Machine learning-assisted advances and perspectives for electrolytes of protonic solid oxide fuel cells

Abstract

Protonic solid oxide fuel cells (P-SOFCs), as a promising power generation technology, have garnered increasing attention due to their advantages of cleanliness, high efficiency, and high reliability. As a critical component of P-SOFCs, proton-conducting electrolytes exhibit high ionic conductivity, enabling high chemical-to-electrical energy conversion efficiency at intermediate temperatures. However, there are still many challenges in further enhancing the proton conductivity and stability of the currently widely used Ba(Zr, Ce)O₃ electrolytes through traditional experimental methods. Herein, this review firstly summarized the current research status of proton-conducting oxides, including ABO₃ perovskite-type oxides and other structural oxides, and highlighted the challenges faced by electrolyte development in terms of proton conductivity, compatibility with other components, and long-term durability. Then, the relevant progress of machine learning (ML) in the research of P-SOFC electrolytes was meticulously discussed and the promising applications of ML in proton-conducting electrolyte performance screening, stability prediction, and morphology analysis were pointed out. More importantly, the challenges and solutions of proton-conducting electrolytes designed by ML were uncovered by considering the reliable database, feature engineering, accurate model, and experimental validation. Overall, this review concluded the advances of ML-assisted P-SOFC electrolytes and addressed the future research directions in the synergy of ML and electrolytes.

Keywords

Proton-conducting electrolytes / machine learning / conductivity / chemical stability / fuel cells

References

[1] Chen P,Li S.The promise and challenges of inverted perovskite solar cells.Chem Rev2024;124:10623-700

[2] Pryor SC,Bukovsky MS,Sakaguchi K.Climate change impacts on wind power generation.Nat Rev Earth Environ2020;1:627-43

[3] Wang N,Du L.Advanced cathode materials for protonic ceramic fuel cells: recent progress and future perspectives.Adv Energy Mater2022;12:2201882

[4] Hu A,Li Y.High-entropy driven self-assembled dual-phase composite air electrodes with enhanced performance and stability for reversible protonic ceramic cells.Adv Energy Mater2025;2405466

[5] Oni AM,Rahman MM.A comprehensive evaluation of solar cell technologies, associated loss mechanisms, and efficiency enhancement strategies for photovoltaic cells.Energy Rep2024;11:3345-66

[6] Dai K,Liang C,Huang Z.Environmental issues associated with wind energy - a review.Renew Energy2015;75:911-21

[7] Sayed ET,Elsaid K.A critical review on environmental impacts of renewable energy systems and mitigation strategies: wind, hydro, biomass and geothermal.Sci Total Environ2021;766:144505

[8] Kuriqi A,Sordo-ward A.Water-energy-ecosystem nexus: Balancing competing interests at a run-of-river hydropower plant coupling a hydrologic–ecohydraulic approach.Energy Convers Manag2020;223:113267

[9] Soltani M,Souri M.Environmental, economic, and social impacts of geothermal energy systems.Renew Sustain Energy Rev2021;140:110750

[10] Mekhilef S,Safari A.Comparative study of different fuel cell technologies.Renew Sustain Energy Rev2012;16:981-9

[11] Tellez-Cruz MM,Solorza-Feria O.Proton exchange membrane fuel cells (PEMFCs): advances and challenges.Polymers2021;13:3064 PMCID:PMC8468942

[12] Das G,Nguyen PKT,Yoon YS.Anion exchange membranes for fuel cell application: a review.Polymers2022;14:1197 PMCID:PMC8955432

[13] Petrovic S.Development of a novel technological readiness assessment tool for fuel cell technology.IEEE Access2020;8:132237-52

[14] Yu G,Liu N,Wang N.Hydrocarbon extraction with ionic liquids.Chem Rev2024;124:3331-91

[15] Alaswad A,Sodre JR.Technical and commercial challenges of proton-exchange membrane (PEM) fuel cells.Energies2021;14:144

[16] Xiao F,Wu ZP.Recent advances in electrocatalysts for proton exchange membrane fuel cells and alkaline membrane fuel cells.Adv Mater2021;33:2006292

[17] Mclean G.An assessment of alkaline fuel cell technology.Int J Hydrogen Energy2002;27:507-26

[18] Kirubakaran A,Nema R.A review on fuel cell technologies and power electronic interface.Renew Sustain Energy Rev2009;13:2430-40

[19] Tang C,Wang N.Green hydrogen production by intermediate-temperature protonic solid oxide electrolysis cells: advances, challenges, and perspectives.InfoMat2024;6:e12515

[20] Weng B,Zhu R.Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts.Nat Commun2020;11:3513 PMCID:PMC7360597

[21] Li Z,Feng D.Prediction of perovskite oxygen vacancies for oxygen electrocatalysis at different temperatures.Nat Commun2024;15:9318 PMCID:PMC11522418

[22] Divilov S,Hicks D.Disordered enthalpy-entropy descriptor for high-entropy ceramics discovery.Nature2024;625:66-73

[23] Hyodo J,Shiga M,Yamazaki Y.Accelerated discovery of proton-conducting perovskite oxide by capturing physicochemical fundamentals of hydration.ACS Energy Lett2021;6:2985-92

[24] Priya P.Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning.npj Comput Mater2021;7:90

[25] Cao J,Shao Z.Perovskites for protonic ceramic fuel cells: a review.Energy Environ Sci2022;15:2200-32

[26] Duan C,Sullivan N.Proton-conducting oxides for energy conversion and storage.Appl Phys Rev2020;7:011314

[27] Liu F,Diercks D.Lowering the operating temperature of protonic ceramic electrochemical cells to < 450  °C.Nat Energy2023;8:1145-57

[28] Bello IT,Zhao S,Yu N.Scientometric review of proton-conducting solid oxide fuel cells.Int J Hydrogen Energy2021;46:37406-28

[29] Sajid M,Rahman A.An overview of cooling of thermoelectric devices.Renew Sustain Energy Rev2017;78:15-22

[30] Kreuer K,Weppner W.Vehicle Mechanism, A new model for the interpretation of the conductivity of fast proton conductors.Angew Chem Int Ed1982;21:208-9

[31] Su H.Degradation issues and stabilization strategies of protonic ceramic electrolysis cells for steam electrolysis.Energy Sci Eng2022;10:1706-25

[32] Kim J,Kim S,Bu Y.Proton conducting oxides: a review of materials and applications for renewable energy conversion and storage.Renew Sustain Energy Rev2019;109:606-18

[33] Jing J,Chen L,Lei Z.Structure, synthesis, properties and solid oxide electrolysis cells application of Ba(Ce, Zr)O₃ based proton conducting materials.Chem Eng J2022;429:132314

[34] Geneste G.Proton transfer in barium zirconate: lattice reorganization, landau-zener curve-crossing approach.Solid State Ion2018;323:172-202

[35] Seong A,Jeong D.Electrokinetic proton transport in triple (H⁺/O^2-/e^-) conducting oxides as a key descriptor for highly efficient protonic ceramic fuel cells.Adv Sci2021;8:2004099

[36] Iwahara H,Uchida H.Proton conduction in sintered oxides and its application to steam electrolysis for hydrogen production.Solid State Ion1981;3-4:359-63

[37] Iwahara H,Maeda N.High temperature fuel and steam electrolysis cells using proton conductive solid electrolytes.J Power Sources1982;7:293-301

[38] Iwahara H,Tanaka S.High temperature type proton conductor based on SrCeO₃ and its application to solid electrolyte fuel cells.Solid State Ion1983;9-10:1021-5

[39] Iwahara H,Ushida H.Effect of ionic radii of dopants on mixed ionic conduction (H⁺+O^2-) in BaCeO₃-based electrolytes.Solid State Ion1994;70-71:267-71

[40] Iwahara H,Hibino T,Suzuki H.Protonic conduction in calcium, strontium and barium zirconates.Solid State Ion1993;61:65-9

[41] Iwahara H.Proton conducting ceramics and their applications.Solid State Ion1996;86-88:9-15

[42] Tarasova N,Janjua NK,Motola M.Fluorine-insertion in solid oxide materials for improving their ionic transport and stability. A brief review.Int J Hydrogen Energy2024;50:104-23

[43] Matkin DE,Hanif MB.Revisiting the ionic conductivity of solid oxide electrolytes: a technical review.J Mater Chem A2024;12:25696-714

[44] Tian Y,Yang C.Progress and potential for symmetrical solid oxide electrolysis cells.Matter2022;5:482-514

[45] Yang L,Blinn K.Enhanced sulfur and coking tolerance of a mixed ion conductor for SOFCs: BaZr_0.1Ce_0.7Y_0.2-xYb_xO_3-δ.Science2009;326:126-9

[46] Duan C,Shang M.Readily processed protonic ceramic fuel cells with high performance at low temperatures.Science2015;349:1321-6

[47] Choi S,Liang Y.Exceptional power density and stability at intermediate temperatures in protonic ceramic fuel cells.Nat Energy2018;3:202-10

[48] Saito K.High proton conductivity within the ‘Norby gap’ by stabilizing a perovskite with disordered intrinsic oxygen vacancies.Nat Commun2023;14:7466 PMCID:PMC10656576

[49] Fop S.Solid oxide proton conductors beyond perovskites.J Mater Chem A2021;9:18836-56.

[50] Tarasova N,Galisheva A.Incorporation and conduction of protons in Ca, Sr, Ba-doped BaLaInO₄ with ruddlesden-popper structure.Materials2019;12:1668 PMCID:PMC6566999

[51] Murakami T,Yashima M.High proton conductivity in Ba₅Er₂Al₂ZrO₁₃, a hexagonal perovskite-related oxide with intrinsically oxygen-deficient layers.J Am Chem Soc2020;142:11653-7

[52] Haugsrud R.High-temperature proton conductivity in acceptor-substituted rare-earth ortho-tantalates, LnTaO₄.J Am Ceram Soc2007;90:1116-21

[53] Kim D,Kim KJ.High-performance protonic ceramic electrochemical cells.ACS Energy Lett2022;7:2393-400

[54] Fop S,Wildman EJ.High oxide ion and proton conductivity in a disordered hexagonal perovskite.Nat Mater2020;19:752-7

[55] Ling CD,Kharton VV,Macquart RB.Structures, phase transitions, hydration, and ionic conductivity of Ba₄Ta₂O₉.Chem Mater2010;22:532-40

[56] Haugsrud R.Proton conduction in rare-earth ortho-niobates and ortho-tantalates.Nature Mater2006;5:193-6

[57] Wang J,Zhang Z,Li Z.Protonic conduction in Ca2+-doped La2M2O7 (M=Ce, Zr) with its application to ammonia synthesis electrochemically.Mater Res Bull2005;40:1294-302

[58] 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

[59] Nomura K,Yamaguchi Y.Machine learning based prediction of space group for Ba(Ce_0.8-xZr_x)Y_0.2O₃ perovskite-type protonic conductors.Ceram Int2023;49:5058-65

[60] Bello IT,Yu N.Revolutionizing material design for protonic ceramic fuel cells: bridging the limitations of conventional experimental screening and machine learning methods.Chem Eng J2023;477:147098

[61] Zhai S,Cui P.A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells.Nat Energy2022;7:866-75

[62] Luo Z,Zhou Y.Harnessing high-throughput computational methods to accelerate the discovery of optimal proton conductors for high-performance and durable protonic ceramic electrochemical cells.Adv Mater2024;36:2311159

[63] Fujii S,Hyodo J,Yamazaki Y.Discovery of unconventional proton-conducting inorganic solids via defect-chemistry-trained, interpretable machine learning.Adv Energy Mater2023;13:2301892

[64] Islam MS,Hall AT.First-principles computational design and discovery of solid-oxide proton conductors.Chem Mater2022;34:5938-48

[65] Szaro NA,Chen F.First principles material screening and trend discovery for the development of perovskite electrolytes for proton-conducting solid oxide fuel cells.J Power Sources2024;603:234411

[66] Freysoldt C,Hickel T.First-principles calculations for point defects in solids.Rev Mod Phys2014;86:253-305

[67] Peng J,Wang X,Wang J.Metal-organic frameworks: advances in first-principles computational studies on catalysis, adsorption, and energy storage.Mater Today Commun2024;40:109780

[68] Tawfik SA.Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors.J Cheminform2022;14:78 PMCID:PMC9644534

[69] Nazemzadeh N,Nielsen RF,Andersson MP.Integration of first-principle models and machine learning in a modeling framework: an application to flocculation.Chem Eng Sci2021;245:116864

[70] Bello IT,Esan OC.AI-enabled materials discovery for advanced ceramic electrochemical cells.Energy AI2024;15:100317

[71] Li Z,Chen X.A deep-learning-boosted surrogate model of a metal foam based protonic ceramic electrolysis cell stack for uncertainty quantification.Energy Convers Manag2024;318:118886

[72] Liu X,Wu J.Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times.J Energy Chem2023;78:582-8

[73] Chowdhury A,Yener B.Image driven machine learning methods for microstructure recognition.Comput Mater Sci2016;123:176-87

[74] Ge M,Zhao Z.Deep learning analysis on microscopic imaging in materials science.Mater Today Nano2020;11:100087

[75] Lee B,Lee JW.Statistical characterization of the morphologies of nanoparticles through machine learning based electron microscopy image analysis.ACS Nano2020;14:17125-33

[76] Nandy A,Kulik HJ.Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery.Curr Opin Chem Eng2022;36:100778

[77] Jablonka KM,Moosavi SM.Big-data science in porous materials: materials genomics and machine learning.Chem Rev2020;120:8066-129 PMCID:PMC7453404

[78] Anand DV,Wee J,Sum TC.Topological feature engineering for machine learning based halide perovskite materials design.npj Comput Mater2022;8:203

[79] Himanen L,Morooka EV.DScribe: library of descriptors for machine learning in materials science.Comput Phys Commun2020;247:106949

[80] Li Z,Xin H.Feature engineering of machine-learning chemisorption models for catalyst design.Catal Today2017;280:232-8

[81] Liu Y,Ju W.Materials discovery and design using machine learning.J Materiomics2017;3:159-77

[82] Xu P,Li M.Small data machine learning in materials science.npj Comput Mater2023;9:42