Accelerating the Selection of Covalent Organic Frameworks with Automated Machine Learning
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- @Article{yang:2021:omega,
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author = "Peisong Yang and Huan Zhang and Xin Lai and
Kunfeng Wang and Qingyuan Yang and Duli Yu",
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title = "Accelerating the Selection of Covalent Organic
Frameworks with Automated Machine Learning",
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journal = "ACS omega",
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year = "2021",
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volume = "6",
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number = "27",
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pages = "17149--17161",
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month = jul # " 13",
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keywords = "genetic algorithms, genetic programming, TPOT,
methane, natural gas",
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ISSN = "2470-1343",
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DOI = "doi:10.1021/acsomega.0c05990",
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abstract = "Covalent organic frameworks (COFs) have the advantages
of high thermal stability and large specific surface
and have great application prospects in the fields of
gas storage and catalysis. This article mainly focuses
on COFs' working capacity of methane (CH(4)). Due to
the vast number of possible COF structures, it is
time-consuming to use traditional calculation methods
to find suitable materials, so it is important to apply
appropriate machine learning (ML) algorithms to build
accurate prediction models. A major obstacle for the
use of ML algorithms is that the performance of an
algorithm may be affected by many design decisions.
Finding appropriate algorithm and model parameters is
quite a challenge for nonprofessionals. In this work,
we use automated machine learning (AutoML) to analyze
the working capacity of CH(4) based on 403,959 COFs. We
explore the relationship between 23 features such as
the structure, chemical characteristics, atom types of
COFs, and the working capacity. Then, the tree-based
pipeline optimization tool (TPOT) in AutoML and the
traditional ML methods including multiple linear
regression, support vector machine, decision tree, and
random forest that manually set model parameters are
compared. It is found that the TPOT can not only save
complex data preprocessing and model parameter tuning
but also show higher performance than traditional ML
models. Compared with traditional grand canonical Monte
Carlo simulations, it can save a lot of time. AutoML
has broken through the limitations of professionals so
that researchers in nonprofessional fields can realize
automatic parameter configuration for experiments to
obtain highly accurate and easy-to-understand results,
which is of great significance for material
screening.",
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notes = "PMID: 34278102",
- }
Genetic Programming entries for
Peisong Yang
Huan Zhang
Xin Lai
Kunfeng Wang
Qingyuan Yang
Duli Yu
Citations