1. Meirer F, Weckhuysen BM. Spatial and temporal exploration of heterogeneous catalysts with synchrotron radiation. Nat Rev Mater 2018;3:324-40.

2. Toniato A, Vaucher AC, Laino T. Grand challenges on accelerating discovery in catalysis. Catal Today 2022;387:140-2.

3. Chu S, Cui Y, Liu N. The path towards sustainable energy. Nat Mater 2016;16:16-22.

4. 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:eaad4998.

5. Guan Q, Zhu C, Lin Y, et al. Bimetallic monolayer catalyst breaks the activity-selectivity trade-off on metal particle size for efficient chemoselective hydrogenations. Nat Catal 2021;4:840-9.

6. O’connor NJ, Jonayat ASM, Janik MJ, Senftle TP. Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning. Nat Catal 2018;1:531-9.

7. Liu L, Corma A. Metal catalysts for heterogeneous catalysis: from single atoms to nanoclusters and nanoparticles. Chem Rev 2018;118:4981-5079.

8. Cui X, Li W, Ryabchuk P, Junge K, Beller M. Bridging homogeneous and heterogeneous catalysis by heterogeneous single-metal-site catalysts. Nat Catal 2018;1:385-97.

9. Wang ZL, Yan JM, Ping Y, Wang HL, Zheng WT, Jiang Q. An efficient CoAuPd/C catalyst for hydrogen generation from formic acid at room temperature. Angew Chem Int Ed Engl 2013;52:4406-9.

10. Lang X, Han G, Xiao B, et al. Mesostructured intermetallic compounds of platinum and non-transition metals for enhanced electrocatalysis of oxygen reduction reaction. Adv Funct Mater 2015;25:230-7.

11. Qin Y, Zhang W, Guo K, et al. Fine-tuning intrinsic strain in penta-twinned Pt-Cu-Mn nanoframes boosts oxygen reduction catalysis. Adv Funct Mater 2020;30:1910107.

12. Yao R, Zhou Y, Shi H, et al. Nanoporous surface high-entropy alloys as highly efficient multisite electrocatalysts for nonacidic hydrogen evolution reaction. Adv Funct Mater 2021;31:2009613.

13. Yao Y, Dong Q, Brozena A, et al. High-entropy nanoparticles: synthesis-structure-property relationships and data-driven discovery. Science 2022;376:eabn3103.

14. Cantor B, Chang I, Knight P, Vincent A. Microstructural development in equiatomic multicomponent alloys. Mater Sci Eng A 2004;375-377:213-8.

15. Yeh J, Chen S, Lin S, et al. Nanostructured high-entropy alloys with multiple principal elements: novel alloy design concepts and outcomes. Adv Eng Mater 2004;6:299-303.

16. Cheng C, Zhang X, Haché MJR, Zou Y. Phase transition and nanomechanical properties of refractory high-entropy alloy thin films: effects of co-sputtering Mo and W on a TiZrHfNbTa system. Nanoscale 2022;14:7561-8.

17. Cheng C, Zhang X, Haché MJR, Zou Y. Magnetron co-sputtering synthesis and nanoindentation studies of nanocrystalline (TiZrHf)x(NbTa)1-x high-entropy alloy thin films. Nano Res 2022;15:4873-9.

18. Xin Y, Li S, Qian Y, et al. High-entropy alloys as a platform for catalysis: progress, challenges, and opportunities. ACS Catal 2020;10:11280-306.

19. Ma Y, Ma Y, Wang Q, et al. High-entropy energy materials: challenges and new opportunities. Energy Environ Sci 2021;14:2883-905.

20. Xie P, Yao Y, Huang Z, et al. Highly efficient decomposition of ammonia using high-entropy alloy catalysts. Nat Commun 2019;10:4011.

21. Mori K, Hashimoto N, Kamiuchi N, Yoshida H, Kobayashi H, Yamashita H. Hydrogen spillover-driven synthesis of high-entropy alloy nanoparticles as a robust catalyst for CO2 hydrogenation. Nat Commun 2021;12:3884.

22. Wang D, Chen Z, Huang Y, et al. Tailoring lattice strain in ultra-fine high-entropy alloys for active and stable methanol oxidation. Sci China Mater 2021;64:2454-66.

23. Wu D, Kusada K, Nanba Y, et al. Noble-metal high-entropy-alloy nanoparticles: atomic-level insight into the electronic structure. J Am Chem Soc 2022;144:3365-9.

24. Li H, Han Y, Zhao H, et al. Fast site-to-site electron transfer of high-entropy alloy nanocatalyst driving redox electrocatalysis. Nat Commun 2020;11:5437.

25. Chen ZW, Chen L, Gariepy Z, Yao X, Singh CV. High-throughput and machine-learning accelerated design of high entropy alloy catalysts. Trends Chem 2022;4:577-9.

26. Haché MJ, Cheng C, Zou Y. Nanostructured high-entropy materials. J Mater Res 2020;35:1051-75.

27. Wan X, Zhang Z, Niu H, et al. Machine-learning-accelerated catalytic activity predictions of transition metal phthalocyanine dual-metal-site catalysts for CO2 reduction. J Phys Chem Lett 2021;12:6111-8.

28. Chen ZW, Lu Z, Chen LX, Jiang M, Chen D, Singh CV. Machine-learning-accelerated discovery of single-atom catalysts based on bidirectional activation mechanism. Chem Catal 2021;1:183-95.

29. Zafari M, Kumar D, Umer M, Kim KS. Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts. J Mater Chem A 2020;8:5209-16.

30. Deng C, Su Y, Li F, Shen W, Chen Z, Tang Q. Understanding activity origin for the oxygen reduction reaction on bi-atom catalysts by DFT studies and machine-learning. J Mater Chem A 2020;8:24563-71.

31. Wan X, Zhang Z, Yu W, Niu H, Wang X, Guo Y. Machine-learning-assisted discovery of highly efficient high-entropy alloy catalysts for the oxygen reduction reaction. Patterns (N Y) 2022;3:100553.

32. Roy D, Mandal SC, Pathak B. Machine learning assisted exploration of high entropy alloy-based catalysts for selective CO2 reduction to methanol. J Phys Chem Lett 2022;13:5991-6002.

33. Nandy A, Duan C, Taylor MG, Liu F, Steeves AH, Kulik HJ. Computational discovery of transition-metal complexes: from high-throughput screening to machine learning. Chem Rev 2021;121:9927-10000.

34. Rodríguez-Martínez X, Pascual-San-José E, Campoy-Quiles M. Accelerating organic solar cell material’s discovery: high-throughput screening and big data. Energy Environ Sci 2021;14:3301-22.

35. Conway PL, Klaver T, Steggo J, Ghassemali E. High entropy alloys towards industrial applications: high-throughput screening and experimental investigation. Mater Sci Eng A 2022;830:142297.

36. Miracle D, Majumdar B, Wertz K, Gorsse S. New strategies and tests to accelerate discovery and development of multi-principal element structural alloys. Scr Mater 2017;127:195-200.

37. Mannodi-kanakkithodi A, Chan MK. Computational data-driven materials discovery. Trends Chem 2021;3:79-82.

38. Huang E, Lee W, Singh SS, et al. Machine-learning and high-throughput studies for high-entropy materials. Mater Sci Eng R Rep 2022;147:100645.

39. Lederer Y, Toher C, Vecchio KS, Curtarolo S. The search for high entropy alloys: a high-throughput ab-initio approach. Acta Mater 2018;159:364-83.

40. Ångqvist M, Muñoz WA, Rahm JM, et al. ICET - a python library for constructing and sampling alloy cluster expansions. Adv Theory Simul 2019;2:1900015.

41. van de Walle A, Tiwary P, de Jong M, et al. Efficient stochastic generation of special quasirandom structures. Calphad 2013;42:13-8.

42. de Walle A, Asta M, Ceder G. The alloy theoretic automated toolkit: a user guide. Calphad 2002;26:539-53.

43. Okhotnikov K, Charpentier T, Cadars S. Supercell program: a combinatorial structure-generation approach for the local-level modeling of atomic substitutions and partial occupancies in crystals. J Cheminform 2016;8:17.

44. Zunger A, Wei S, Ferreira LG, Bernard JE. Special quasirandom structures. Phys Rev Lett 1990;65:353-6.

45. Feugmo CG, Ryczko K, Anand A, Singh CV, Tamblyn I. Neural evolution structure generation: high entropy alloys. J Chem Phys 2021;155:044102.

46. Li B, Li X, Gao W, Jiang Q. An effective scheme to determine surface energy and its relation with adsorption energy. Acta Mater 2021;212:116895.

47. Sharma M, Jang J, Shin DY, et al. Work function-tailored graphene via transition metal encapsulation as a highly active and durable catalyst for the oxygen reduction reaction. Energy Environ Sci 2019;12:2200-11.

48. Duong T, Wang Y, Yan X, Couet A, Chaudhuri S. A first-principles-based approach to the high-throughput screening of corrosion-resistant high entropy alloys. arXiv preprint arXiv 2021;2104:10590.

49. Abild-Pedersen F, Greeley J, Studt F, et al. Scaling properties of adsorption energies for hydrogen-containing molecules on transition-metal surfaces. Phys Rev Lett 2007;99:016105.

50. Nørskov J, Bligaard T, Logadottir A, et al. Universality in heterogeneous catalysis. J Catal 2002;209:275-8.

51. Kuhl KP, Cave ER, Abram DN, Jaramillo TF. New insights into the electrochemical reduction of carbon dioxide on metallic copper surfaces. Energy Environ Sci 2012;5:7050.

52. Chen ZW, Gao W, Zheng WT, Jiang Q. Steric hindrance in sulfur vacancy of monolayer MoS2 boosts electrochemical reduction of carbon monoxide to methane. ChemSusChem 2018;11:1455-9.

53. Roy D, Mandal SC, Pathak B. Machine learning-driven high-throughput screening of alloy-based catalysts for selective CO2 hydrogenation to methanol. ACS Appl Mater Inter 2021;13:56151-63.

54. Singh AR, Rohr BA, Schwalbe JA, et al. Electrochemical ammonia synthesis - the selectivity challenge. ACS Catal 2017;7:706-9.

55. Chen Z, Lang XY, Jiang Q. Discovery of cobweb-like MoC6 and its application for nitrogen fixation. J Mater Chem A 2018;6:9623-8.

56. Saidi WA, Shadid W, Veser G. Optimization of high-entropy alloy catalyst for ammonia decomposition and ammonia synthesis. J Phys Chem Lett 2021;12:5185-92.

57. Montoya JH, Tsai C, Vojvodic A, Nørskov JK. The challenge of electrochemical ammonia synthesis: a new perspective on the role of nitrogen scaling relations. ChemSusChem 2015;8:2180-6.

58. Skúlason E, Bligaard T, Gudmundsdóttir S, et al. A theoretical evaluation of possible transition metal electro-catalysts for N2reduction. Phys Chem Chem Phys 2012;14:1235-45.

59. Nørskov JK, Rossmeisl J, Logadottir A, et al. Origin of the overpotential for oxygen reduction at a fuel-cell cathode. J Phys Chem B 2004;108:17886-92.

60. Batchelor TA, Pedersen JK, Winther SH, Castelli IE, Jacobsen KW, Rossmeisl J. High-entropy alloys as a discovery platform for electrocatalysis. Joule 2019;3:834-45.

61. Lu Z, Chen ZW, Singh CV. Neural network-assisted development of high-entropy alloy catalysts: decoupling ligand and coordination effects. Matter 2020;3:1318-33.

62. Saidi WA. Emergence of local scaling relations in adsorption energies on high-entropy alloys. NPJ Comput Mater 2022:8.

63. McCullough K, Williams T, Mingle K, Jamshidi P, Lauterbach J. High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery. Phys Chem Chem Phys 2020;22:11174-96.

64. Shukla S, Wang T, Frank M, et al. Friction stir gradient alloying: a novel solid-state high throughput screening technique for high entropy alloys. Mater Today Commun 2020;23:100869.

65. Zhu C, Li C, Wu D, et al. A titanium alloys design method based on high-throughput experiments and machine learning. J Mater Res Technol 2021;11:2336-53.

66. Coury FG, Wilson P, Clarke KD, Kaufman MJ, Clarke AJ. High-throughput solid solution strengthening characterization in high entropy alloys. Acta Mater 2019;167:1-11.

67. Moorehead M, Bertsch K, Niezgoda M, et al. High-throughput synthesis of Mo-Nb-Ta-W high-entropy alloys via additive manufacturing. Mater Des 2020;187:108358.

68. Pegues JW, Melia MA, Puckett R, Whetten SR, Argibay N, Kustas AB. Exploring additive manufacturing as a high-throughput screening tool for multiphase high entropy alloys. Addit Manuf 2021;37:101598.

69. Dobbelstein H, George EP, Gurevich EL, Kostka A, Ostendorf A, Laplanche G. Laser metal deposition of refractory high-entropy alloys for high-throughput synthesis and structure-property characterization. Int J Extrem Manuf 2021;3:015201.

70. Li M, Gazquez J, Borisevich A, Mishra R, Flores KM. Evaluation of microstructure and mechanical property variations in AlxCoCrFeNi high entropy alloys produced by a high-throughput laser deposition method. Intermetallics 2018;95:110-8.

71. Li M, Flores KM. Laser processing as a high-throughput method to investigate microstructure-processing-property relationships in multiprincipal element alloys. J Alloys Compd 2020;825:154025.

72. Zhao L, Jiang L, Yang L, et al. High throughput synthesis enabled exploration of CoCrFeNi-based high entropy alloys. J Mater Sci Technol 2022;110:269-82.

73. Xu Y, Bu Y, Liu J, Wang H. In-situ high throughput synthesis of high-entropy alloys. Scr Mater 2019;160:44-7.

74. Zhu B, Alavi S, Cheng C, et al. Fast and High-throughput synthesis of medium- and high-entropy alloys using radio frequency inductively coupled plasma. Adv Eng Mater 2021;23:2001116.

75. Huang J, Shi H, Ma Y, Yin H, Wang D. A combinatorial electrode for high-throughput, high-entropy alloy screening. ChemElectroChem 2021;8:4573-9.

76. Matsubara M, Suzumura A, Ohba N, Asahi R. Identifying superionic conductors by materials informatics and high-throughput synthesis. Commun Mater 2020:1.

77. Vecchio KS, Dippo OF, Kaufmann KR, Liu X. High-throughput rapid experimental alloy development (HT-READ). Acta Mater 2021;221:117352.

78. Yao Y, Huang Z, Li T, et al. High-throughput, combinatorial synthesis of multimetallic nanoclusters. Proc Natl Acad Sci U S A 2020;117:6316-22.

79. Shi Y, Yang B, Rack PD, Guo S, Liaw PK, Zhao Y. High-throughput synthesis and corrosion behavior of sputter-deposited nanocrystalline Alx(CoCrFeNi)100-x combinatorial high-entropy alloys. Mater Des 2020;195:109018.

80. Batchelor TAA, Löffler T, Xiao B, et al. Complex-solid-solution electrocatalyst discovery by computational prediction and high-throughput experimentation*. Angew Chem Int Ed Engl 2021;60:6932-7.

81. Banko L, Krysiak OA, Pedersen JK, et al. Unravelling composition-activity-stability trends in high entropy alloy electrocatalysts by using a data-guided combinatorial synthesis strategy and computational modeling. Adv Energy Mater 2022;12:2103312.

82. Yang Z, Gao W. Applications of machine learning in alloy catalysts: rational selection and future development of descriptors. Adv Sci (Weinh) 2022;9:e2106043.

83. Gao W, Chen Y, Li B, Liu SP, Liu X, Jiang Q. Determining the adsorption energies of small molecules with the intrinsic properties of adsorbates and substrates. Nat Commun 2020;11:1196.

84. Pedersen JK, Clausen CM, Krysiak OA, et al. Bayesian optimization of high-entropy alloy compositions for electrocatalytic oxygen reduction*. Angew Chem Int Ed Engl 2021;60:24144-52.

85. Zhong M, Tran K, Min Y, et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 2020;581:178-83.

86. Pedersen JK, Batchelor TAA, Bagger A, Rossmeisl J. High-entropy alloys as catalysts for the CO2 and CO reduction reactions. ACS Catal 2020;10:2169-76.

87. Tran K, Ulissi ZW. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat Catal 2018;1:696-703.

88. Li Z, Ma X, Xin H. Feature engineering of machine-learning chemisorption models for catalyst design. Catal Today 2017;280:232-8.

89. Huang B, von Lilienfeld OA. Quantum machine learning using atom-in-molecule-based fragments selected on the fly. Nat Chem 2020;12:945-51.

90. Li X, Chiong R, Hu Z, Cornforth D, Page AJ. Improved representations of heterogeneous carbon reforming catalysis using machine learning. J Chem Theory Comput 2019;15:6882-94.

91. Bartók AP, Kondor R, Csányi G. On representing chemical environments. Phys Rev B 2013:87.

92. Back S, Yoon J, Tian N, Zhong W, Tran K, Ulissi ZW. Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts. J Phys Chem Lett 2019;10:4401-8.

93. Gu GH, Noh J, Kim S, Back S, Ulissi Z, Jung Y. Practical deep-learning representation for fast heterogeneous catalyst screening. J Phys Chem Lett 2020;11:3185-91.

94. Christensen AS, Bratholm LA, Faber FA, Anatole von Lilienfeld O. FCHL revisited: faster and more accurate quantum machine learning. J Chem Phys 2020;152:044107.

95. Li X, Chiong R, Page AJ. Group and period-based representations for improved machine learning prediction of heterogeneous alloy catalysts. J Phys Chem Lett 2021;12:5156-62.

96. Li X, Li B, Yang Z, Chen Z, Gao W, Jiang Q. A transferable machine-learning scheme from pure metals to alloys for predicting adsorption energies. J Mater Chem A 2022;10:872-80.

97. Fu M, Ma X, Zhao K, Li X, Su D. High-entropy materials for energy-related applications. iScience 2021;24:102177.

98. Wang D, Chen Z, Wu Y, et al. Structurally ordered high-entropy intermetallic nanoparticles with enhanced C-C bond cleavage for ethanol oxidation. SmartMat 2022; doi: 10.1002/smm2.1117.

99. Feng G, Ning F, Song J, et al. Sub-2 nm ultrasmall high-entropy alloy nanoparticles for extremely superior electrocatalytic hydrogen evolution. J Am Chem Soc 2021;143:17117-27.

Journal of Materials Informatics
ISSN 2770-372X (Online)
Follow Us


All published articles are preserved here permanently:


All published articles are preserved here permanently: