Training Model for Predicting Adsorption Energy of Metal Ions Based on Machine Learning
Source abstract
Abstract
The adsorption behavior of graphene oxide and metal ions was simulated theoretically by density functional theory. In the process of training the prediction model based on the machine learning method, the missing values were processed by matrix completion method, which was widely used in the recommendation systems, and gradient boosting machine (GBM) was trained to explain the importance of factors that affect the adsorption energy.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Water Quality Monitoring and Analysis · Neural Networks and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (34)
Show source-provided references
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. Journal of Molecular Liquids, 2017, 230:496-504.10.1016/j.molliq.2017.01.064 ↗
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. Chemosphere, 2020, 248:126008.10.1016/j.chemosphere.2020.126008 ↗
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. Scientia Sinica Chimica, 2019, 49(1):91-102.10.1360/N032018-00129 ↗
4. PENG X J, WANG Y F. Efficient stochastic simulation algorithm for chemically reacting systems based on support vector regression. Chinese Journal of Chemical Physics, 2009, 22(5):502-510.10.1088/1674-0068/22/05/502-510 ↗
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. Acta Chimica Sinica, 2020, 78(5):427.10.6023/A20030065 ↗
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. Computational Materials Science, 2020, 174:109497-16.10.1016/j.commatsci.2019.109474 ↗
7. LI X, XI L L, YANG J. First principles high-throughput research on thermoelectric materials: a review. Journal of Inorganic Materials, 2019, 34(3):236-246.10.15541/jim20180321 ↗
8. MENG Y, WANG X, YANG J, et al. Research on machine learning based model for predicting the impact status of laminated glass. Journal of Inorganic Materials, 2021, 36(1):61-68.10.15541/jim20200187 ↗
9. FENG C, SHARMAN E, YE S, et al. A neural network protocol for predicting molecular bond energy. Sci. China Chem., 2019, 62(12):1698-1703.10.1007/s11426-019-9619-8 ↗
10. LU S, ZHOU Q, OUYANG Y, et al. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nature Communications, 2018, 9(1):3405.10.1038/s41467-018-05761-w ↗
11. TEHRANI A M, OLIYNYK A O, PARRY M, et al. Machine learning directed search for ultraincompressible, superhard materials. Journal of the American Chemical Society, 2018, 140(31):9844-9853.10.1021/jacs.8b02717 ↗
12. KANG Y, LI L, LI B. Recent progress on discovery and properties prediction of energy materials: simple machine learning meets complex quantum chemistry. Journal of Energy Chemistry, 2020, 54:72-88.10.1016/j.jechem.2020.05.044 ↗
13. BROCKHERDE F, VOGT L, LI L, et al. By-passing the Kohn-Sham equations with machine learning. Nature Communications, 2017, 8(1):872.10.1038/s41467-017-00839-3 ↗
14. XIAO Y, MIARA L J, WANG Y, et al. Computational screening of cathode coatings for solid-state batteries. Joule, 2019, 3(5):1252-1275.10.1016/j.joule.2019.02.006 ↗
15. PANAPITIYA G, AVENDANO-FRANCO G, REN P, et al. Machine learning prediction of CO adsorption in thiolated, Ag alloyed Au nanoclusters. Journal of the American Chemical Society, 2018, 140(50):17508-17514.10.1021/jacs.8b08800 ↗
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). ACS Combinatorial Science, 2017, 19(10):640-645.10.1021/acscombsci.7b00056 ↗
17. SI Y, SAMULSKIE T. Synthesis of water soluble graphene. Nano Letters, 2008, 8(6):1679-1682.10.1021/nl080604h ↗
18. CHEN D, FENG H, LI J. Graphene oxide: preparation, functionalization, and electrochemical applications. Chemical Reviews, 2012, 112(11):6027-6053.10.1021/cr300115g ↗
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. ACS Nano, 2011, 5(6):4350-4358.10.1021/nn103584t ↗
20. MAURIZIO C, VICENZO B, MICHAEL A R. A direct procedure for the evaluation of solvent effects in MC-SCF calculations. Journal of Chemical Physics, 1999, 111(12):5295-5302.10.1063/1.479788 ↗
21. BRAND M. Incremental Singular Value Decomposition of Uncertain Data with Missing Values. Computer Vision-ECCV 2002, Berlin, Heidelberg, 2002: 707-720.10.1007/3-540-47969-4_47 ↗
22. NEELAKANTAN A, VILNIS L, LEQ V, et al. Adding gradiant noise improves learning for very deep networks. arXiv: Machine Learning, 2015, 1511:06807.
23. 迪安J A, 魏俊发. 兰氏化学手册, 2版. 北京: 科学出版社, 2003: 1-1579.
24. FRIEDMAN J H, JAO S. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 2001, 29(5):1189-1232.10.1214/aos/1013203451 ↗
25. LIASHCHYNSKYI P, LIASHCHYNSKYI P. Grid search, random search, genetic algorithm: a big comparison for NAS. arXiv: Learning, 2019, 1912:06059.
26. MARTENS H A, DARDENNE P J, CSYSTEMS I L. Validation and verification of regression in small data sets. ChemomERTics & Intelligent Laboratory Systems, 1998, 44(1/2):99-121.10.1016/S0169-7439(98)00167-1 ↗
27. FRIEDMAN J H. Stochastic gradient boosting. Computational Statistics & Data Analysis, 2002, 38(4):367-378.10.1016/S0167-9473(01)00065-2 ↗
28. SMOLA A J, SCHOLKOPF B. A tutorial on support vector regression. Statistics and Computing, 2004, 14(3):199-222.10.1023/B:STCO.0000035301.49549.88 ↗
29. RUPP M. Machine learning for quantum mechanics in a nutshell. International Journal of Quantum Chemistry, 2015, 115(16):1058-1073.10.1002/qua.24954 ↗
30. SVETNIK V. Random forest: a classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information and Modeling, 2003, 43(6):1947-1958.10.1021/ci034160g ↗
31. GEURTS P, ERNST D, WEHENKEL L. Extremely randomized trees. Machine Learning, 2006, 63(1):3-42.10.1007/s10994-006-6226-1 ↗
32. DRUCKER H. Improving regressors using boosting techniques. Morgan Kaufmann Publishers Inc, 1997: 107-115.
33. FRANLLIN J. The elements of statistical learning: data mining, inference, and prediction. The Mathematical Intelligencer, 2005, 27(2):83-85.10.1007/BF02985802 ↗
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. Journal of Molecular Modeling, 2019, 25(9):267.10.1007/s00894-019-4151-z ↗