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2000
Volume 21, Issue 4
  • ISSN: 1573-4110
  • E-ISSN: 1875-6727

Abstract

Background

The corrosion of Mild Steel (MS) in harsh acidic environments, such as Hydrochloric acid (HCl), is a significant industrial issue with environmental consequences. Corrosion inhibitors, particularly those containing heteroatoms and aromatic rings, are a proven method for mitigating corrosion. Traditional methods for studying corrosion inhibitors often require resource-intensive experiments.

Methods

This study explores the use of Quantitative Structure-Property Relationship (QSPR) modeling, a Machine Learning (ML) technique, to predict the inhibition efficiency of organic corrosion inhibitors in HCl environments. Several ML models were employed: Linear Regression (LR), Random Forest Regression (RF), Support Vector Regression (SVR), Multilayer Perceptron Regression (MLP), and XGBoost Regression (XGB).

Results

The investigation revealed that some models achieved exceptional predictive accuracy with significantly reduced errors and high precision. These models offer a promising avenue for efficient corrosion inhibitor design, reducing reliance on extensive experimentation.

Conclusion

This study contributes to the advancement of corrosion science and materials engineering by introducing innovative strategies for developing effective corrosion inhibitors using machine-learning-driven QSPR models.

© 2025 Bentham Science Publishers
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2025-09-30

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References

  1. MissiouiM. Bouziani IdrissiM. BenhibaF. AtioğluZ. AkkurtM. OuddaH. MagueJ.T. EssassiE.M. ZarroukA. RamliY. Synthesis, structural characterization, Hirshfeld surface analysis and anti-corrosion on mild steel in 1M HCl of ethyl 2-(3-methyl-2-oxo-1,2-dihydroquinoxaline-1-yl)acetate.J. Mol. Struct.20221251Dec13204710.1016/j.molstruc.2021.132047
     [Google Scholar]
  2. ErazuaE.A. AdelekeB.B. A computational study of quinoline derivatives as corrosion inhibitors for mild steel in acidic medium.J. Appl. Sci. Environ. Manag.201923101819182410.4314/jasem.v23i10.8
     [Google Scholar]
  3. El FaydyM. BenhibaF. TimoudanN. LakhrissiB. WaradI. SaoiabiS. GuenbourA. BentissF. ZarroukA. Experimental and theoretical examinations of two quinolin-8-ol-piperazine derivatives as organic corrosion inhibitors for C35E steel in hydrochloric acid.J. Mol. Liq.202235411890010.1016/j.molliq.2022.118900
     [Google Scholar]
  4. Al-BaghdadiS.B. Al-KhazaaliA.E. Al-AzawiK.F. A comprehensive review on the nature and synthetic organic compounds as corrosion inhibitors.International Journal of Research in Engineering and Innovation20237310211110.36037/IJREI.2023.7302
     [Google Scholar]
  5. SalmanT.A. Al-AmieryA.A. ShakerL.M. KadhumA.A.H. TakriffM.S. A study on the inhibition of mild steel corrosion in hydrochloric acid environment by 4-methyl-2-(pyridin-3-yl)thiazole-5-carbohydrazide.International Journal of Corrosion and Scale Inhibition2019841035105910.17675/2305‑6894‑2019‑8‑4‑14
     [Google Scholar]
  6. HadizadehM.H. HamadanianM. Evaluation of corrosion inhibition efficiency of some novel Schiff bases through a proposed QSAR model: DFT investigations supported by weight loss technique.International Journal of Corrosion and Scale Inhibition20211041516153010.17675/2305‑6894‑2021‑10‑4‑9
     [Google Scholar]
  7. QuadriT.W. OlasunkanmiL.O. FayemiO.E. LgazH. DagdagO. SherifE-S.M. AlrashdiA.A. AkpanE.D. LeeH-S. EbensoE.E. Computational insights into quinoxaline-based corrosion inhibitors of steel in HCl: Quantum chemical analysis and QSPR-ANN studies.Arab. J. Chem.202215710387010.1016/j.arabjc.2022.103870
     [Google Scholar]
  8. ÇetinerE. SayınK. ÜnalY. Optimization, spectral characterization, QSAR, and molecular docking analyses of newly designed boron compounds.Struct. Chem.20233451731174210.1007/s11224‑022‑02086‑9
     [Google Scholar]
  9. DFT-QSAR studies on corrosion inhibition efficiency of derivatives of thiadiazole, oxadiazole and triazole.International Journal of Corrosion and Scale Inhibition20165324826210.17675/2305‑6894‑2016‑5‑3‑5
     [Google Scholar]
  10. AndréT.M. KonéA. RenéK.N. YeoM. NiamienP.M. Copper corrosion inhibition in nitric acid solution by 2-(1,3-dihydrobenzimidazol-2-ylidene) -3-oxo-3-(pyridin-3-yl) propanenitrile: Gravimetric, quantum chemical and QSPR studies.Mediterr. J. Chem.202212212313910.13171/mjc02209131649tigori
     [Google Scholar]
  11. Beltran-PerezC. SerranoA.A.A. Solís-RosasG. Martínez-JiménezA. Orozco-CruzR. Espinoza-VázquezA. MiralrioA. A general use QSAR-ARX model to predict the corrosion inhibition efficiency of drugs in terms of quantum mechanical descriptors and experimental comparison for lidocaine.Int. J. Mol. Sci.2022239508610.3390/ijms23095086 35563474
     [Google Scholar]
  12. Camacho-MendozaR.L. FeriaL. Zárate-HernándezL.Á. Alvarado-RodríguezJ.G. Cruz-BorbollaJ. New QSPR model for prediction of corrosion inhibition using conceptual density functional theory.J. Mol. Model.202228823810.1007/s00894‑022‑05240‑6 35906451
     [Google Scholar]
  13. AkromM. RustadS. SaputroA.G. RamelanA. FathurrahmanF. DipojonoH.K. A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds.Mater. Today Commun.20233510640210.1016/j.mtcomm.2023.106402
     [Google Scholar]
  14. AkromM. RustadS. DipojonoH.K. Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds.Mater. Today Commun.20243910875810.1016/j.mtcomm.2024.108758
     [Google Scholar]
  15. AkromM. RustadS. SaputroA.G. DipojonoH.K. Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors.Comput. Theor. Chem.2023122911430710.1016/j.comptc.2023.114307
     [Google Scholar]
  16. QuraishiM.A. ChauhanD.S. SajiV.S. Heterocyclic biomolecules as green corrosion inhibitors.J. Mol. Liq.202134111726510.1016/j.molliq.2021.117265
     [Google Scholar]
  17. HadisaputraS. IrhamA.D. PurwokoA.A. JunaidiE. HakimA. Development of QSPR models for furan derivatives as corrosion inhibitors for mild steel.Int. J. Electrochem. Sci.202318810020710.1016/j.ijoes.2023.100207
     [Google Scholar]
  18. El AssiriE.H. DriouchM. BensoudaZ. JhilalF. SaffajT. SfairaM. AbboudY. Quantum chemical and QSPR studies of bis-benzimidazole derivatives as corrosion inhibitors by using electronic and lipophilic descriptors.Desalination Water Treat.201811120822510.5004/dwt.2018.22198
     [Google Scholar]
  19. MoumenI. AbouchabakaJ. RafaliaN. Adaptive traffic lights based on traffic flow prediction using machine learning models.International Journal of Electrical and Computer Engineering (IJECE)20231355813582310.11591/ijece.v13i5.pp5813‑5823
     [Google Scholar]
  20. ConsonniV. TodeschiniR. Molecular descriptors.Challenges and Advances in Computational Chemistry and Physics200982910210.1007/978‑1‑4020‑9783‑6_1
     [Google Scholar]
  21. AlamriA.H. AlhazmiN. Development of data driven machine learning models for the prediction and design of pyrimidine corrosion inhibitors.J. Saudi Chem. Soc.202226610153610.1016/j.jscs.2022.101536
     [Google Scholar]
  22. RavitejaK.V.N.S. KavyaK.V.B.S. SenapatiR. ReddyK.R. Machine-learning modelling of tensile force in anchored geomembrane liners.Geosynth. Int.202310.1680/jgein.22.00377
     [Google Scholar]
  23. SongX. WangK. ZhouL. ChenY. RenK. WangJ. ZhangC. Multi-factor mining and corrosion rate prediction model construction of carbon steel under dynamic atmospheric corrosion environment.Eng. Fail. Anal.202213410598710.1016/j.engfailanal.2021.105987
     [Google Scholar]
  24. OuakkiM. GalaiM. RbaaM. AbousalemA.S. LakhrissiB. TouhamiM.E. CherkaouiM. Electrochemical, thermodynamic and theoretical studies of some imidazole derivatives compounds as acid corrosion inhibitors for mild steel.J. Mol. Liq.202031911406310.1016/j.molliq.2020.114063
     [Google Scholar]
  25. LaabaissiT. BenhibaF. MissiouiM. RouifiZ. RbaaM. OuddaH. RamliY. GuenbourA. WaradI. ZarroukA. Coupling of chemical, electrochemical and theoretical approach to study the corrosion inhibition of mild steel by new quinoxaline compounds in 1 M HCl.Heliyon202065e0393910.1016/j.heliyon.2020.e03939 32420498
     [Google Scholar]
  26. OuakkiM. RbaaM. GalaiM. LakhrissiB. RifiE.H. CherkaouiM. Experimental and quantum chemical investigation of imidazole derivatives as corrosion inhibitors on mild steel in 1.0 M hydrochloric acid.J. Bio Tribocorros.2018433510.1007/s40735‑018‑0151‑2
     [Google Scholar]
  27. BenhibaF. HsissouR. BenzekriZ. BelghitiM.E. LamhamdiA. BellaouchouA. GuenbourA. BoukhrisS. OuddaH. WaradI. ZarroukA. Nitro substituent effect on the electronic behavior and inhibitory performance of two quinoxaline derivatives in relation to the corrosion of mild steel in 1M HCl.J. Mol. Liq.202031211336710.1016/j.molliq.2020.113367
     [Google Scholar]
  28. FergachiO. BenhibaF. RbaaM. TouirR. OuakkiM. GalaiM. LakhrissiB. OuddaH. TouhamiM.E. Experimental and theoretical study of corrosion inhibition of mild steel in 1.0 M HCl medium by 2(-4(hlorophenyl-1H-benzo[d]imidazol)-1-yl)phenyl) methanone.Mater. Res.201821610.1590/1980‑5373‑mr‑2017‑1038
     [Google Scholar]
  29. BenhibaF. BenzekriZ. GuenbourA. TabyaouiM. BellaouchouA. BoukhrisS. OuddaH. WaradI. ZarroukA. Combined electronic/atomic level computational, surface (SEM/EDS), chemical and electrochemical studies of the mild steel surface by quinoxalines derivatives anti-corrosion properties in 1 mol⋅L-1 HCl solution.Chin. J. Chem. Eng.20202851436145810.1016/j.cjche.2020.03.002
     [Google Scholar]
  30. TangY. ZhangF. HuS. CaoZ. WuZ. JingW. Novel benzimidazole derivatives as corrosion inhibitors of mild steel in the acidic media. Part I: Gravimetric, electrochemical, SEM and XPS studies.Corros. Sci.20137427128210.1016/j.corsci.2013.04.053
     [Google Scholar]
  31. ZarroukA. ZarrokH. RamliY. BouachrineM. HammoutiB. Sahibed-dineA. BentissF. Inhibitive properties, adsorption and theoretical study of 3,7-dimethyl-1-(prop-2-yn-1-yl)quinoxalin-2(1H)-one as efficient corrosion inhibitor for carbon steel in hydrochloric acid solution.J. Mol. Liq.201622223925210.1016/j.molliq.2016.07.046
     [Google Scholar]
  32. OuakkiM. GalaiM. BenzekriZ. VermaC. Ech-chihbiE. KayaS. BoukhrisS. EbensoE.E. TouhamiM.E. CherkaouiM. Insights into corrosion inhibition mechanism of mild steel in 1 M HCl solution by quinoxaline derivatives: Electrochemical, SEM/EDAX, UV-visible, FT-IR and theoretical approaches.Colloids Surf. A Physicochem. Eng. Asp.202161112581010.1016/j.colsurfa.2020.125810
     [Google Scholar]
  33. LiW. HeQ. PeiC. HouB. Experimental and theoretical investigation of the adsorption behaviour of new triazole derivatives as inhibitors for mild steel corrosion in acid media.Electrochim. Acta200752226386639410.1016/j.electacta.2007.04.077
     [Google Scholar]
  34. GalaiM. RbaaM. OuakkiM. GuoL. DahmaniK. NounehK. BricheS. LakhrissiB. DkhirecheN. Ebn TouhamiM. Effect of alkyl group position on adsorption behavior and corrosion inhibition of new naphthol based on 8-hydroxyquinoline: Electrochemical, surface, quantum calculations and dynamic simulations.J. Mol. Liq.202133511655210.1016/j.molliq.2021.116552
     [Google Scholar]
  35. RouifiZ. RbaaM. BenhibaF. LaabaissiT. OuddaH. LakhrissiB. GuenbourA. WaradI. ZarroukA. Preparation and anti-corrosion activity of novel 8-hydroxyquinoline derivative for carbon steel corrosion in HCl molar: Computational and experimental analyses.J. Mol. Liq.202030711292310.1016/j.molliq.2020.112923
     [Google Scholar]
  36. GalaiM. RbaaM. OuakkiM. AbousalemA.S. Ech-chihbiE. DahmaniK. DkhirecheN. LakhrissiB. EbnTouhami, M. Chemically functionalized of 8-hydroxyquinoline derivatives as efficient corrosion inhibition for steel in 1.0 M HCl solution: Experimental and theoretical studies.Surf. Interfaces20202110069510.1016/j.surfin.2020.100695
     [Google Scholar]
  37. RbaaM. OuakkiM. GalaiM. BerishaA. LakhrissiB. JamaC. WaradI. ZarroukA. Simple preparation and characterization of novel 8-Hydroxyquinoline derivatives as effective acid corrosion inhibitor for mild steel: Experimental and theoretical studies.Colloids Surf. A Physicochem. Eng. Asp.2020602May12509410.1016/j.colsurfa.2020.125094
     [Google Scholar]
  38. RbaaM. BenhibaF. AbousalemA.S. GalaiM. RouifiZ. OuddaH. LakhrissiB. WaradI. ZarroukA. Sample synthesis, characterization, experimental and theoretical study of the inhibitory power of new 8-hydroxyquinoline derivatives for mild steel in 1.0 M HCl.J. Mol. Struct.2020121312815510.1016/j.molstruc.2020.128155
     [Google Scholar]
  39. RbaaM. LgazH. El KacimiY. LakhrissiB. BentissF. ZarroukA. Synthesis, characterization and corrosion inhibition studies of novel 8-hydroxyquinoline derivatives on the acidic corrosion of mild steel: Experimental and computational studies.Mater. Discov.201812435410.1016/j.md.2018.11.003
     [Google Scholar]
  40. RbaaM. GalaiM. AbousalemA.S. LakhrissiB. TouhamiM.E. WaradI. ZarroukA. Synthetic, spectroscopic characterization, empirical and theoretical investigations on the corrosion inhibition characteristics of mild steel in molar hydrochloric acid by three novel 8-hydroxyquinoline derivatives.Ionics202026150352210.1007/s11581‑019‑03160‑9
     [Google Scholar]
  41. RbaaM. LakhrissiB. Novel oxazole and imidazole based on 8-hydroxyquinoline as a corrosion inhibition of mild steel in HCl solution: Insights from experimental and computational studies.Elsevier B.V.2019Vol. 1510.1016/j.surfin.2019.01.010
     [Google Scholar]
  42. VermaC. AbdellattifM.H. AlfantaziA. QuraishiM.A. N-heterocycle compounds as aqueous phase corrosion inhibitors: A robust, effective and economic substitute.J. Mol. Liq.202134011721110.1016/j.molliq.2021.117211
     [Google Scholar]
  43. SahuB.C. SahuB.C. Organic corrosion inhibitors.Introduction to Corrosion Basics and Advances202310.5772/intechopen.109523
     [Google Scholar]
  44. WajdiF. HadisaputraS. SumarlanI. Benzimidazole corrosion inhibition performance: A DFT Study.Acta Chimica Asiana201812434910.29303/aca.v1i2.27
     [Google Scholar]
  45. HadisaputraS. PurwokoA.A. WajdiF. SumarlanI. HamdianiS. Theoretical study of the substituent effect on corrosion inhibition performance of benzimidazole and its derivatives.International Journal of Corrosion and Scale Inhibition20198367368810.17675/2305‑6894‑2019‑8‑3‑15
     [Google Scholar]
  46. SehrawatR. VashishthP. BairagiH. ShuklaS.K. KumarH. JiG. ManglaB. Coordination bonding and corrosion inhibition characteristics of chalcone compounds for metals: An inclusive review based on experimental as well as theoretical perspectives.Coord. Chem. Rev.202451421582010.1016/j.ccr.2024.215820
     [Google Scholar]
  47. ChaouikiA. New 8-hydroxyquinoline-bearing quinoxaline derivatives as effective corrosion inhibitors for mild steel in hcl: Electrochemical and computational investigations.Coatings202010981110.3390/coatings10090811
     [Google Scholar]
  48. BerdimurodovE. VermaC. BerdimuradovK. QuraishiM.A. KholikovA. AkbarovK. UmirovN. BorikhonovB. 8–Hydroxyquinoline is key to the development of corrosion inhibitors: An advanced review.Inorg. Chem. Commun.202214410983910.1016/j.inoche.2022.109839
     [Google Scholar]
  49. El AssiriE.H. DriouchM. LazrakJ. BensoudaZ. ElhalouiA. SfairaM. SaffajT. TalebM. Development and validation of QSPR models for corrosion inhibition of carbon steel by some pyridazine derivatives in acidic medium.Heliyon2020610e0506710.1016/j.heliyon.2020.e05067 33072903
     [Google Scholar]
  50. SepehriB. A review on created QSPR models for predicting ionic liquids properties and their reliability from chemometric point of view.J. Mol. Liq.202029711201310.1016/j.molliq.2019.112013
     [Google Scholar]
  51. ŠkutaC. Cortés-CirianoI. DehaenW. KřížP. van WestenG.J.P. TetkoI.V. BenderA. SvozilD. QSAR-derived affinity fingerprints (part 1): Fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping.J. Cheminform.20201213910.1186/s13321‑020‑00443‑6 33431038
     [Google Scholar]
  52. DuZ. WangD. LiY. Comprehensive evaluation and comparison of machine learning methods in QSAR modeling of antioxidant tripeptides.ACS Omega2022729257602577110.1021/acsomega.2c03062 35910147
     [Google Scholar]
  53. VidyasrilekhaV. SanapalliB.K.R. MohammedA.A. DFT and molecular dynamics simulation studies of 4-(2-(2-(2-Chloroacetamido) phenoxy)acetamido)-3-nitrobenzoic acid and 4-(2-(Benzo[D]thiazol- 2-ylthio)acetamido)-3-nitrobenzoic acid against escherichia coli pare enzyme.Lett. Drug Des. Discov.20221910.2174/1570180819666220811102929
     [Google Scholar]
  54. Schrödinger Release 2021-4.LigPrepSchrödinger, LLC: New York, NY2022
     [Google Scholar]
  55. AryaM. TiwariA. SinghD.B. TajG. Computational study of lactucine and its derivatives to investigate its anti-cancerous properties targeting apoptosis-inducing proteins.Lett. Drug Des. Discov.20232010.2174/1570180820666230224143303
     [Google Scholar]
  56. RaniA.C. SujithaS. KalaimathiK. VijayakumarS. VaratharajuG. KarthikeyanK. ThiyagarajanG. SanjeeviS.B. PrabhuS. Uncovering of anti-dengue molecules from plants prescribed for dengue: A computational investigation.Chemistry Africa2022551321133610.1007/s42250‑022‑00421‑5
     [Google Scholar]
  57. BelghitiM. BenhibaF. BenzbiriaN. LaiC-H. EchihiS. SalahM. ZeroualA. KarzaziY. TounsiA. AbbicheK. BelaaouadS. Elalaoui-ElabdallaouiH. NaimiY. Performance of triazole derivatives as potential corrosion in-hibitors for mild steel in a strong phosphoric acid medium: Combining experimental and computational (DFT, MDs & QSAR) approaches.J. Mol. Struct.2022125613251510.1016/j.molstruc.2022.132515
     [Google Scholar]
  58. AmengorC.D.K. OrmanE. DanquahC.A. BenI.O. BiniyamP.D. HarleyB.K. Pyridine-N-oxide alkaloids from Allium stipitatum and their synthetic disulfide analogs as potential drug candidates against mycobacterium tuberculosis: A molecular docking, QSBAR, and ADMET prediction approach.BioMed Res. Int.2022202211410.1155/2022/6261528 36246961
     [Google Scholar]
  59. VakarelskaE. NedyalkovaM. VasighiM. SimeonovV. Persistent organic pollutants (POPs) QSPR classification models by means of Machine learning strategies.Chemosphere2022287Pt 213218910.1016/j.chemosphere.2021.132189 34826905
     [Google Scholar]
  60. MoumenI. AbouchabakaJ. RafaliaN. Enhancing urban mobility: Integration of IoT road traffic data and artificial intelligence in smart city environment.Indonesian Journal of Electrical Engineering and Computer Science202332298599310.11591/ijeecs.v32.i2.pp985‑993
     [Google Scholar]
  61. MohammedM.A.A. KhleelN.A.A. SzabóN.P. SzűcsP. Modeling of groundwater quality index by using artificial intelligence algorithms in northern khartoum state, sudan.Model. Earth Syst. Environ.2023922501251610.1007/s40808‑022‑01638‑6
     [Google Scholar]
  62. LiaoL. LiH. ShangW. MaL. An empirical study of the impact of hyperparameter tuning and model optimization on the performance properties of deep neural networks.ACM Trans. Softw. Eng. Methodol.202231314010.1145/3506695
     [Google Scholar]
  63. TohaA. PurwonoP. GataW. Model prediksi kualitas udara dengan support vector machines dengan optimasi hyperparameter gridsearch CV.Buletin Ilmiah Sarjana Teknik Elektro202241122110.12928/biste.v4i1.6079
     [Google Scholar]
  64. WuZ. ZhuM. KangY. LeungE.L.H. LeiT. ShenC. JiangD. WangZ. CaoD. HouT. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.Brief. Bioinform.2021224bbaa32110.1093/bib/bbaa321 33313673
     [Google Scholar]
  65. GhasemiF. MehridehnaviA. Pérez-GarridoA. Pérez-SánchezH. Neural network and deep-learning algorithms used in QSAR studies: Merits and drawbacks.Drug Discov. Today201823101784179010.1016/j.drudis.2018.06.016 29936244
     [Google Scholar]
  66. KeyvanpourM.R. ShirzadM.B. An analysis of QSAR research based on machine learning concepts.Curr. Drug Discov. Technol.2021181173010.2174/1570163817666200316104404 32178612
     [Google Scholar]
  67. LiZ. LongZ. LeiS. LiuX. YangL. ZhangW. ZhangT. Evaluating the corrosion resistance of marine steels under different exposure environments via machine learning.Phys. Scr.202398101540210.1088/1402‑4896/aca43a
     [Google Scholar]
  68. Bouziani IdrissiM. Theory and experimental investigations on the effect of the halogenated chain of new synthesis compounds based on benzimidazole derivatives on the inhibition corrosion of mild steel in acid media.International Journal of Corrosion and Scale Inhibition20231241535156310.17675/2305‑6894‑2023‑12‑4‑8
     [Google Scholar]
  69. CostaP. C. S. EvangelistaJ. S. LealI. MirandaP. C. M. L. Chemical graph theory for property modeling in QSAR and QSPR—charming QSAR & QSPRMathematics2020916010.3390/math9010060
     [Google Scholar]
  70. JiaoZ. YuanS. ZhangZ. WangQ. Machine learning prediction of hydrocarbon mixture lower flammability limits using quantitative structure-property relationship models.Process Saf. Prog.2020392e1210310.1002/prs.12103
     [Google Scholar]
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Harnessing Machine Learning for QSPR Modeling of Corrosion Inhibitors in HCl for Mild Steel Protection
Current Analytical Chemistry 21, 356 (2025); https://doi.org/10.2174/0115734110312696240822101941
/content/journals/cac/10.2174/0115734110312696240822101941
/content/journals/cac/10.2174/0115734110312696240822101941
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  • Article Type:     Research Article
Keyword(s): corrosion inhibitors; machine learning; mild steel; multilayer perceptron regression; QSPR; random forest regression; support vector regression; XGBoost

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