[1] PENG W J, LI H Q, LIU Y Y et al. A review on heavy metalions adsorption from water by graphene oxide and its composites[J]. Journal of Molecular Liquids, 230, 496-504(2017).

[2] AHMAD S Z N, SALLEH W N W, ISMAIL A F et al. Adsorptive removal of heavy metal ions using graphene-based nanomaterials: toxicity, roles of functional groups and mechanisms[J]. Chemosphere, 248, 126008(2020).

[3] LIU Y, ZHAO C F, ZHANG A R et al. Theoretical study on the removal of uranyl by nitrogen, phosphorus and sulfur doped graphene materials[J]. Scientia Sinica Chimica, 49, 91-102(2019).

[4] PENG X J, WANG Y F. Efficient stochastic simulation algorithm for chemically reacting systems based on support vector regression[J]. Chinese Journal of Chemical Physics, 22, 502-510(2009).

[5] CAI C, LI L, DENG X et al. Machine learning and high- throughput computational screening of metal-organic framework for separation of methane/ethane/propane[J]. Acta Chimica Sinica, 78, 427(2020).

[6] ORUPATTUR N V, MUSHRIF S H, PRASAD V. Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods[J]. Computational Materials Science, 174, 109497-16(2020).

[7] LI X, XI L L, YANG J. First principles high-throughput research on thermoelectric materials: a review[J]. Journal of Inorganic Materials, 34, 236-246(2019).

[8] MENG Y, WANG X, YANG J et al. Research on machine learning based model for predicting the impact status of laminated glass[J]. Journal of Inorganic Materials, 36, 61-68(2021).

[9] FENG C, SHARMAN E, YE S et al. A neural network protocol for predicting molecular bond energy[J]. Sci. China Chem., 62, 1698-1703(2019).

[10] LU S, ZHOU Q, OUYANG Y et al. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning[J]. Nature Communications, 9, 3405(2018).

[11] TEHRANI A M, OLIYNYK A O, PARRY M et al. Machine learning directed search for ultraincompressible, superhard materials[J]. Journal of the American Chemical Society, 140, 9844-9853(2018).

[12] KANG Y, LI L, LI B. Recent progress on discovery and properties prediction of energy materials: simple machine learning meets complex quantum chemistry[J]. Journal of Energy Chemistry, 54, 72-88(2020).

[13] BROCKHERDE F, VOGT L, LI L et al. By-passing the Kohn-Sham equations with machine learning[J]. Nature Communications, 8, 872(2017).

[14] XIAO Y, MIARA L J, WANG Y et al. Computational screening of cathode coatings for solid-state batteries[J]. Joule, 3, 1252-1275(2019).

[15] PANAPITIYA G, AVENDANO-FRANCO G, REN P et al. Machine learning prediction of CO adsorption in thiolated, Ag alloyed Au nanoclusters[J]. Journal of the American Chemical Society, 140, 17508-17514(2018).

[16] PARDAKHTI M, MOHARRERI E, WANIK D et al. Machine learning using combined structural and chemical descriptors for prediction of methane adsorption performance of metal organic frameworks (MOFs)[J]. ACS Combinatorial Science, 19, 640-645(2017).

[17] SI Y, SAMULSKIE T. Synthesis of water soluble graphene[J]. Nano Letters, 8, 1679-1682(2008).

[18] CHEN D, FENG H, LI J. Graphene oxide: preparation, functionalization, and electrochemical applications[J]. Chemical Reviews, 112, 6027-6053(2012).

[19] SHENG Z H, SHAO L, CHEN J J et al. Catalyst free synthesis of nitrogen-doped graphene via thermal annealing graphite oxide with melamine and its excellent electrocatalysis[J]. ACS Nano, 5, 4350-4358(2011).

[20] MAURIZIO C, VICENZO B, MICHAEL A R. A direct procedure for the evaluation of solvent effects in MC-SCF calculations[J]. Journal of Chemical Physics, 111, 5295-5302(1999).

[21] BRAND M. Incremental Singular Value Decomposition of Uncertain Data with Missing Values[J]. Computer Vision-ECCV 2002, Berlin, Heidelberg, 707-720(2002).

[22] NEELAKANTAN A, VILNIS L, LEQ V et al. Adding gradiant noise improves learning for very deep networks[J]. arXiv: Machine Learning, 1511, 06807(2015).

[24] FRIEDMAN J H, JAO S. Greedy function approximation: a gradient boosting machine[J]. Annals of Statistics, 29, 1189-1232(2001).

[25] LIASHCHYNSKYI P, LIASHCHYNSKYI P. Grid search, random search, genetic algorithm: a big comparison for NAS[J]. arXiv: Learning, 1912, 06059(2019).

[26] MARTENS H A, DARDENNE P J, CSYSTEMS I L. Validation and verification of regression in small data sets[J]. ChemomERTics & Intelligent Laboratory Systems, 44, 99-121(1998).

[27] FRIEDMAN J H. Stochastic gradient boosting[J]. Computational Statistics & Data Analysis, 38, 367-378(2002).

[28] SMOLA A J, SCHOLKOPF B. A tutorial on support vector regression[J]. Statistics and Computing, 14, 199-222(2004).

[29] RUPP M. Machine learning for quantum mechanics in a nutshell[J]. International Journal of Quantum Chemistry, 115, 1058-1073(2015).

[30] SVETNIK V. Random forest: a classification and regression tool for compound classification and QSAR modeling[J]. Journal of Chemical Information and Modeling, 43, 1947-1958(2003).

[31] GEURTS P, ERNST D, WEHENKEL L. Extremely randomized trees[J]. Machine Learning, 63, 3-42(2006).

[32] DRUCKER H. Improving regressors using boosting techniques[J]. Morgan Kaufmann Publishers Inc, 107-115(1997).

[33] FRANLLIN J. The elements of statistical learning: data mining, inference, and prediction[J]. The Mathematical Intelligencer, 27, 83-85(2005).

[34] SANTOS R I H, REIS D T, PEREIRA D H. A DFT based analysis of adsorption of Cd²⁺, Cr³⁺, Cu²⁺, Hg²⁺, Pb²⁺, and Zn²⁺, on vanillin monomer: a study of the removal of metalions from effluents[J]. Journal of Molecular Modeling, 25, 267(2019).