AI-accelerated search for global minimum configurations of Pt-Ni nanoclusters using embedded-atom method interatomic potential
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Machine Learning in Materials Science · Electrocatalysts for Energy Conversion · Advanced Chemical Physics Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (56)
Show source-provided references
1. Li C, Clament Sagaya Selvam N, Fang J (2023) Shape-controlled synthesis of platinum-based nanocrystals and their electrocatalytic applications in fuel cells. Nano-Micro Lett 15:8310.1007/s40820-023-01060-2 ↗
2. Yang T, Zhao Y, Li W, Yu C, Luan J, Lin D, Fan L, Jiao Z, Liu W, Liu X (2020) Ultrahigh-strength and ductile superlattice alloys with nanoscale disordered interfaces. Science 369:427–43210.1126/science.abb6830 ↗
3. Ma L, Duan X, Chen K, Hou C, Li H, Ye J, Wu Y, Rong J, Yu X, Wei Y (2025) Deep potential model-based study on the thermodynamic, kinetic, and mechanical properties of PtNi alloys. Chem Eng Sci. https://doi.org/10.1016/j.ces.2025.12279310.1016/j.ces.2025.122793 ↗
4. Xu M, Jeon Y, Naden A, Kim H, Kerherve G, Payne DJ, Shul Y-G, Irvine JT (2024) Synergistic growth of nickel and platinum nanoparticles via exsolution and surface reaction. Nat Commun 15:400710.1038/s41467-024-48455-2 ↗
5. Cao A, Veser G (2010) Exceptional high-temperature stability through distillation-like self-stabilization in bimetallic nanoparticles. Nat Mater 9:75–8110.1038/nmat2584 ↗
6. Xu Z, Ji Y, Liu C, He L, Zhao H, Yuan Y, Qian Y, Cui J, Xiao A, Wang W (2024) A polymer-like ultrahigh-strength metal alloy. Nature 633:575–58110.1038/s41586-024-07900-4 ↗
7. Cheng D, Yuan S, Ferrando R (2013) Structure, chemical ordering and thermal stability of Pt–Ni alloy nanoclusters. J Phys Condens Matter 25:35500810.1088/0953-8984/25/35/355008 ↗
8. Awual MR, Hasan MM, Rahman MM, Asiri AM (2019) Novel composite material for selective copper (II) detection and removal from aqueous media. J Mol Liq 283:772–78010.1016/j.molliq.2019.03.141 ↗
9. Islam A, Malek A, Islam MT, Nipa FY, Raihan O, Mahmud H, Uddin ME, Ibrahim ML, Abdulkareem-Alsultan G, Mondal AH (2025) Next frontier in photocatalytic hydrogen production through CdS heterojunctions. Int J Hydrogen Energy 101:173–21110.1016/j.ijhydene.2024.12.300 ↗
10. Akbarzadeh H, Abbaspour M, Masoumi A, Mehrjouei E (2017) Dynamical investigation of formation of NiPt nanoclusters in gas phase. J Mol Liq 240:221–22410.1016/j.molliq.2017.05.109 ↗
11. Zhen H, Liu L, Lin Z, Gao S, Li X, Zhang X (2021) Physically compatible machine learning study on the Pt–Ni nanoclusters. J Phys Chem Lett 12:1573–158010.1021/acs.jpclett.0c03600 ↗
12. Kulkarni A, Siahrostami S, Patel A, Nørskov JK (2018) Understanding catalytic activity trends in the oxygen reduction reaction. Chem Rev 118:2302–231210.1021/acs.chemrev.7b00488 ↗
13. Ferreira de Morais R, Franco AA, Sautet P, Loffreda D (2016) How does the surface structure of Pt–Ni alloys control water and hydrogen peroxide formation? ACS Catal 6:5641–565010.1021/acscatal.6b00842 ↗
14. Wang K, Wang Y, Geng S, Wang Y, Song S (2022) High-temperature confinement synthesis of supported Pt–Ni nanoparticles for efficiently catalyzing oxygen reduction reaction. Adv Funct Mater 32:211339910.1002/adfm.202113399 ↗
15. Huang J, Sementa L, Liu Z, Barcaro G, Feng M, Liu E, Jiao L, Xu M, Leshchev D, Lee S-J (2022) Experimental Sabatier plot for predictive design of active and stable Pt-alloy oxygen reduction reaction catalysts. Nat Catal 5:513–52310.1038/s41929-022-00797-0 ↗
16. Tian X, Zhao X, Su Y-Q, Wang L, Wang H, Dang D, Chi B, Liu H, Hensen EJ, Lou XW (2019) Engineering bunched Pt-Ni alloy nanocages for efficient oxygen reduction in practical fuel cells. Science 366:850–85610.1126/science.aaw7493 ↗
17. He W, Wu W, Tang Z (2018) Ultrasmall PtNi bimetallic nanoclusters for oxygen reduction reaction in alkaline media. Int J Electrochem Sci 13:4438–445410.20964/2018.05.80 ↗
18. Ishiki NA, Della-Costa ML, Keller B, Rocha KO, Bortoloti F, Ângelo AC (2023) Impact of Pt-Ni nanoparticle architecture on electrocatalytic oxidation reaction in fuel cells. Catal Res 3:1–2710.21926/cr.2304027 ↗
19. Nørskov JK, Bligaard T, Logadottir A, Kitchin JR, Chen JG, Pandelov S, Stimming U (2005) Trends in the exchange current for hydrogen evolution. J Electrochem Soc 152:J23–J2610.1149/1.1856988 ↗
20. Javed H, Kolmeijer K, Deka N, van Spronsen MA, van Huis MA, Mohandas Sandhya AL, Khalakhan I, Mom RV (2025) The potential-dependent structure of Pt3Ni alloy electrocatalysts and its effect on electrocatalytic activity. ACS Catal 15:12994–1300210.1021/acscatal.5c02601 ↗
21. Hammer B, Nørskov JK (1995) Electronic factors determining the reactivity of metal surfaces. Surf Sci 343:211–22010.1016/0039-6028(96)80007-0 ↗
22. Stamenkovic VR, Fowler B, Mun BS, Wang G, Ross PN, Lucas CA, Markovic NM (2007) Improved oxygen reduction activity on Pt3Ni (111) via increased surface site availability. Science 315:493–49710.1126/science.1135941 ↗
23. Cui C, Gan L, Heggen M, Rudi S, Strasser P (2013) Compositional segregation in shaped Pt alloy nanoparticles and their structural behaviour during electrocatalysis. Nat Mater 12:765–77110.1038/nmat3668 ↗
24. Choi S-I, Xie S, Shao M, Odell JH, Lu N, Peng H-C, Protsailo L, Guerrero S, Park J, Xia X (2013) Synthesis and characterization of 9 nm Pt–Ni octahedra with a record high activity of 3.3 A/mgPt for the oxygen reduction reaction. Nano Lett 13:3420–342510.1021/nl401881z ↗
25. Chen C, Kang Y, Huo Z, Zhu Z, Huang W, Xin HL, Snyder JD, Li D, Herron JA, Mavrikakis M (2014) Highly crystalline multimetallic nanoframes with three-dimensional electrocatalytic surfaces. Science 343:1339–134310.1126/science.1249061 ↗
26. Jellinek J (2008) Nanoalloys: tuning properties and characteristics through size and composition. Faraday Discuss 138:11–3510.1039/b800086g ↗
27. Liu L, Corma A (2023) Bimetallic sites for catalysis: from binuclear metal sites to bimetallic nanoclusters and nanoparticles. Chem Rev 123:4855–493310.1021/acs.chemrev.2c00733 ↗
28. Ferrando R, Jellinek J, Johnston RL (2008) Nanoalloys: from theory to applications of alloy clusters and nanoparticles. Chem Rev 108:845–91010.1021/cr040090g ↗
29. Baletto F, Ferrando R (2005) Structural properties of nanoclusters: energetic, thermodynamic, and kinetic effects. Rev Mod Phys 77:371–42310.1103/RevModPhys.77.371 ↗
30. Li Z, Scheraga HA (1987) Monte Carlo-minimization approach to the multiple-minima problem in protein folding. Proc Natl Acad Sci U S A 84:6611–661510.1073/pnas.84.19.6611 ↗
31. Yalet NM, Quiña PLD, Ranea VA (2023) Electronic and magnetic properties of mono and bimetallic subnanometer clusters. A DFT research. Mater Today Commun 34:10548310.1016/j.mtcomm.2023.105483 ↗
32. Wen Z, Zhao Y, Zhao H, Chen Y, Xiao G, Su H (2026) Engineering bimetallic nanozyme structures for enhanced catalytic activity and multifunctional applications. Discov Catal 3:210.1007/s44344-025-00030-7 ↗
33. Vajda S, White MG (2015) Catalysis applications of size-selected cluster deposition. ACS Catal 5:7152–717610.1021/acscatal.5b01816 ↗
34. Xing X, Yoon B, Landman U, Parks JH (2006) Structural evolution of Au nanoclusters: from planar to cage to tubular motifs. Phys Rev B Condens Matter Mater Phys 74:16542310.1103/PhysRevB.74.165423 ↗
35. Wales DJ, Doye JP (1997) Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms. J Phys Chem A 101:5111–511610.1021/jp970984n ↗
36. Wales DJ, Scheraga HA (1999) Global optimization of clusters, crystals, and biomolecules. Science 285:1368–137210.1126/science.285.5432.1368 ↗
37. Johnston RL (2003) Evolving better nanoparticles: genetic algorithms for optimising cluster geometries. Dalton Trans. https://doi.org/10.1039/b305686d10.1039/b305686d ↗
38. Davis JB, Shayeghi A, Horswell SL, Johnston RL (2015) The Birmingham parallel genetic algorithm and its application to the direct DFT global optimisation of Ir N (N= 10–20) clusters. Nanoscale 7:14032–1403810.1039/C5NR03774C ↗
39. Zhang J, Dolg M (2015) ABCluster: the artificial bee colony algorithm for cluster global optimization. Phys Chem Chem Phys 17:24173–2418110.1039/C5CP04060D ↗
40. Jäger M, Schäfer R, Johnston RL (2019) GIGA: a versatile genetic algorithm for free and supported clusters and nanoparticles in the presence of ligands. Nanoscale 11:9042–905210.1039/C9NR02031D ↗
41. Call ST, Zubarev DY, Boldyrev AI (2007) Global minimum structure searches via particle swarm optimization. J Comput Chem 28:1177–118610.1002/jcc.20621 ↗
42. Zhai H, Alexandrova AN (2016) Ensemble-average representation of Pt clusters in conditions of catalysis accessed through GPU accelerated deep neural network fitting global optimization. J Chem Theory Comput 12:6213–622610.1021/acs.jctc.6b00994 ↗
43. Zhou XW, Johnson R, Wadley HN (2004) Misfit-energy-increasing dislocations in vapor-deposited CoFe/NiFe multilayers. Phys Rev B 69:14411310.1103/PhysRevB.69.144113 ↗
44. Mishin Y, Farkas D, Mehl M, Papaconstantopoulos D (1999) Interatomic potentials for monoatomic metals from experimental data and ab initio calculations. Phys Rev B 59:339310.1103/PhysRevB.59.3393 ↗
45. Gupta A, Dahale C, Maiti S, Srinivasan SG, Rai B (2024) Development of an embedded-atom method potential of Ni-Mo alloys for electrocatalysis/surface compositional studies, arXiv preprint arXiv:2409.07320. https://doi.org/10.48550/arXiv.2409.0732010.48550/arXiv.2409.07320 ↗
46. Foiles S, Baskes MI, Daw MS (1986) Embedded-atom-method functions for the fcc metals Cu. Ag, Au, Ni, Pd, Pt, and their alloys. Phys Rev B 33:798310.1103/PhysRevB.33.7983 ↗
47. Sheng H, Kramer M, Cadien A, Fujita T, Chen M (2011) Highly optimized embedded-atom-method potentials for fourteen fcc metals. Phys Rev B 83:13411810.1103/PhysRevB.83.134118 ↗
48. Cheng Y, Ma E, Sheng H (2009) Atomic level structure in multicomponent bulk metallic glass. Phys Rev Lett 102:24550110.1103/PhysRevLett.102.245501 ↗
49. Raju RK (2024) Exploring nanocluster potential energy surfaces via deep reinforcement learning: strategies for global minimum search. J Phys Chem A 128:9122–913410.1021/acs.jpca.4c04416 ↗
50. Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal policy optimization algorithms. arXiv preprint arXiv:170706347
51. Lillicrap TP, Hunt JJ, Pritzel A, Heess NMO, Erez T, Tassa Y, Silver D, Wierstra DP (2020) Continuous control with deep reinforcement learning, in, Google Patents. https://doi.org/10.48550/arXiv.1509.0297110.48550/arXiv.1509.02971 ↗
52. Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M (2013) Playing atari with deep reinforcement learning, arXiv preprint arXiv:1312.5602. https://doi.org/10.48550/arXiv.1312.560210.48550/arXiv.1312.5602 ↗
53. Xu Y, Shan S, Chai Y, Liu J, Li W, Farooq MU, Guo L, Tang Q, Jin T, Chen F (2023) Self-limiting oxidation of AgPdF nanoalloy models: a computational investigation. Chem Phys 573:11197210.1016/j.chemphys.2023.111972 ↗
54. Rodríguez-Kessler PL, Rodríguez-Domínguez AR, Muñoz-Castro A (2021) Structural evolution and electronic properties of intermediate sized tin (n= 33–60) clusters. Adv Theory Simul 4:210028310.1002/adts.202100283 ↗
55. Canestrari N, Nelli D, Ferrando R (2025) General theory for packing icosahedral shells into multi-component aggregates. Nat Commun 16:165510.1038/s41467-025-56952-1 ↗
56. Thompson AP, Aktulga HM, Berger R, Bolintineanu DS, Brown WM, Crozier PS, In’t Veld PJ, Kohlmeyer A, Moore SG, Nguyen TD (2022) LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput Phys Commun 271:10817110.1016/j.cpc.2021.108171 ↗