical clustering, decision trees, k -nearest neighbours, support vector machines and bagging, were applied to construct models to predict the molecular weight of the polymers produced by a set of 96 homogeneous catalysts. The goal of the study was to develop models that could be used to screen large virtual libraries of catalysts in order to suggest candidates for further synthesis and screening. The descriptors used to represent the catalysts did not require detailed information about the catalysts themselves; they could be calculated using only the topology of the ligands. Using an initial set of five descriptors, model accuracies of about 70% were observed from each learning algorithm. A larger descriptor set (with ten descriptors) allowed bag classifiers that were 80% accurate to be built. All models were carefully evaluated to detect overfitting (memorization of the training data) and one example of the effects of overfitting is provided. Because the descriptors used in this study can be calculated very rapidly and the models themselves are very efficient, these bag classifiers are well suited to screening very large virtual libraries.

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  • Although descriptors and representations have evolved with the increasing use of more sophisticated ML models in recent years, demonstrations in the early 2000s bear striking similarity to many modern applications.

    “Although descriptors and representations have evolved with the increasing use of more sophisticated ML models in recent years, demonstrations in the early 2000s (5-8, 42) bear striking similarity to many modern applications (32,43,(72)(73)(74).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [8]

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Research works printed but not linked: 2

  1. Landrum G A, Penzotti J E and Putta S 2003 Machine-learning models for high-throughput materials discovery 225th Am. Chem. Soc. Conf. (New Orleans, LA)
  2. Mitchell T 1997 Machine Learning (New York: McGraw-Hill)

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Authors
Landrum, G. A., Penzotti, J. E., & Putta, S.
ORCID
Landrum, Gregory A
Venue
Measurement Science and Technology
Publisher
IOP Publishing
Published
2004-12-18
Volume
16
Issue
1
Pages
270-277
ISSN
  • 0957-0233 (ISSN-L)
  • 1361-6501
DOI
10.1088/0957-0233/16/1/035
OpenAlex
W2067090670
Cited by
48
References
42
Citations by year
2026: 1 · 2025: 4 · 2023: 4 · 2022: 6 · 2021: 7 · 2020: 4 · 2019: 7 · 2018: 2 · 2015: 1 · 2013: 2
FWCI
0.62
Type
journal article
Language
en
Open access
Locations
Measurement Science and Technology (publishedVersion)
Funders
National Science Foundation
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › History and advancements in chemistry
Keywords
catalyst discovery, homogeneous catalysts, bagging classifier, overfitting, support vector machine, k-nearest neighbors, decision tree, hierarchical clustering, combinatorial chemistry

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