Integrating machine learning in catalyst design for sustainable hydrogen from plastic waste

Abstract

The escalating plastic waste crisis has heightened the need for sustainable, scalable valorization strategies. Catalytic conversion of plastic waste into hydrogen offers dual benefits: waste mitigation and clean fuel generation. However, the variability of plastic feedstock and the complexity of reaction conditions pose significant challenges for designing efficient catalysts. Recent advances in artificial intelligence (AI) and machine learning (ML) are increasingly being employed to optimize process conditions for hydrogen production via electrolysis and traditional thermochemical pathways. ML models, such as neural networks and ensemble methods, have demonstrated high accuracy in predicting hydrogen yields and optimizing parameters for the gasification and pyrolysis of plastic waste. ML is also opening new avenues for accelerating catalyst discovery by enabling rapid prediction of catalyst performance, reaction pathways, and surface interactions. Computational tools and data-driven descriptors are being used to interpret complex catalytic systems and guide the design of more effective catalysts. However, their application to plastic-derived intermediates remains limited. Despite progress, significant gaps persist in applying ML to the unique challenges of plastic waste conversion, including catalyst discovery and the handling of heterogeneous feedstocks. Key limitations include the need for larger, high-quality datasets, improved model interpretability and the integration of domain-specific knowledge with advanced simulation techniques. In this review we critically summarized the current landscape of AI-driven catalyst design focusing on hydrogen production from plastic waste. It identified methodological and practical limitations and proposed a roadmap for integrating AI, domain-specific data, and catalysis simulations to unlock new catalysts for sustainable hydrogen production.

Keywords

Waste valorization / plastic polymers / electrocatalysts / pyrolysis / photoreforming / machine learning / deep learning / hydrogen evolution reaction

References

[1] Nayanathara Thathsarani Pilapitiya PGC.The world of plastic waste: a review.Clean Mater2024;11:100220

[2] Global plastics outlook: economic drivers, environmental impacts and policy options. OECD Publishing; 2022.

[3] Real LEP.Plastics statistics: production, recycling, and market data. In: Recycled materials for construction applications. Cham: Springer International Publishing; 2022. pp. 103-13.

[4] Mohamadi M.Plastic types and applications. In: Hasanzadeh R, Mojaver P, editors. Plastic waste treatment and management. Cham: Springer Nature Switzerland; 2023. pp. 1-19.

[5] Björkner B,Pontén A.Plastic materials. In: Contact dermatitis, fifth edition; 2011, pp. 695-728.

[6] Manzoor J,Sofi IR.Plastic waste environmental and human health impacts. In: Handbook of research on environmental and human health impacts of plastic pollution. IGI Global Scientific Publishing; 2020, pp. 29-37.

[7] Samiul Islam FA.The impact of plastic waste on ecosystems and human health and strategies for managing it for a sustainable environment.Int J Latest Technol Eng Manag Appl Sci2025;14:706-23

[8] Vethaak AD.Plastic debris is a human health issue.Environ Sci Technol2016;50:6825-6

[9] Proshad R,Islam MS,Rahman MM.Toxic effects of plastic on human health and environment: a consequences of health risk assessment in Bangladesh.Int J Health2017;6:1-5

[10] Zhao X,Li K.Plastic waste upcycling toward a circular economy.Chem Eng J2022;428:131928

[11] Zhang F,Yappert RD.Polyethylene upcycling to long-chain alkylaromatics by tandem hydrogenolysis/aromatization.Science2020;370:437-41

[12] Chen Z,Chen X,Shen Y.Upcycling of plastic wastes for hydrogen production: advances and perspectives.Renew Sustain Energy Rev2024;195:114333

[13] Niu F,Chen D.State-of-the-art and perspectives of hydrogen generation from waste plastics.Chem Soc Rev2025;54:4948-72 PMCID:PMC11997959

[14] Yusaf T,Alrefae W.Hydrogen energy demand growth prediction and assessment (2021-2050) using a system thinking and system dynamics approach.Appl Sci2022;12:781

[15] Baykara SZ.Hydrogen: a brief overview on its sources, production and environmental impact.Int J Hydrogen Energy2018;43:10605-14

[16] Farias CBB,da Silva MF,Converti A.Use of hydrogen as fuel: a trend of the 21st century.Energies2022;15:311

[17] Ball M.The hydrogen economy opportunities and challenges. Cambridge University Press; 2009, pp. 277-308.

[18] Da Silva Sousa P,De França Serpa J.Trends and challenges in hydrogen production for a sustainable energy future.Biofuels Bioprod Bioref2024;18:2196-210

[19] Al-Qadri AA,Ahmad N,Zahid U.A review of hydrogen generation through gasification and pyrolysis of waste plastic and tires: opportunities and challenges.Int J Hydrogen Energy2024;77:1185-204

[20] Alshareef R,Williams PT.Hydrogen production by three-stage (i) pyrolysis, (ii) catalytic steam reforming, and (iii) water gas shift processing of waste plastic.Energy Fuels2023;37:3894-907 PMCID:PMC9986875

[21] Hu K,Wang Y,Wang S.Catalytic carbon and hydrogen cycles in plastics chemistry.Chem Catal2022;2:724-61

[22] Mustapha SI,Akpasi SO.Biomass conversion for sustainable hydrogen generation: a comprehensive review.Fuel Process Technol2025;272:108210

[23] Cui M,Shi X.Metal-organic framework-derived single-atom catalysts for electrocatalytic energy conversion applications.J Mater Chem A2024;12:18921-47

[24] Nguyen TT.Efficient photoreforming of plastic waste using a high-entropy oxide catalyst.J Catal2024;440:115808

[25] Paiu M,Favier L.Heterogeneous photocatalysis for advanced water treatment: materials, mechanisms, reactor configurations, and emerging applications.Appl Sci2025;15:5681

[26] Butler KT,Cartwright H,Walsh A.Machine learning for molecular and materials science.Nature2018;559:547-55

[27] Nyangiwe NN.Applications of density functional theory and machine learning in nanomaterials: a review.Next Mater2025;8:100683

[28] Wang K.Bayesian optimization for chemical products and functional materials.Curr Opin Chem Eng2022;36:100728

[29] Bin Abu Sofian ADA,Chew KW.Hydrogen production and pollution mitigation: Enhanced gasification of plastic waste and biomass with machine learning & storage for a sustainable future.Environ Pollut2024;342:123024

[30] Obaid A,Tabraiz S.Life cycle impact assessment of hydrogen production from plastic waste polymers.Clean Technol Environ Policy2025;27:6809-25

[31] Bishnu SK,Al-Mohannadi DM.Computational applications using data driven modeling in process systems: a review.Digit Chem Eng2023;8:100111

[32] Lai NS,Zhong X.Artificial intelligence (AI) workflow for catalyst design and optimization.Ind Eng Chem Res2023;62:17835-48

[33] Vipin KE. Data-driven catalyst design: a machine learning approach to predicting electrocatalytic performance in hydrogen evolution and oxygen evolution reactions. 2024. Available from: https://arxiv.org/pdf/2412.12846 [Last accessed on 13 May 2026]

[34] Available from: https://www.scopus.com/home.uri [Last accessed on 13 May 2026]

[35] Faizan M.Plastic waste to hydrogen fuel: cutting-edge catalytic technologies for sustainable energy transition.Int J Hydrogen Energy2025;127:678-701

[36] Quintanilla P,Duan L.Artificial intelligence and robotics in the hydrogen lifecycle: a systematic review.Int J Hydrogen Energy2025;113:801-17

[37] Jin Z,Li P.Artificial intelligence-driven catalyst design for electrocatalytic hydrogen production: paradigm innovation and challenges in material discovery.Sustain Chem Energy Mater2025;2:100010

[38] Azman DQ,Abdul Patah MF,Saw PA.Plastic waste management through liquefaction in hydrogen donating solvents: A review.J Environ Manag2024;359:120961

[39] Fang Y,Dai L,You S.Artificial intelligence in plastic recycling and conversion: a review.Resour Conserv Recycl2025;215:108090

[40] Ji Y,Zheng T.Plastic waste upcycling through electrocatalysis.Curr Opin Electrochem2025;52:101712

[41] Chen Y,Wei F,Li J.Single-use plastics: production, usage, disposal, and adverse impacts.Sci Total Environ2021;752:141772

[42] Desidery L.Polymers and plastics: types, properties, and manufacturing. plastic waste for sustainable asphalt roads. Elsevier; 2022. pp. 3-28.

[43] Muringayil Joseph T,Ahmadi Z.Polyethylene terephthalate (PET) recycling: a review.Case Stud Chem Environ Eng2024;9:100673

[44] Adeniran AA,Shakantu W.A review of the literature on the environmental and health impact of plastic waste pollutants in sub-Saharan Africa.Pollutants2022;2:531-45

[45] Geyer R.Production, use, and fate of synthetic polymers. In: Plastic Waste and Recycling. Elsevier; 2020. pp. 13-32.

[46] Stegmann P,Londo M,Junginger M.Plastic futures and their CO₂ emissions.Nature2022;612:272-6

[47] Ilyas M,Khan H,Khan K.Plastic waste as a significant threat to environment - a systematic literature review.Rev Environ Health2018;33:383-406

[48] Ragusa A,Santacroce C.Plasticenta: first evidence of microplastics in human placenta.Environ Int2021;146:106274

[49] Leslie HA,Brandsma SH,Garcia-Vallejo JJ.Discovery and quantification of plastic particle pollution in human blood.Environ Int2022;163:107199

[50] Vuppaladadiyam SSV,Sahoo A.Waste to energy: trending key challenges and current technologies in waste plastic management.Sci Total Environ2024;913:169436

[51] Fan YV,Tan RR.Forecasting plastic waste generation and interventions for environmental hazard mitigation.J Hazard Mater2022;424:127330

[52] Chawla S,A C,Keçili R.Environmental impacts of post-consumer plastic wastes: Treatment technologies towards eco-sustainability and circular economy.Chemosphere2022;308:135867

[53] Maitlo G,Maitlo HA.Plastic waste recycling, applications, and future prospects for a sustainable environment.Sustainability2022;14:11637

[54] Huang Z,Liu Z.Chemical recycling of polystyrene to valuable chemicals via selective acid-catalyzed aerobic oxidation under visible light.J Am Chem Soc2022;144:6532-42

[55] Babaremu K,Hughes E.Sustainable plastic waste management in a circular economy.Heliyon2022;8:e09984 PMCID:PMC9304725

[56] Huang J,Chan WP,Lisak G.Chemical recycling of plastic waste for sustainable material management: a prospective review on catalysts and processes.Renew Sustain Energy Rev2022;154:111866

[57] Biririza E.Prospects and challenges of sustainable energy future: policy and technology perspectives.Int J Econ Energy Environ2025;10:7-16

[58] Masson-Delmotte V,Pörtner HO. Global warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 2018. Available from: https://www.ipcc.ch/sr15/ [Last accessed on 19 May 2026]

[59] Mastellone ML.Technical description and performance evaluation of different packaging plastic waste management's systems in a circular economy perspective.Sci Total Environ2020;718:137233

[60] Arshad H,Hussain Z,Basrawi F.Microwave assisted pyrolysis of plastic waste for production of fuels: a review.MATEC Web Conf2017;131:02005

[61] Sajwan D,Sharma M.Upcycling of plastic waste using photo-, electro-, and photoelectrocatalytic approaches: a way toward circular economy.ACS Catal2024;14:4865-926

[62] Qin TH,Qu B.Pyrolysis-catalytic gasification of plastic waste for hydrogen-rich syngas production with hybrid-functional Ni-CaO Ca₂SiO₄ catalyst.Carbon Capture Sci Technol2025;14:100382

[63] Zhao Y,Chen G.Energy, efficiency, and environmental analysis of hydrogen generation via plasma co-gasification of biomass and plastics based on parameter simulation using Aspen plus.Energy Convers Manag2023;295:117623

[64] Mishra R,Gollakota AR.Unveiling the potential of pyrolysis-gasification for hydrogen-rich syngas production from biomass and plastic waste.Energy Convers Manag2024;321:118997

[65] Ganza PE.A novel method for industrial production of clean hydrogen (H₂) from mixed plastic waste.Int J Hydrogen Energy2023;48:15037-52

[66] Sabbir MHR,Zahan T.Review on plastic waste pyrolysis: a promising pathway of fuel generation and renewable energy. 2024.

[67] Medaiyese FJ,Khan K.Sustainable hydrogen production from plastic waste: optimizing pyrolysis for a circular economy.Hydrogen2025;6:15

[68] Dharmaraj S,Chew KW,Show PL.Novel strategy in biohydrogen energy production from COVID - 19 plastic waste: a critical review.Int J Hydrogen Energy2022;47:42051-74 PMCID:PMC8576595

[69] Bashir MA,Weidman J.Plastic waste gasification for low-carbon hydrogen production: a comprehensive review.Energy Adv2025;4:330-63

[70] Mitta H,Havaei M.Challenges and opportunities in catalytic hydrogenolysis of oxygenated plastics waste: polyesters, polycarbonates, and epoxy resins.Green Chem2025;27:10-40

[71] Seitz M.Catalytic depolymerization of polyolefinic plastic waste.Chem Ing Technol2022;94:720-6

[72] Defects in catalyst design boost plastic recycling efficiency.Nat India2024;d44151-024

[73] Venkataraghavan R,Khan TS,Devi RN.Exploring thermocatalytic pyrolysis to derive sustainable chemical intermediates from plastic waste; Role of temperature, catalyst, and reactor conditions.J Indian Inst Sci2024;104:383-94

[74] Martín AJ,Jaydev SD.Catalytic processing of plastic waste on the rise.Chem2021;7:1487-533

[75] Liu X,Nahil MA,Wu C.Development of Ni- and Fe- based catalysts with different metal particle sizes for the production of carbon nanotubes and hydrogen from thermo-chemical conversion of waste plastics.J Anal Appl Pyrolysis2017;125:32-9

[76] Nabgan W,Tuan Abdullah TA.Ni-Pt/Al nano-sized catalyst supported on TNPs for hydrogen and valuable fuel production from the steam reforming of plastic waste dissolved in phenol.Int J Hydrogen Energy2020;45:22817-32

[77] Zhang Z,Ge X.Mixed plastics wastes upcycling with high-stability single-atom Ru catalyst.J Am Chem Soc2023;145:22836-44

[78] Li R,Liang X.Polystyrene waste thermochemical hydrogenation to ethylbenzene by a N-bridged Co, Ni dual-atom catalyst.J Am Chem Soc2023;145:16218-27

[79] Huang K,Li C,Meng X.Ti₃C₂ MXene supporting platinum nanoparticles as rapid electrons transfer channel and active sites for boosted photocatalytic water splitting over g-C₃N₄.J Colloid Interface Sci2023;636:21-32

[80] Yao D,Mohan BC,Dai Y.Conversion of waste plastic packings to carbon nanomaterials: investigation into catalyst material, waste type, and product applications.ACS Sustain Chem Eng2022;10:1125-36

[81] Yao D,Yang H.Co-production of hydrogen and carbon nanotubes from catalytic pyrolysis of waste plastics on Ni-Fe bimetallic catalyst.Energy Convers Manag2017;148:692-700

[82] Docherty JH,Mcarthur G.Transition-metal-catalyzed C-H bond activation for the formation of C-C bonds in complex molecules.Chem Rev2023;123:7692-760 PMCID:PMC10311463

[83] Gandeepan P,Zell D,Warratz S.3d transition metals for C-H activation.Chem Rev2018;119:2192-452

[84] Lakiss L,Cruchade H.Zeolite catalysts for hydrogen harvesting from polyethylene: a sustainable approach to plastic waste upgrading.ChemCatChem2025;17:e202500592

[85] Munir D,Aslam R,Usman MR.Composite zeolite beta catalysts for catalytic hydrocracking of plastic waste to liquid fuels.Mater Renew Sustain Energy2020;9:9

[86] Kumar A,Sheet N,Singh SK.One-pot upcycling of waste plastics for selective hydrogen production at low-temperature.ChemCatChem2023;15:e202300574

[87] Zhou H,Li Z.Electrocatalytic upcycling of polyethylene terephthalate to commodity chemicals and H2 fuel.Nat Commun2021;12:4679 PMCID:PMC8371182

[88] Kang H,Yan X.Cu promoted the dynamic evolution of Ni-based catalysts for polyethylene terephthalate plastic upcycling.ACS Catal2024;14:5314-25

[89] Shi R,Liu F,Hou C.Electrocatalytic reforming of waste plastics into high value-added chemicals and hydrogen fuel.Chem Commun2021;57:12595-8

[90] Hai HTN,Nishibori M,Edalati K.Photoreforming of plastic waste into valuable products and hydrogen using a high-entropy oxynitride with distorted atomic-scale structure.Appl Catal B Environ Energy2025;365:124968

[91] Talebian-Kiakalaieh A,Guo M.Photocatalytic reforming raw plastic in seawater by atomically-engineered GeS/ZnIn₂S₄.Adv Energy Mater2025;15:2404963

[92] Yue S,Zhang T,Wang P.Photoreforming of plastic waste to sustainable fuels and chemicals: waste to energy.Environ Sci Technol2024;58:22865-79

[93] Yang RX,Chuang KH.Co-production of carbon nanotubes and hydrogen from waste plastic gasification in a two-stage fluidized catalytic bed.Renew Energy2020;159:10-22

[94] Wang J,Song J.A high-quality hydrogen production strategy from waste plastics through microwave-assisted reactions with heterogeneous bimetallic iron/nickel/cerium catalysts.J Anal Appl Pyrolysis2022;166:105612

[95] Li W,Yang Z,Tian W.Promotion effect of cobalt doping on microwave-initiated plastic deconstruction for hydrogen production over iron catalysts.Appl Catal B Environ2023;327:122451

[96] Seh ZW,Dickens CF,Nørskov JK.Combining theory and experiment in electrocatalysis: Insights into materials design.Science2017;355:eaad4998

[97] Wang N,Hu M.Ordered macroporous superstructure of bifunctional cobalt phosphide with heteroatomic modification for paired hydrogen production and polyethylene terephthalate plastic recycling.Appl Catal B Environ2022;316:121667

[98] Li Y,Wang X.Electrochemical upgrading of PET plastic wastes for hydrogen production using porous Fe-Ni₂P nanosheets.Int J Hydrogen Energy2024;96:794-802

[99] Jiao Y,Jaroniec M.Design of electrocatalysts for oxygen- and hydrogen-involving energy conversion reactions.Chem Soc Rev2015;44:2060-86

[100] Banoth P,Kollu P.Introduction to electrocatalysts. In: Noble metal-free electrocatalysts: new trends in electrocatalysts for energy applications. 2022, pp. 1-37.

[101] Voiry D,Li J.Enhanced catalytic activity in strained chemically exfoliated WS₂ nanosheets for hydrogen evolution.Nat Mater2013;12:850-5

[102] Hu D,Wang Y.Growth of two-dimensional edge-rich screwed WS₂ with high active site density for accelerated hydrogen evolution.Catalysts2025;15:496

[103] Yan Y,Xu S.Electrocatalytic upcycling of biomass and plastic wastes to biodegradable polymer monomers and hydrogen fuel at high current densities.J Am Chem Soc2023;145:6144-55

[104] Wang H,Qi X.Upcycling of monomers derived from waste polyester plastics via electrocatalysis.J Energy Chem2025;101:535-61

[105] Oku A,Yamada E.Alkali decomposition of poly(ethylene terephthalate) with sodium hydroxide in nonaqueous ethylene glycol: A study on recycling of terephthalic acid and ethylene glycol.J Appl Polym Sci1997;63:595-601

[106] Wang G,Wei W.Electrocatalysis-driven sustainable plastic waste upcycling.Electron2024;2:e34

[107] Zubair M,Klingenhof M.Vacancy promotion in layered double hydroxide electrocatalysts for improved oxygen evolution reaction performance.ACS Catal2023;13:4799-810

[108] Chen Z,Bao T.Dual-doped nickel sulfide for electro-upgrading polyethylene terephthalate into valuable chemicals and hydrogen fuel.Nano-Micro Lett2023;15:210 PMCID:PMC10495299

[109] Hauffe W.The electrooxidation of ethylene glycol at platinum in potassium hydroxide.Electrochim Acta1978;23:299-304

[110] Wang B,Yu Z.In situ structural evolution of the multi-site alloy electrocatalyst to manipulate the intermediate for enhanced water oxidation reaction.Energy Environ Sci2020;13:2200-8

[111] Fernandez E,Santamaria L.Tuning pyrolysis temperature to improve the in-line steam reforming catalyst activity and stability.Process Saf Environ Prot2022;166:440-50

[112] Ochoa A,Amutio M.Coking and sintering progress of a Ni supported catalyst in the steam reforming of biomass pyrolysis volatiles.Appl Catal B Environ2018;233:289-300

[113] Argyle M.Heterogeneous catalyst deactivation and regeneration: a review.Catalysts2015;5:145-269

[114] Ren J,Yang F,Tang W.Understandings of catalyst deactivation and regeneration during biomass tar reforming: a crucial review.ACS Sustain Chem Eng2021;9:17186-206

[115] Ochoa A,Gayubo AG.Coke formation and deactivation during catalytic reforming of biomass and waste pyrolysis products: a review.Renew Sustain Energy Rev2020;119:109600

[116] Yin G,Chen S.Machine learning-assisted high-throughput screening for electrocatalytic hydrogen evolution reaction.Molecules2025;30:759 PMCID:PMC11857985

[117] Xu Y,Zhang W.AI-empowered catalyst discovery: a survey from classical machine learning approaches to large language models.arXiv2025;

[118] Aramouni NAK,Tarboush BA,Ahmad MN.Catalyst design for dry reforming of methane: analysis review.Renew Sustain Energy Rev2018;82:2570-85

[119] Wang N,Zheng F.Machine-learning assisted screening proton conducting Co/Fe based oxide for the air electrode of protonic solid oxide cell.Adv Funct Mater2024;34:2309855

[120] Gu Y,Xu Z.Design and application of electrocatalyst based on machine learning.Interdiscip Mater2025;4:456-79

[121] Ding R,Chen Y,Bando Y.Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation.Chem Soc Rev2024;53:11390-461

[122] Xu S,Qin M.Developing new electrocatalysts for oxygen evolution reaction via high throughput experiments and artificial intelligence.NPJ Comput Mater2024;10:194

[123] Yang TT.Reconciling the volcano trend with the Butler-Volmer model for the hydrogen evolution reaction.J Phys Chem Lett2022;13:5310-5

[124] Steinmann SN, .Seh ZW. How machine learning can accelerate electrocatalysis discovery and optimization.Mater Horiz2023;10:393-406

[125] Smith A,Dumesic JA,Zavala VM.A machine learning framework for the analysis and prediction of catalytic activity from experimental data.Appl Catal B2020;263:118257

[126] Terry J,Jiateng Sun J.Analysis of extended X-ray absorption fine structure (EXAFS) data using artificial intelligence techniques.Appl Surf Sci2021;547:149059

[127] Kim J,Kim S.Catalyze materials science with machine learning.ACS Mater Lett2021;3:1151-71

[128] Zhang H,Dong F.Dynamic transformation of active sites in energy and environmental catalysis.Energy Environ Sci2024;17:6435-81

[129] Taylor HS.A theory of the catalytic surface. In: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences; 1925, pp. 105-11.

[130] Vojvodic A.New design paradigm for heterogeneous catalysts.Natl Sci Rev2015;2:140-3

[131] Varela AS,Strasser P.Molecular nitrogen-carbon catalysts, solid metal organic framework catalysts, and solid metal/nitrogen-doped carbon (MNC) catalysts for the electrochemical CO₂ reduction.Adv. Energy Mater2018;8:1703614

[132] Zhang L,Qin H,Li H.Artificial intelligence for catalyst design and synthesis.Matter2025;8:102138

[133] Martini A,Timoshenko J.Tracking the evolution of single-atom catalysts for the CO₂ electrocatalytic reduction using operando X-ray absorption spectroscopy and machine learning.J.Am Chem Soc2023;145:17351-66 PMCID:PMC10416299

[134] Fan X,Huang D.From single metals to high-entropy alloys: how machine learning accelerates the development of metal electrocatalysts.Adv Funct Mater2024;34:2401887

[135] Cheng D,Zhang G.The nature of active sites for carbon dioxide electroreduction over oxide-derived copper catalysts.Nat Commun2021;12:395 PMCID:PMC7810728

[136] Li J,Palansooriya KN.Zeolite-catalytic pyrolysis of waste plastics: machine learning prediction, interpretation, and optimization.Appl Energy2025;382:125258

[137] Xu D,He Z.Machine learning-driven prediction and optimization of monoaromatic oil production from catalytic co-pyrolysis of biomass and plastic wastes.Fuel2023;350:128819

[138] Taşar Ş.Estimation of pyrolysis liquid product yield and its hydrogen content for biomass resources by combined evaluation of pyrolysis conditions with proximate-ultimate analysis data: A machine learning application.J Anal Appl Pyrolysis2022;165:105546

[139] Tang Q,Yang H.Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: Effects of pyrolysis conditions and biomass characteristics.Bioresour Technol2021;339:125581

[140] Potnuri R,Surya DV,Basak T.Utilizing support vector regression modeling to predict pyro product yields from microwave-assisted catalytic co-pyrolysis of biomass and waste plastics.Energy Convers Manag2023;292:117387

[141] Riaz S,Farooq W,Sajid M.Catalytic pyrolysis of HDPE for enhanced hydrocarbon yield: a boosted regression tree assisted kinetics study for effective recycling of waste plastic.Dig Chem Eng2025;14:100213

[142] Cheng Y,Yildiz G,Coward B.Applied machine learning for prediction of waste plastic pyrolysis towards valuable fuel and chemicals production.J Anal Appl Pyrolysis2023;169:105857

[143] Bu Q,Wang B,Long H.Machine learning-assisted prediction of gas production during co-pyrolysis of biomass and waste plastics.Waste Manag2025;200:114748

[144] Devasahayam S.Predicting hydrogen production from co-gasification of biomass and plastics using tree based machine learning algorithms.Renew Energy2024;222:119883

[145] Bakır R,Yüksel A.Optimizing hydrogen evolution prediction: A unified approach using random forests, lightGBM, and Bagging Regressor ensemble model.Int J Hydrogen Energy2024;67:101-10

[146] Yi CQ,Kamis SKBH,Karri RR.Production of hydrogen using plastic waste via Aspen Hysys simulation.Sci Rep2024;14:4934 PMCID:PMC10901799

[147] Lahafdoozian M,Zein SH.Hydrogen production from plastic waste: a comprehensive simulation and machine learning study.Int J Hydrogen Energy2024;59:465-79

[148] Aminu I,Williams PT.Pyrolysis-plasma/catalytic reforming of post-consumer waste plastics for hydrogen production.Catal Today2023;420:114084

[149] Cai N,Xia S.Pyrolysis-catalysis of different waste plastics over Fe/Al₂O₃ catalyst: high-value hydrogen, liquid fuels, carbon nanotubes and possible reaction mechanisms.Energy Convers Manag2021;229:113794

[150] Zhou X,Zhang Z.Boosting the optimization strategy for the waste plastics pyrolysis engineering application: a machine learning multi-dimensional evaluation framework.Chen J Clean Prod2025;492:144891

[151] Peng Y,Ke L.et al. A review on catalytic pyrolysis of plastic wastes to high-value products.Energy Convers Manag2022;254:115243

[152] Mou LH,Smith PES,Jiang J.Machine learning descriptors for data-driven catalysis study.Adv Sci2023;10:2301020 PMCID:PMC10401178

[153] Zhao Y,Chi C,Tang S.Design and screening of a NORR electrocatalyst with co-coordinating active centers of the support and coordination atoms: a machine learning descriptor for quantifying eigen properties.J Mater Chem A2024;12:8226-35

[154] Srour H,Mekki-Berrada A.Regeneration of an aged hydrodesulfurization catalyst: conventional thermal vs non-thermal plasma technology.Fuel2021;306:121674

[155] Wang B.Main descriptors to correlate structures with the performances of electrocatalysts.Angew Chem Int Ed2021;61:e202111026

[156] González-Poggini S.Hydrogen evolution descriptors: a review for electrocatalyst development and optimization.Int J Hydrogen Energy2024;59:30-42

[157] Zhang J,Zhou D.Adsorption energy in oxygen electrocatalysis.Chem Rev2022;122:17028-72

[158] Jiao S,Huang H.Descriptors for the evaluation of electrocatalytic reactions: d-band theory and beyond.Adv Funct Mater2021;32:2107651

[159] Zhang Y,Guo Z.Establishing theoretical landscapes for identifying basal plane active sites in MBene toward multifunctional HER, OER, and ORR catalysts.J. Colloid Interface Sci2023;652:1954-64

[160] Liao X,Xia L.Density functional theory for electrocatalysis.Energy Environ Mater2021;5:157-85

[161] Niederer KA,Kozlowski MC.Oxidative photocatalytic homo- and cross-coupling of phenols: nonenzymatic, catalytic method for coupling tyrosine.ACS Catal2020;10:14615-23 PMCID:PMC8078885

[162] Hu L,Wu S.Biocompatible and biodegradable super-toughness regenerated cellulose via water molecule-assisted molding.Chem Eng J2021;417:129229

[163] Singla RK.Corrigendum to “Application of decomposition method and inverse prediction of parameters in a moving fin” [Energy Convers. Manage. 84 (2014) 268-281].Energy Convers Manag2015;93:458-9

[164] Bagheri S.Nano-diamond based photocatalysis for solar hydrogen production.Int J Hydrogen Energy2020;45:31538-54

[165] Pandelidis D.Numerical study and performance evaluation of the Maisotsenko cycle cooling tower.Energy Convers Manag2020;210:112735

[166] Wu H,Ji G.Renewable production of nitrogen-containing compounds and hydrocarbons from catalytic microwave-assisted pyrolysis of chlorella over metal-doped HZSM-5 catalysts.J Anal Appl Pyrolysis2020;151:104902

[167] Fang A,Mei X.The simultaneous recruitment of anammox granules and biofilm by a sequential immobilization and granulation approach.Chem Eng J2021;417:128041

[168] Schwaller P,Gaudin T.Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction.ACS Cent Sci2019;5:1572-83 PMCID:PMC6764164

[169] Mortazavi B.Recent advances in machine learning-assisted multiscale design of energy materials.Adv Energy Mater2025;15:2403876

[170] Deshmukh MA,Zbořil R.Bimetallic single-atom catalysts for water splitting.Nanomicro Lett2025;17:1 PMCID:PMC11422407

[171] Xia C,He C,Guo W.Structural reconstruction of electrocatalysts.Fundam Res2025;5:2537-52 PMCID:PMC12744674

[172] Belkhode PN,Prakash C.An integrated AI-driven framework for maximizing the efficiency of heterostructured nanomaterials in photocatalytic hydrogen production.Sci Rep2025;15:24936 PMCID:PMC12246241

[173] Wang Z,Wang S.The future of catalysis: applying graph neural networks for intelligent catalyst design.WIREs Comput Mol Sci2025;15:e70010

[174] Tamtaji M,Abdi J.DFT and machine learning studies on a multi-functional single-atom catalyst for enhanced oxygen and hydrogen evolution as well as CO₂ reduction reactions.Int J Hydrogen Energy2024;80:1075-83

[175] Wang C,Wang C,Yang M.Efficient machine learning model focusing on active sites for the discovery of bifunctional oxygen electrocatalysts in binary alloys.ACS Appl Mater Interfaces2024;16:16050-61

[176] Lee J,Shin S,Han Y.Machine learning for the expedited screening of hydrogen evolution catalysts for transition metal-doped transition metal dichalcogenides.Int J Energy Res2023;2023:1-11

[177] Ghoroghi A,Petri I.Advances in application of machine learning to life cycle assessment: a literature review.Int J Life Cycle Assess2022;27:433-56

[178] Long F.An integration of machine learning models and life cycle assessment for lignocellulosic bioethanol platforms.Energy Convers Manag2023;292:117379

[179] Romeiko XX,Pang Y.A review of machine learning applications in life cycle assessment studies.Sci Total Environ2024;912:168969 PMCID:PMC12191033

[180] Karka P,Kokossis A.Digitizing sustainable process development: from ex-post to ex-ante LCA using machine-learning to evaluate bio-based process technologies ahead of detailed design.Chem Eng Sci2022;250:117339

[181] Xin H,Pillai HS,Huang Y.Interpretable machine learning for catalytic materials design toward sustainability.Acc Mater Res2023;5:22-34

[182] Abraham BM,Sinha P,Singh JK.Catalysis in the digital age: unlocking the power of data with machine learning.WIREs Comput Mol Sci2024;14:e1730

[183] Srivastava S,Malarvizhi A,Balakarthikeyan M.A research based on eco-friendly catalysis: environmental sustainability using AI.Int J Environ Sci2025;11:5244-51

[184] Guo K,Chen L,Zhou Z.Machine learning-based design of electrocatalysts and catalytic mechanism research.Sci Sin Chim2025;55:1660-73

[185] Nguyen TT,Sauvage X,Guo Q.Phase and sulfur vacancy engineering in cadmium sulfide for boosting hydrogen production from catalytic plastic waste photoconversion.Chem Eng J2025;504:158730

[186] Li J,Ma X.Selective ethylene glycol oxidation to formate on nickel selenide with simultaneous evolution of hydrogen.Adv Sci2023;10:2300841 PMCID:PMC10214232

[187] Tabu B,Yu P.Nonthermal atmospheric plasma reactors for hydrogen production from low-density polyethylene.Int J Hydrogen Energy2022;47:39743-57

[188] Shoukat B,Naz MY.Microwave-assisted catalytic deconstruction of plastics waste into nanostructured carbon and hydrogen fuel using composite magnetic ferrite catalysts.Scientifica2024;2024:3318047 PMCID:PMC11161267

[189] Dmitrieva AP,Tracey CT.AI and ML for selecting viable electrocatalysts: progress and perspectives.J Mater Chem A2024;12:31074-102

[190] Kronberg R,Laasonen K.Hydrogen adsorption on defective nitrogen-doped carbon nanotubes explained via machine learning augmented DFT calculations and game-theoretic feature attributions.J Phys Chem C2021;125:15918-33

[191] Hu Y,Wei Z,Zhao Y.Recent advances and applications of machine learning in electrocatalysis.J Mater Inf2023;3:18

[192] Kek HY,Tan H.Plastic-to-hydrogen through pyrolysis and gasification: Life-cycle implications, techno-economic, and digital optimisation.J Anal Appl Pyrolysis2026;193:107440

[193] Sun Y,Liu Q.Modulating electronic structure of metal-organic frameworks by introducing atomically dispersed Ru for efficient hydrogen evolution.Nat Commun2021;12:1369 PMCID:PMC7921655

[194] Jäger MOJ,Canova FF,Foster AS.Efficient machine-learning-aided screening of hydrogen adsorption on bimetallic nanoclusters.ACS Comb Sci2020;22:768-81 PMCID:PMC7739401

[195] Korovin AN,Samtsevich AI.Boosting heterogeneous catalyst discovery by structurally constrained deep learning models.Mater Today Chem2023;30:101541

[196] Li J,Zhao G,Sun W.Plastic waste conversion by leveraging renewable photo/electro-catalytic technologies.ChemSusChem2024;17:e202301352

[197] Li Y,Lee LQ.Electroreforming of plastic wastes for value-added products.Chem Commun2025;61:33-45