Journal of Inorganic Materials, Volume. 36, Issue 11, 1178(2021)

Training Model for Predicting Adsorption Energy of Metal Ions Based on Machine Learning

Ruihong ZHANG1... Xin WEI2, Zhanhui LU1 and Yuejie AI3,* |Show fewer author(s)
Author Affiliations
  • 11. College of Mathematics and Physics, North China Electric Power University, Beijing 102206, China
  • 22. College of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
  • 33. MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
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    Figures & Tables(7)
    (a) Thermal map of correlation between features with correlation coefficient>0.6, and (b) example of adsorption structure of GO adsorbing Cr3+
    Feature importance ranking
    Fitting effect diagram and score of six machine learning methods.
    (a) Mean square error (MSE) of the four ensemble methods, and (b-e) correlation graphs of the true and predicted values of the four ensemble methods
    (a) Example of the structure of vanillin monomer adsorbing metal ions; (b) Fitting effect graph of Extremely Randomized Trees (ERT) for VMA-Mn+ adsorption energy; (c) Correlation diagram of ERT for VMA-Mn+ adsorption energy
    • Table 1. 21 feature descriptors calculated based on DFT

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      Table 1. 21 feature descriptors calculated based on DFT

      No.Feature descriptorNo.Feature descriptorNo.Feature descriptor
      1Charge8Ionic radius15CV (Cal/mol-K)
      2Spin9Melting point16S(Cal/mol-K)
      3Atomic radius10Boiling point17Zero-point vibrational energy/(kCal·mol-1)
      4Atomic number11First ionization energy18Molecular mass
      5Atomic weight12Electronegativity19Mulliken charges
      6Density/(g·cm-3) 13M-O (bond length)20APT charges
      7Atomic volume14E(Thermal)/(kCal·mol-1) 21Dipole moment/D
    • Table 2. Optimal hyperparameters of six machine learning methods

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      Table 2. Optimal hyperparameters of six machine learning methods

      CategoryMethodOptimal hyperparameters
      KernelSupport vector regression (SVR)C = 2, kernel=“ rbf ”
      Ridge regressionAlpha = 30
      Random forestRandom forest (RF)n_estimators = 31, max_depth = 6, max_features = 2
      Extremely randomized trees (ERT)n_estimators = 31, max_depth = 7, random_state = 1
      BoostingExtreme gradient boosting (XGBoost)n_estimators = 31, max_depth = 2, min_child_weight = 13, learning_rate =.32
      Light gradient boosting machine (LightGBM)n_estimators =17, objective = ‘regression’, num_leaves = 31, learning_ rate = 0.32
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    Ruihong ZHANG, Xin WEI, Zhanhui LU, Yuejie AI. Training Model for Predicting Adsorption Energy of Metal Ions Based on Machine Learning[J]. Journal of Inorganic Materials, 2021, 36(11): 1178

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    Paper Information

    Category: RESEARCH ARTICLE

    Received: Dec. 31, 2020

    Accepted: --

    Published Online: Dec. 20, 2021

    The Author Email: AI Yuejie (aiyuejie@ncepu.edu.cn)

    DOI:10.15541/jim20200748

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