First-principles, machine learning and symbolic regression modelling for organic molecule adsorption on two-dimensional CaO surface
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- @Article{HU:2023:jmgm,
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author = "Wenguang Hu and Lei Zhang",
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title = "First-principles, machine learning and symbolic
regression modelling for organic molecule adsorption on
two-dimensional {CaO} surface",
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journal = "Journal of Molecular Graphics and Modelling",
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volume = "124",
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pages = "108530",
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year = "2023",
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ISSN = "1093-3263",
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DOI = "doi:10.1016/j.jmgm.2023.108530",
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URL = "https://www.sciencedirect.com/science/article/pii/S1093326323001286",
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keywords = "genetic algorithms, genetic programming, Machine
learning, Symbolic regression, Two-dimensional,
Adsorption, Data-driven",
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abstract = "Data-driven methods are receiving significant
attention in recent years for chemical and materials
researches; however, more works should be done to
leverage the new paradigm to model and analyze the
adsorption of the organic molecules on low-dimensional
surfaces beyond using the traditional simulation
methods. In this manuscript, we employ machine learning
and symbolic regression method coupled with DFT
calculations to investigate the adsorption of
atmospheric organic molecules on a low-dimensional
metal oxide mineral system. The starting dataset
consisting of the atomic structures of the
organic/metal oxide interfaces are obtained via the
density functional theory (DFT) calculation and
different machine learning algorithms are compared,
with the random forest algorithm achieving high
accuracies for the target output. The feature ranking
step identifies that the polarizability and bond type
of the organic adsorbates are the key descriptors for
the adsorption energy output. In addition, the symbolic
regression coupled with genetic programming
automatically identifies a series of hybrid new
descriptors displaying improved relevance with the
target output, suggesting the viability of symbolic
regression to complement the traditional machine
learning techniques for the descriptor design and fast
modeling purposes. This manuscript provides a framework
for effectively modeling and analyzing the adsorption
of the organic molecules on low-dimensional surfaces
via comprehensive data-driven approaches",
- }
Genetic Programming entries for
Wenguang Hu
Lei Zhang
Citations