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The prediction of crystal densities of a big data set using 1D and 2D structure features

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Abstract

A large data set of over 30 thousand organic compounds containing carbon, nitrogen, oxygen, fluorine, and hydrogen was collected, and the density of each compound was predicted by 1D descriptors derived from its molecular formula and 2D descriptors derived from its constitutional structural features. The 2D structural features are composed of Benson’s groups, corrected groups, and 2D structural features of the whole molecular structures. All the descriptors were extracted by an in-house program in Java with a function to ensure that each atom (or bond) of molecules is represented by Benson’s groups once for atom-based (or bond-based) descriptors. Partial least square (PLS) and random forest (RF) methods were used separately to build models to predict the density. Further, the variable selection of descriptors was performed by variable importance of RF. For partial least square, the combination of the models constructed by descriptors based on the atoms and the bonds achieved the best results in this paper: for the cross-validation of the training set, the Pearson correlation coefficient (R) = 0.9270, mean absolute error (MAE) = 0.0270 g·cm−3, and root mean squared error (RMSE) = 0.0426 g·cm−3; for the prediction of the test set, R = 0.9454, MAE = 0.0263 g·cm−3, and RMSE = 0.0375 g·cm−3.

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Data availability

CCDC contains density data for various compounds. These data can be obtained from https://www.ccdc.cam.ac.uk/.

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Funding

This research work was financially supported by the Open Research Fund Program of Science and Technology on Aerospace Chemical Power Laboratory (No.: 120201B01) and the National Natural Science Foundation of China (Nos.: 21875061 and 21975066).

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Author notes

  1. Xianlan Li and Dingling Kong contribute equally to the article.

Authors and Affiliations

  1. Henan Engineering Research Center of Industrial Circulating Water Treatment, Henan Joint International Research Laboratory of Environmental Pollution Control Materials, Henan University, Kaifeng, 475004, People’s Republic of China

    Xianlan Li, Dingling Kong, Yue Luan, Lili Guo & Qingyou Zhang

  2. Science and Technology on Aerospace Chemical Power Laboratory, Hubei Institute of Aerospace Chemotechnology, Xiangyang, 441003, Hubei, China

    Yanhua Lu, Wei Li & Aimin Pang

  3. School of Physics, Harbin Institute of Technology, 150001, Harbin, People’s Republic of China

    Meng Tang

Authors
  1. Xianlan Li
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  2. Dingling Kong
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  3. Yue Luan
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  4. Lili Guo
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  5. Yanhua Lu
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  6. Wei Li
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  8. Qingyou Zhang
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Contributions

Data collection and analysis were performed by Xianlan Li, Dingling Kong, Yue Luan, Lili Guo, Yanhua Lu, Wei Li and Meng Tang. The first draft of the manuscript was written by Qingyou Zhang and Aimin Pang. All authors read and approved the final manuscript.

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Correspondence to Qingyou Zhang or Aimin Pang.

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Li, X., Kong, D., Luan, Y. et al. The prediction of crystal densities of a big data set using 1D and 2D structure features. Struct Chem (2024). https://doi.org/10.1007/s11224-024-02279-4

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