Advances in machine learning applications for thiolate-protected metal nanoclusters with potential metallic core exploration

Abstract

Thiolate-protected metal nanoclusters (TPMNCs) exhibit tunable physicochemical properties governed by quantum effects related to size, composition, assembly, and surface ligands. Atomically precise synthesis enables researchers to directly correlate nanostructure with material performance. However, slight variations in structure can lead to significant and nonlinear quantum effects, making macroscopic properties unpredictable. Therefore, nanolevel property tuning remained challenging before the advanced development of machine learning (ML). In contrast to traditional nanodesign methods, algorithm development based on data enables ML approaches to capture the nonlinear behaviors and electronic features of nanoclusters by embedding characteristics into a high-dimensional numerical space, thereby improving the predictability and generative capability for property prediction. TPMNCs are a representative system with atomic precision and distinct optical and catalytic properties. This review explores ML applications in nanocluster research, with a focus on TPMNCs, including synthesis, structure prediction, optical property analysis, and catalytic mechanism discovery. The rapid advancement of ML is propelling progress in this field and paving the way for future directions including active learning, model transferability, autonomous experimentation, and adaptive simulation.

Keywords

catalytic mechanisms / machine learning / optical properties / structural prediction / thiolate-protected metal nanoclusters

References

[1] Fan Y, Liu S, Yi Y, Rong H, Zhang J. Catalytic nanomaterials toward atomic levels for biomedical applications: from metal clusters to single-atom catalysts. ACS Nano. 2021; 15(2): 2005-2037. https://doi.org/10.1021/acsnano.0c06962

[2] Halperin WP. Quantum size effects in metal particles. Rev Mod Phys. 1986; 58(3): 533-606. https://doi.org/10.1103/revmodphys.58.533

[3] Ziefuss AR, Steenbock T, Benner D, et al. Photoluminescence of fully inorganic colloidal gold nanocluster and their manipulation using surface charge effects. Adv Mater. 2021; 33(31):2101549. https://doi.org/10.1002/adma.202101549

[4] Maity S, Bain D, Patra A. An overview on the current understanding of the photophysical properties of metal nanoclusters and their potential applications. Nanoscale. 2019; 11(47): 22685-22723. https://doi.org/10.1039/c9nr07963g

[5] Xu H, Cheng D, Gao Y, Zeng XC. Assessment of catalytic activities of gold nanoclusters with simple structure descriptors. ACS Catal. 2018; 8(10): 9702-9710. https://doi.org/10.1021/acscatal.8b02423

[6] Wang Y, Su Y-Q, Hensen EJM, Vlachos DG. Insights into supported subnanometer catalysts exposed to CO via machine-learning-enabled multiscale modeling. Chem Mater. 2022; 34(4): 1611-1619. https://doi.org/10.1021/acs.chemmater.1c03616

[7] Muetterties EL. Molecular metal clusters: cluster chemistry may provide valuable insights to chemisorption and catalysis on surfaces. Science. 1977; 196(4292): 839-848. https://doi.org/10.1126/science.196.4292.839

[8] Gates BC. Supported metal clusters: synthesis, structure, and catalysis. Chem Rev. 1995; 95(3): 511-522. https://doi.org/10.1021/cr00035a003

[9] Mo F, Zhou Q, Xue W, et al. The optimized catalytic performance of single-atom catalysts by incorporating atomic clusters or nanoparticles: in-depth understanding on their synergisms. Adv Energy Mater. 2023; 13(38):2301711. https://doi.org/10.1002/aenm.202301711

[10] Rong H, Ji S, Zhang J, Wang D, Li Y. Synthetic strategies of supported atomic clusters for heterogeneous catalysis. Nat Commun. 2020; 11(1):5884. https://doi.org/10.1038/s41467-020-19571-6

[11] Chen Y, Zhao J, Zhao X, et al. Stabilizing supported atom-precise low-nuclearity platinum cluster catalysts by nanoscale confinement. Nat Chem Eng. 2025; 2(1): 38-49. https://doi.org/10.1038/s44286-024-00162-x

[12] Aikens CM. Electronic and geometric structure, optical properties, and excited state behavior in atomically precise thiolate-stabilized noble metal nanoclusters. Acc Chem Res. 2018; 51(12): 3065-3073. https://doi.org/10.1021/acs.accounts.8b00364

[13] Jin R. Atomically precise metal nanoclusters: stable sizes and optical properties. Nanoscale. 2015; 7(5): 1549-1565. https://doi.org/10.1039/c4nr05794e

[14] Han Z, Duan C, Dong XY, et al. Tightly bonded excitons in chiral metal clusters for luminescent brilliance. Nat Commun. 2025; 16(1):1867. https://doi.org/10.1038/s41467-025-57209-7

[15] Kirkpatrick J, McMorrow B, Turban DHP, et al. Pushing the frontiers of density functionals by solving the fractional electron problem. Science. 2021; 374(6573): 1385-1389. https://doi.org/10.1126/science.abj6511

[16] Goerigk L, Hansen A, Bauer C, Ehrlich S, Najibi A, Grimme S. A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions. Phys Chem Chem Phys. 2017; 19(48): 32184-32215. https://doi.org/10.1039/c7cp04913g

[17] Raju RK, Sivakumar S, Wang X, Ulissi ZW. Cluster-MLP: an active learning genetic algorithm framework for accelerated discovery of global minimum configurations of pure and alloyed nanoclusters. J Chem Inf Model. 2023; 63(20): 6192-6197. https://doi.org/10.1021/acs.jcim.3c01431

[18] Gong F-Q, Liu Y, Wang Y, Tian Z, Cheng J. Machine learning molecular dynamics shows anomalous entropic effect on catalysis through surface pre-melting of nanoclusters. Angew Chem Int Ed. 2024; 63(27):e202405379. https://doi.org/10.1002/anie.202405379

[19] Unke OT, Stöhr M, Ganscha S, et al. Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments. Sci Adv. 2024; 10(14):eadn4397. https://doi.org/10.1126/sciadv.adn4397

[20] Itteboina R, Madhuri UD, Ghosal P, et al. Efficient one-pot synthesis and pH-dependent tuning of photoluminescence and stability of Au₁₈(SC₂H₄CO₂H)₁₄ cluster. J Phys Chem A. 2018; 122(5): 1228-1234. https://doi.org/10.1021/acs.jpca.7b10888

[21] Liu X, Tang Y, Gao J, Luo Q, Zeng Z, Wang Q. Room-temperature synthesis of novel polymeric nanocluster with emissions and its Cu²⁺ recognition performance. J Lumin. 2019; 205: 142-147. https://doi.org/10.1016/j.jlumin.2018.09.020

[22] Hudgens JW, Pettibone JM, Senftle TP, Bratton RN. Reaction mechanism governing formation of 1,3-bis(diphenylphosphino)propane-protected gold nanoclusters. Inorg Chem. 2011; 50(20): 10178-10189. https://doi.org/10.1021/ic2018506

[23] Cook AW, Hayton TW. Case studies in nanocluster synthesis and characterization: challenges and opportunities. Acc Chem Res. 2018; 51(10): 2456-2464. https://doi.org/10.1021/acs.accounts.8b00329

[24] Wang Z, Wang Y, Xu T, et al. Temperature-controlled selective formation of silver nanoclusters and their transformation to the same product. Angew Chem Int Ed Engl. 2024; 63(23):e202403464. https://doi.org/10.1002/anie.202403464

[25] Negishi Y, Nobusada K, Tsukuda T. Glutathione-protected gold clusters revisited: bridging the gap between gold(I)−thiolate complexes and thiolate-protected gold nanocrystals. J Am Chem Soc. 2005; 127(14): 5261-5270. https://doi.org/10.1021/ja042218h

[26] Yousefalizadeh G, Stamplecoskie KG. Norrish type I photochemistry as a powerful tool in the isolation of thiol protected Au₂₅SR₁₈ clusters. J Photochem Photobiol Chem. 2018; 353: 251-254. https://doi.org/10.1016/j.jphotochem.2017.11.033

[27] Santiago González B, Rodríguez MJ, Blanco C, Rivas J, López-Quintela MA, Martinho JMG. One step synthesis of the smallest photoluminescent and paramagnetic PVP-protected gold atomic clusters. Nano Lett. 2010; 10: 4217-4221. https://doi.org/10.1021/nl1026716

[28] Qian H, Zhu Y, Jin R. Size-focusing synthesis, optical and electrochemical properties of monodisperse Au₃₈(SC₂ H₄ Ph)₂₄ nanoclusters. ACS Nano. 2009; 3(11): 3795-3803. https://doi.org/10.1021/nn901137h

[29] Shichibu Y, Negishi Y, Tsukuda T, Teranishi T. Large-scale synthesis of thiolated Au₂₅ clusters via ligand exchange reactions of phosphine-stabilized Au₁₁ clusters. J Am Chem Soc. 2005; 127(39): 13464-13465. https://doi.org/10.1021/ja053915s

[30] Sugi KS, Bhat S, Nag A, Ganesan P, Mahendranath A, Pradeep T. Ligand structure and charge state-dependent separation of monolayer protected Au₂₅ clusters using non-aqueous reversed-phase HPLC. Analyst. 2020; 145(4): 1337-1345. https://doi.org/10.1039/c9an02043h

[31] Kimura K, Sugimoto N, Sato S, Yao H, Negishi Y, Tsukuda T. Size determination of gold clusters by polyacrylamide gel electrophoresis in a large cluster region. J Phys Chem C. 2009; 113(32): 14076-14082. https://doi.org/10.1021/jp808967p

[32] Chi M, Wei J, Zhao Z, et al. Structural isomerism of {Ag₁₄ }¹⁰⁺ nanocluster encapsulated by bowl-like polyoxometalates. Angew Chem Int Ed. 2025; 64(15):e202424499. https://doi.org/10.1002/anie.202424499

[33] Cowan MJ, Mpourmpakis G. Structure-property relationships on thiolate-protected gold nanoclusters. Nanoscale Adv. 2019; 1: 184-188. https://doi.org/10.1039/c8na00246k

[34] Tang L, Wang L, Wang B, Pei Y, Wang S. Discovering of atomically precise metal nanoclusters by high-throughput syntheses platform. Chem Eur J. 2024; 30(53):e202302602. https://doi.org/10.1002/chem.202302602

[35] Li J, Chen T, Lim K, et al. Deep learning accelerated gold nanocluster synthesis. Adv Intell Syst. 2019; 1(3):1900029. https://doi.org/10.1002/aisy.201900029

[36] Jin K, Wang W, Qi G, et al. An explainable machine-learning approach for revealing the complex synthesis path-property relationships of nanomaterials. Nanoscale. 2023; 15(37): 15358-15367. https://doi.org/10.1039/d3nr02273k

[37] Taylor MG, Mpourmpakis G. Thermodynamic stability of ligand-protected metal nanoclusters. Nat Commun. 2017; 8(1):15988. https://doi.org/10.1038/ncomms15988

[38] Heinecke CL, Ni TW, Malola S, et al. Structural and theoretical basis for ligand exchange on thiolate monolayer protected gold nanoclusters. J Am Chem Soc. 2012; 134(32): 13316-13322. https://doi.org/10.1021/ja3032339

[39] Sun C, Mammen N, Kaappa S, et al. Atomically precise, thiolated copper-hydride nanoclusters as single-site hydrogenation catalysts for ketones in mild conditions. ACS Nano. 2019; 13(5): 5975-5986. https://doi.org/10.1021/acsnano.9b02052

[40] Shen H, Wu Q, Malola S, et al. N-heterocyclic carbene-stabilized gold nanoclusters with organometallic motifs for promoting catalysis. J Am Chem Soc. 2022; 144(24): 10844-10853. https://doi.org/10.1021/jacs.2c02669

[41] Mammen N, Malola S, Honkala K, Häkkinen H. Dynamics of weak interactions in the ligand layer of meta -mercaptobenzoic acid protected gold nanoclusters Au₆₈ (m -MBA)₃₂ and Au₁₄₄ (m -MBA)₄₀. Nanoscale. 2020; 12(46): 23859-23868. https://doi.org/10.1039/d0nr07366k

[42] Cowan MJ, Mpourmpakis G. Towards elucidating structure of ligand-protected nanoclusters. Dalton Trans. 2020; 49(27): 9191-9202. https://doi.org/10.1039/d0dt01418d

[43] Sun X, Li S, Tian G, Gao Y, Wei J, Shen H. Ag₁₀ Cu₁₆ nanoclusters with triple-ligand protection: total structure and electronic structure analysis. Phys Chem Chem Phys. 2023; 25(27): 17901-17906. https://doi.org/10.1039/d3cp02227g

[44] Srivastava R. Application of optimization algorithms in clusters. Front Chem. 2021; 9:637286. https://doi.org/10.3389/fchem.2021.637286

[45] Zhou M, Xu Y, Cui Y, Zhang X, Kong X. Search for global minimum structures of P2n+1+ (n = 1-15) using xTB-based Basin-Hopping algorithm. Front Chem. 2021; 9:694156. https://doi.org/10.3389/fchem.2021.694156

[46] Joswig J-O, Springborg M. Genetic-algorithms search for global minima of aluminum clusters using a Sutton-Chen potential. Phys Rev B. 2003; 68(8):085408. https://doi.org/10.1103/physrevb.68.085408

[47] Grigoryan VG, Alamanova D, Springborg M. Structure and energetics of Cu N clusters with (2 ⩽ N ⩽ 150): an embedded-atom-method study. Phys Rev B. 2006; 73:115415.

[48] Gu Y, Tang S, Liu X, et al. Stability prediction of gold nanoclusters with different ligands and doped metals: deep learning and experimental tests. J Mater Chem A. 2024; 12(8): 4460-4472. https://doi.org/10.1039/d3ta06892g

[49] Malola S, Nieminen P, Pihlajamäki A, Hämäläinen J, Kärkkäinen T, Häkkinen H. A method for structure prediction of metal-ligand interfaces of hybrid nanoparticles. Nat Commun. 2019; 10(1):3973. https://doi.org/10.1038/s41467-019-12031-w

[50] Zhang Y, Butler KT, Higham MD, Catlow CRA. REMatch plus SOS: machine-learning-accelerated structure prediction for supported metal nanoclusters. Phys Rev Mater. 2025; 9(3):033801. https://doi.org/10.1103/physrevmaterials.9.033801

[51] Orlando Morais F, Andriani KF, Da Silva JLF. Investigation of the stability mechanisms of eight-atom binary metal clusters using DFT calculations and k-means clustering algorithm. J Chem Inf Model. 2021; 61(7): 3411-3420. https://doi.org/10.1021/acs.jcim.1c00253

[52] Fang L, Laakso J, Rinke P, Chen X. Machine-learning accelerated structure search for ligand-protected clusters. J Chem Phys. 2024; 160(9):094106. https://doi.org/10.1063/5.0180529

[53] Wei W, Mengshan L, Yan W, Lixin G. Cluster energy prediction based on multiple strategy fusion whale optimization algorithm and light gradient boosting machine. BMC Chem. 2024; 18(1): 24. https://doi.org/10.1186/s13065-024-01127-0

[54] Yang Q, Li Z-Y, Rodríguez-Kessler PL, He S-G. Accelerated global optimization of metal cluster structures via the deep neural network complemented with transfer learning and genetic algorithm. Chin J Chem Phys. 2024; 37: 321-329.

[55] Unke OT, Chmiela S, Sauceda HE, et al. Machine learning force fields. Chem Rev. 2021; 121(16): 10142-10186. https://doi.org/10.1021/acs.chemrev.0c01111

[56] Chmiela S, Sauceda HE, Müller K-R, Tkatchenko A. Towards exact molecular dynamics simulations with machine-learned force fields. Nat Commun. 2018; 9(1):3887. https://doi.org/10.1038/s41467-018-06169-2

[57] Ojwang’ JGO, Van Santen R, Kramer GJ, Van Duin ACT, Goddard WA. Predictions of melting, crystallization, and local atomic arrangements of aluminum clusters using a reactive force field. J Chem Phys. 2008; 129(24):244506. https://doi.org/10.1063/1.3050278

[58] Verkhovtsev AV, Sushko GB, Yakubovich AV, Solov’yov AV. Benchmarking of classical force fields by ab initio calculations of atomic clusters: Ti and Ni-Ti case. Comput Theor Chem. 2013; 1021: 101-108. https://doi.org/10.1016/j.comptc.2013.06.034

[59] Sushko GB, Verkhovtsev AV, Solov’yov AV. Validation of classical force fields for the description of thermo-mechanical properties of transition metal materials. J Phys Chem A. 2014; 118(37): 8426-8436. https://doi.org/10.1021/jp501723w

[60] Jia W, Wang H, Chen M, et al. Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning. In: SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE; 2020: 1-14. https://doi.org/10.1109/SC41405.2020.00009

[61] Li Y, Li H, Pickard FC, et al. Machine learning force field parameters from ab initio data. J Chem Theor Comput. 2017; 13(9): 4492-4503. https://doi.org/10.1021/acs.jctc.7b00521

[62] Huan TD, Batra R, Chapman J, Krishnan S, Chen L, Ramprasad R. A universal strategy for the creation of machine learning-based atomistic force fields. npj Comput Mater. 2017; 3(1): 37. https://doi.org/10.1038/s41524-017-0042-y

[63] Zhang L, Han J, Wang H, Car R, E W. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys Rev Lett. 2018; 120(14):143001. https://doi.org/10.1103/physrevlett.120.143001

[64] Panapitiya G, Avendaño-Franco G, Ren P, Wen X, Li Y, Lewis JP. Machine-learning prediction of CO adsorption in thiolated, Ag-alloyed Au nanoclusters. J Am Chem Soc. 2018; 140(50): 17508-17514. https://doi.org/10.1021/jacs.8b08800

[65] Mok DH, Li H, Zhang G, Lee C, Jiang K, Back S. Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning. Nat Commun. 2023; 14(1):7303. https://doi.org/10.1038/s41467-023-43118-0

[66] Fronzi M, Amos RD, Kobayashi R, Matsumura N, Watanabe K, Morizawa RK. Evaluation of machine learning interatomic potentials for the properties of gold nanoparticles. Nanomaterials. 2022; 12(21):3891. https://doi.org/10.3390/nano12213891

[67] McCandler CA, Pihlajamäki A, Malola S, Häkkinen H, Persson KA. Gold-thiolate nanocluster dynamics and intercluster reactions enabled by a machine learned interatomic potential. ACS Nano. 2024; 18(29): 19014-19023. https://doi.org/10.1021/acsnano.4c03094

[68] Wang Y, Liu S, Lile P, et al. Accelerated prediction of atomically precise cluster structures using on-the-fly machine learning. npj Comput Mater. 2022; 8(1):173. https://doi.org/10.1038/s41524-022-00856-x

[69] Han S, Barcaro G, Fortunelli A, Lysgaard S, Vegge T, Hansen HA. Unfolding the structural stability of nanoalloys via symmetry-constrained genetic algorithm and neural network potential. npj Comput Mater. 2022; 8: 1-11. https://doi.org/10.1038/s41524-022-00807-6

[70] Andolina CM, Bon M, Passerone D, Saidi WA. Robust, multi-length-scale, machine learning potential for Ag-Au bimetallic alloys from clusters to bulk materials. J Phys Chem C. 2021; 125(31): 17438-17447. https://doi.org/10.1021/acs.jpcc.1c04403

[71] Wang H, Zhang L, Han J, E W. DeePMD-kit: a deep learning package for many-body potential energy representation and molecular dynamics. Comput Phys Commun. 2018; 228: 178-184. https://doi.org/10.1016/j.cpc.2018.03.016

[72] Gromoff Q, Benzo P, Saidi WA, et al. Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations. Nanoscale. 2024; 16(1): 384-393. https://doi.org/10.1039/d3nr04471h

[73] Tanaka A, Hashimoto K, Kominami H. Selective photocatalytic oxidation of aromatic alcohols to aldehydes in an aqueous suspension of gold nanoparticles supported on cerium(iv) oxide under irradiation of green light. Chem Commun. 2011; 47(37):10446. https://doi.org/10.1039/c1cc13801d

[74] Sakthivel NA, Stener M, Sementa L, Fortunelli A, Ramakrishna G, Dass A. Au₂₇₉(SR)₈₄: the smallest gold thiolate nanocrystal that is metallic and the birth of plasmon. J Phys Chem Lett. 2018; 9(6): 1295-1300. https://doi.org/10.1021/acs.jpclett.8b00308

[75] Day PN, Pachter R, Nguyen KA, Jin R. Theoretical prediction of optical absorption and emission in thiolated gold clusters. J Phys Chem A. 2019; 123(30): 6472-6481. https://doi.org/10.1021/acs.jpca.9b02434

[76] Das R, Sarkar S, Saha M, Dey PC, Nath SS. Two peak luminescence from linoleic acid protected gold nanoparticles. J Lumin. 2015; 168: 325-329. https://doi.org/10.1016/j.jlumin.2015.08.047

[77] Kang X, Zhu M. Tailoring the photoluminescence of atomically precise nanoclusters. Chem Soc Rev. 2019; 48(8): 2422-2457. https://doi.org/10.1039/c8cs00800k

[78] Peyser LA, Vinson AE, Bartko AP, Dickson RM. Photoactivated fluorescence from individual silver nanoclusters. Science. 2001; 291(5501): 103-106. https://doi.org/10.1126/science.291.5501.103

[79] Jia X, Li J, Wang E. Cu nanoclusters with aggregation induced emission enhancement. Small. 2013; 9(22): 3873-3879. https://doi.org/10.1002/smll.201300896

[80] Zhang X-D, Wu D, Shen X, Liu PX, Fan FY, Fan SJ. In vivo renal clearance, biodistribution, toxicity of gold nanoclusters. Biomaterials. 2012; 33(18): 4628-4638. https://doi.org/10.1016/j.biomaterials.2012.03.020

[81] Du B, Jiang X, Das A, et al. Glomerular barrier behaves as an atomically precise bandpass filter in a sub-nanometre regime. Nat Nanotechnol. 2017; 12(11): 1096-1102. https://doi.org/10.1038/nnano.2017.170

[82] Porret E, Le Guével X, Coll J-L. Gold nanoclusters for biomedical applications: toward in vivo studies. J Mater Chem B. 2020; 8(11): 2216-2232. https://doi.org/10.1039/c9tb02767j

[83] Chen L-Y, Wang C-W, Yuan Z, Chang H-T. Fluorescent gold nanoclusters: recent advances in sensing and imaging. Anal Chem. 2015; 87(1): 216-229. https://doi.org/10.1021/ac503636j

[84] Senthamizhan A, Celebioglu A, Uyar T. Flexible and highly stable electrospun nanofibrous membrane incorporating gold nanoclusters as an efficient probe for visual colorimetric detection of Hg(II). J Mater Chem A. 2014; 2(32): 12717-12723. https://doi.org/10.1039/c4ta02295e

[85] Li H-W, Yue Y, Liu T-Y, Li D, Wu Y. Fluorescence-enhanced sensing mechanism of BSA-protected small gold-nanoclusters to silver(I) ions in aqueous solutions. J Phys Chem C. 2013; 117(31): 16159-16165. https://doi.org/10.1021/jp403466b

[86] Chen L-Y, Ou C-M, Chen W-Y, Huang C-C, Chang H-T. Synthesis of photoluminescent Au ND-PNIPAM hybrid microgel for the detection of Hg2+. ACS Appl Mater Interfaces. 2013; 5(10): 4383-4388. https://doi.org/10.1021/am400628p

[87] Mastracco P, Gonzàlez-Rosell A, Evans J, Bogdanov P, Copp SM. Chemistry-informed machine learning enables discovery of DNA-stabilized silver nanoclusters with near-infrared fluorescence. ACS Nano. 2022; 16(10): 16322-16331. https://doi.org/10.1021/acsnano.2c05390

[88] Zhai F, Guan Y, Li Y, Chen S, He R. Predicting the fluorescence properties of hairpin-DNA-templated silver nanoclusters via deep learning. ACS Appl Nano Mater. 2022; 5(7): 9615-9624. https://doi.org/10.1021/acsanm.2c01827

[89] Chen X, Xu J, Zhou H, et al. Tree-based machine learning models assisted fluorescent sensor array for detection of metal ions based on silver nanocluster probe. Spectrochim Acta A Mol Biomol Spectrosc. 2023; 297:122738. https://doi.org/10.1016/j.saa.2023.122738

[90] Noreldeen HAA, He SB, Huang KY, et al. Machine learning-based sensor array: full and reduced fluorescence data for versatile analyte detection based on gold nanocluster as a single probe. Anal Bioanal Chem. 2022; 414(29-30): 8365-8378. https://doi.org/10.1007/s00216-022-04372-1

[91] Wang Y, Chen L, Liu P. Biocompatible triplex Ag@SiO₂ @mTiO₂ core-shell nanoparticles for simultaneous fluorescence-SERS bimodal imaging and drug delivery. Chem Eur J. 2012; 18(19): 5935-5943. https://doi.org/10.1002/chem.201103571

[92] Zhao Z, Li Y. Developing fluorescent copper nanoclusters: synthesis, properties, and applications. Colloids Surf B Biointerfaces. 2020; 195:111244. https://doi.org/10.1016/j.colsurfb.2020.111244

[93] Allian AD, Takanabe K, Fujdala KL, et al. Chemisorption of CO and mechanism of CO oxidation on supported platinum nanoclusters. J Am Chem Soc. 2011; 133(12): 4498-4517. https://doi.org/10.1021/ja110073u

[94] De Amorim RV, Batista KEA, Nagurniak GR, Orenha RP, Parreira RLT, Piotrowski MJ. CO, NO, and SO adsorption on Ni nanoclusters: a DFT investigation. Dalton Trans. 2020; 49(19): 6407-6417. https://doi.org/10.1039/d0dt00288g

[95] Eren B, Zherebetskyy D, Patera LL, et al. Activation of Cu(111) surface by decomposition into nanoclusters driven by CO adsorption. Science. 2016; 351(6272): 475-478. https://doi.org/10.1126/science.aad8868

[96] Akbarzadeh H, Yaghoubi H, Shamkhali AN, Taherkhani F. CO adsorption on Ag nanoclusters supported on carbon nanotube: a molecular dynamics study. J Phys Chem C. 2014; 118(17): 9187-9195. https://doi.org/10.1021/jp412320w

[97] Hadipour NL, Ahmadi Peyghan A, Soleymanabadi H. Theoretical study on the Al-doped ZnO nanoclusters for CO chemical sensors. J Phys Chem C. 2015; 119(11): 6398-6404. https://doi.org/10.1021/jp513019z

[98] Gould AL, Catlow CRA, Logsdail AJ. Computational investigation of CO adsorbed on Aux, Agx and (AuAg)x nanoclusters (x = 1 − 5, 147) and monometallic Au and Ag low-energy surfaces. Eur Phys J B. 2018; 91(2): 32. https://doi.org/10.1140/epjb/e2017-80280-7

[99] Li F, Ahsen AS, Ammal SC, et al. Characterizing the surface compositions of supported bimetallic PtSn clusters: effects of cluster-support interactions and surface adsorbates. Surf Sci. 2025; 751:122623. https://doi.org/10.1016/j.susc.2024.122623

[100] Chen H, Cox DF. CO adsorption on MnO (100): experimental benchmarks compared to DFT. Surf Sci. 2021; 707:121808. https://doi.org/10.1016/j.susc.2021.121808

[101] Lisitsyn AS, Kadtsyna AS. Strong response of Pt clusters to the environment and conditions, formation of metastable states, and simple methods to trace the reversible changes. Phys Chem Chem Phys. 2021; 23(39): 22718-22732. https://doi.org/10.1039/d1cp01484f

[102] Han J, Fan Y, Yang Y, Liu Z. Structural evolution of MoS₂ supported gold nanoclusters under CO oxidation condition and the effect on reaction activity. Appl Surf Sci. 2021; 543:148841. https://doi.org/10.1016/j.apsusc.2020.148841

[103] Gasper R, Shi H, Ramasubramaniam A. Adsorption of CO on low-energy, low-symmetry Pt nanoparticles: energy decomposition analysis and prediction via machine-learning models. J Phys Chem C. 2017; 121(10): 5612-5619. https://doi.org/10.1021/acs.jpcc.6b12800

[104] Tereshchenko A, Pashkov D, Guda A, Guda S, Rusalev Y, Soldatov A. Adsorption sites on Pd nanoparticles unraveled by machine-learning potential with adaptive sampling. Molecules. 2022; 27(2):357. https://doi.org/10.3390/molecules27020357

[105] Song X, Xu L, Sun X, Han B. In situ/operando characterization techniques for electrochemical CO₂ reduction. Sci China Chem. 2023; 66(2): 315-323. https://doi.org/10.1007/s11426-021-1463-6

[106] Zhang D, Liu X, Zhao Y, Zhang H, Rudnev AV, Li JF. In situ Raman spectroscopic studies of CO₂ reduction reactions: from catalyst surface structures to reaction mechanisms. Chem Sci. 2025; 16(12): 4916-4936. https://doi.org/10.1039/d5sc00569h

[107] Bairagi A, Pereverzev AY, Tinnemans P, Pidko EA, Roithová J. Electrocatalytic CO₂ reduction: monitoring of catalytically active, downgraded, and upgraded cobalt complexes. J Am Chem Soc. 2024; 146(8): 5480-5492. https://doi.org/10.1021/jacs.3c13290

[108] Peterson AA, Abild-Pedersen F, Studt F, Rossmeisl J, Nørskov JK. How copper catalyzes the electroreduction of carbon dioxide into hydrocarbon fuels. Energy Environ Sci. 2010; 3(9):1311. https://doi.org/10.1039/c0ee00071j

[109] Seh ZW, Kibsgaard J, Dickens CF, Chorkendorff I, Nørskov JK, Jaramillo TF. Combining theory and experiment in electrocatalysis: insights into materials design. Science. 2017; 355(6321):eaad4998. https://doi.org/10.1126/science.aad4998

[110] Mohanta MK, Jena P. Activation and electrochemical reduction of carbon dioxide by transition metal atom-doped copper clusters. Nanoscale. 2025; 17(14): 8505-8514. https://doi.org/10.1039/D4NR03795B

[111] Kauffman DR, Alfonso D, Matranga C, et al. Probing active site chemistry with differently charged Au25q nanoclusters (q = −1, 0, +1). Chem Sci. 2014; 5(8):3151. https://doi.org/10.1039/c4sc00997e

[112] Stottko R, Dziadyk-Stopyra E, Szyja BM. Can machine learning predict the reaction paths in catalytic CO₂ reduction on small Cu/Ni clusters? Catalysts. 2023; 13(12):1470. https://doi.org/10.3390/catal13121470

[113] Chen Y, Huang Y, Cheng T, Goddard WA. Identifying active sites for CO₂ reduction on dealloyed gold surfaces by combining machine learning with multiscale simulations. J Am Chem Soc. 2019; 141(29): 11651-11657. https://doi.org/10.1021/jacs.9b04956

[114] Roy D, Das A, Pathak B. Conformal active learning-aided screening of ligand-protected Cu-nanoclusters for CO₂ reduction reactions. J Mater Chem A. 2024; 12(42): 29022-29032. https://doi.org/10.1039/d4ta03728f

[115] Zhang X, Wei J, Jia H, et al. Pursuing extreme descriptive power and generality in chemical bond theories: a method to decipher “interatomic genomes” from interatomic electron structures. J Chem Theor Comput. 2024; 20(19): 8482-8493. https://doi.org/10.1021/acs.jctc.4c00557

[116] Zhao X-G, Yang Q, Xu Y, et al. Machine learning for experimental reactivity of a set of metal clusters toward C-H activation. J Am Chem Soc. 2024; 146(18): 12485-12495. https://doi.org/10.1021/jacs.4c00501

[117] Wang X, Shi K, Peng A, Snurr RQ. Computational chemistry and machine learning-assisted screening of supported amorphous metal oxide nanoclusters for methane activation. ACS Catal. 2024; 14(24): 18708-18721. https://doi.org/10.1021/acscatal.4c04021

[118] Sharma RK, Jena MK, Minhas H, Pathak B. Machine-learning-assisted screening of nanocluster electrocatalysts: mapping and reshaping the activity volcano for the oxygen reduction reaction. ACS Appl Mater Interfaces. 2024; 16(46): 63589-63601. https://doi.org/10.1021/acsami.4c14076

RIGHTS & PERMISSIONS

2025 The Author(s). Journal of Intelligent Medicine published by John Wiley & Sons Australia, Ltd on behalf of Tianjin University.