[1] [1] MacDonald E and Wicker R. 2016. Multiprocess 3D printing for increasing component functionality.Science353, aaf2093.

[2] [2] Herzog D, Seyda V, Wycisk E and Emmelmann C. 2016. Additive manufacturing of metals.Acta Mater.117, 371–392.

[3] [3] Kelly B E, Bhattacharya I, Heidari H, Shusteff M, Spadaccini C M and Taylor H K. 2019. Volumetric additive manufacturing via tomographic reconstruction.Science363, 1075–1079.

[4] [4] Walker D A, Hedrick J L and Mirkin C A. 2019. Rapid, largevolume, thermally controlled 3D printing using a mobile liquid interface.Science366, 360–364.

[5] [5] Zastrow M. 2020. 3D printing gets bigger, faster and stronger.Nature578, 20–23.

[6] [6] Gu D D, Shi X Y, Poprawe R, Bourell D L, Setchi R and Zhu J H. 2021. Material-structure-performance integrated laser-metal additive manufacturing.Science372, eabg1487.

[7] [7] Fan J X, Zhang L, Wei S S, Zhang Z, Choi S-K, Song B and Shi Y S. 2021. A review of additive manufacturing of metamaterials and developing trends.Mater. Today50, 303–328.

[8] [8] Martin J H, Yahata B D, Hundley J M, Mayer J A, Schaedler T A and Pollock T M. 2017. 3D printing of high-strength aluminium alloys.Nature549, 365–369.

[9] [9] Wang Y M et al. 2018. Additively manufactured hierarchical stainless steels with high strength and ductility.Nat. Mater.17, 63–71.

[10] [10] Zhang Y J, Song B, Ming J, Yan Q, Wang M, Cai C, Zhang C and Shi Y S. 2020. Corrosion mechanism of amorphous alloy strengthened stainless steel composite fabricated by selective laser melting.Corros. Sci.163, 108241.

[11] [11] Zhang T L, Huang Z H, Yang T, Kong H J, Luan J H, Wang A D, Wang D, Kuo W, Wang Y Z and Liu C-T. 2021. In situ design of advanced titanium alloy with concentration modulations by additive manufacturing.Science374, 478–482.

[12] [12] Zhang J L, Gao J B, Song B, Zhang L J, Han C J, Cai C, Zhou K and Shi Y S. 2021. A novel crack-free Ti-modified Al-Cu-Mg alloy designed for selective laser melting.Addit. Manuf.38, 101829.

[13] [13] Zhang L-C and Wang J. 2024. Stabilizing 3D-printed metal alloys.Science383, 586–587.

[14] [14] Wei S S, Zhang J L, Zhang L, Zhang Y J, Song B, Wang X B, Fan J X, Liu Q and Shi Y S. 2023. Laser powder bed fusion additive manufacturing of NiTi shape memory alloys: a review.Int. J. Extrem. Manuf.5, 032001.

[15] [15] Neumann C, Thore J, Clozel M, Gnster J, Wilbig J and Meyer A. 2023. Additive manufacturing of metallic glass from powder in space.npj Microgravity9, 80.

[16] [16] Zhang J Y et al. 2022. Recent development of chemically complex metallic glasses: from accelerated compositional design, additive manufacturing to novel applications.Mater. Futures1, 012001.

[17] [17] Liu H S, Jiang Y Y, Yang D F, Jiang Q and Yang W M. 2023. Pores and cracks in the metallic glasses prepared by laser powder bed fusion.J. Mater. Res. Technol.26, 3070–3089.

[18] [18] Ren J et al. 2022. Strong yet ductile nanolamellar high-entropy alloys by additive manufacturing.Nature608, 62–68.

[19] [19] Tong Z P, Zhang Y Z, Liu H, Wan W, Zhou W F, Ye Y X and Ren X D. 2024. Laser shock peening of AlCoCrCuFeNi high-entropy alloy fabricated by laser powder bed fusion: an enhanced oxidation-resistance mechanism at high-temperature.Corros. Sci.226, 111667.

[20] [20] Yan X H, Zou Y and Zhang Y. 2022. Properties and processing technologies of high-entropy alloys.Mater. Futures1, 022002.

[21] [21] Zhang J L, Song B, Wei Q S, Bourell D and Shi Y S. 2019. A review of selective laser melting of aluminum alloys: processing, microstructure, property and developing trends.J. Mater. Sci. Technol.35, 270–284.

[22] [22] Ma H Y, Wang J C, Qin P, Liu Y J, Chen L Y, Wang L Q and Zhang L C. 2024. Advances in additively manufactured titanium alloys by powder bed fusion and directed energy deposition: microstructure, defects, and mechanical behavior.J. Mater. Sci. Technol.183, 32–62.

[23] [23] Qu Z et al. 2024. High fatigue resistance in a titanium alloy via near-void-free 3D printing.Nature626, 999–1004.

[24] [24] Abd-Elaziem W et al. 2022. On the current research progress of metallic materials fabricated by laser powder bed fusion process: a review.J. Mater. Res. Technol.20, 681–707.

[25] [25] Kim R E, Jeong S G, Ha H, Lee D W, Amanov A and Kim H S. 2023. Controlling defects of laser-powder bed fusion processed 316L stainless steel via ultrasonic nanocrystalline surface modification.Mater. Sci. Eng.A887, 145726.

[26] [26] Meng L B, McWilliams B, Jarosinski W, Park H-Y, Jung Y-G, Lee J and Zhang J. 2020. Machine learning in additive manufacturing: a review.JOM72, 2363–2377.

[27] [27] Meng L B and Zhang J. 2020. Process design of laser powder bed fusion of stainless steel using a gaussian process-based machine learning model.JOM72, 420–428.

[28] [28] Luo Q X, Huang N, Fu T Y, Wang J Y, Bartles D L, Simpson T W and Beese A M. 2023. New insight into the multivariate relationships among process, structure, and properties in laser powder bed fusion AlSi10Mg.Addit. Manuf.77, 103804.

[29] [29] Wang J C, Zhu R, Liu Y J and Zhang L C. 2023. Understanding melt pool characteristics in laser powder bed fusion: an overview of single- and multi-track melt pools for process optimization.Adv. Powder Mater.2, 100137.

[30] [30] Mao S S. 2013. High throughput growth and characterization of thin film materials.J. Cryst. Growth379, 123–130.

[31] [31] Vecchio K S, Dippo O F, Kaufmann K R and Liu X. 2021. High-throughput rapid experimental alloy development (HT-READ).Acta Mater.221, 117352.

[32] [32] Zhao J-C. 2001. A combinatorial approach for structural materials.Adv. Eng. Mater.3, 143–147.

[33] [33] Wilson P, Field R and Kaufman M. 2016. The use of diffusion multiples to examine the compositional dependence of phase stability and hardness of the Co-Cr-Fe-Mn-Ni high entropy alloy system.Intermetallics75, 15–24.

[34] [34] Wei M, Zhao L, Jiang L W, Yang L X and Wang H Z. 2023. Development of high-throughput rapid heat-treatment and characterization process.Acta Mater.261, 119365.

[35] [35] Zheng Z W, Yue X Z, Wang J C and Hou J. 2024. A framework for general-purpose microscopic image analysis via self-supervised learning.Mater. Charact.213, 114003.

[36] [36] Wang H et al. 2024. Recent advances in machine learningassisted fatigue life prediction of additive manufactured metallic materials: a review.J. Mater. Sci. Technol.198, 111–136.

[37] [37] Luo Q X, Yin L, Simpson T W and Beese A M. 2022. Effect of processing parameters on pore structures, grain features, and mechanical properties in Ti-6Al-4V by laser powder bed fusion.Addit. Manuf.56, 102915.

[38] [38] Liu Q, Chen W L, Yakubov V, Kruzic J J, Wang C H and Li X P. 2024. Interpretable machine learning approach for exploring process-structure-property relationships in metal additive manufacturing.Addit. Manuf.85, 104187.

[39] [39] Hertlein N, Deshpande S, Venugopal V, Kumar M and Anand S. 2020. Prediction of selective laser melting part quality using hybrid Bayesian network.Addit. Manuf.32, 101089.

[40] [40] Lu C Y, Jia X D, Lee J and Shi J. 2022. Knowledge transfer using Bayesian learning for predicting the process-property relationship of Inconel alloys obtained by laser powder bed fusion.Virtual Phys. Prototyp.17, 787–805.

[41] [41] Ng W L, Goh G L, Goh G D, Ten J S J and Yeong W Y. 2024. Progress and opportunities for machine learning in materials and processes of additive manufacturing.Adv. Mater.36, 2310006.

[42] [42] Wang C, Tan X P, Tor S B and Lim C S. 2020. Machine learning in additive manufacturing: state-of-the-art and perspectives.Addit. Manuf.36, 101538.

[43] [43] Brug G J, van den Eeden A L G, Sluyters-Rehbach M and Sluyters J H. 1984. The analysis of electrode impedances complicated by the presence of a constant phase element.J. Electroanal. Chem. Interfacial Electrochem.176, 275–295.

[44] [44] Zuo Y, Mastronardi V, Gamberini A, Zappia M I, Le T H H, Prato M, Dante S, Bellani S and Manna L. 2024. Stainless steel activation for efficient alkaline oxygen evolution in advanced electrolyzers.Adv. Mater.36, 2312071.

[45] [45] Zhu D X et al. 2024. A transfer learning strategy for tensile strength prediction in austenitic stainless steel across temperatures.Scr. Mater.251, 116210.

[46] [46] ener B, avuȘolu O and Yuce C. 2023. Effects of process parameters on surface quality and mechanical performance of 316L stainless steel produced by selective laser melting.Optik287, 171050.

[47] [47] Zheng Q, Chen H S, Zhou J, Wang W X, Xi S X and Yuan Y. 2023. Effect of boron element on microstructure and mechanical properties of 316L stainless steel manufactured by selective laser melting.J. Mater. Res. Technol.26, 3744–3755.

[48] [48] Lv J L, Zhou Z P, Wang Z Q and Xiong Y D. 2023. The effect of build orientation on tensile properties and corrosion resistance of 316L stainless steel fabricated by laser powder bed fusion.J. Manuf. Process.106, 363–369.

[49] [49] Diller J, Blankenhagen J, Siebert D, Radlbeck C and Mensinger M. 2024. Combined effect of surface treatment and heat treatment on the fatigue properties of AISI 316L, manufactured by powder bed fusion of metals using a laser (PBF-LB/M).Int. J. Fatigue178, 108025.

[50] [50] Tolosa I, Garcianda F, Zubiri F, Zapirain F and Esnaola A. 2010. Study of mechanical properties of AISI 316 stainless steel processed by “selective laser melting”, following different manufacturing strategies.Int. J. Adv. Manuf. Technol.51, 639–647.

[51] [51] Chao Q, Thomas S, Birbilis N, Cizek P, Hodgson P D and Fabijanic D. 2021. The effect of post-processing heat treatment on the microstructure, residual stress and mechanical properties of selective laser melted 316L stainless steel.Mater. Sci. Eng.A821, 141611.

[52] [52] Schreiber M, Speer J G, Klemm-Toole J, Gockel J, Brice C and Findley K O. 2024. Influence of annealing on microstructures and mechanical properties of laser powder bed fusion and wire arc directed energy deposition additively manufactured 316L.Mater. Sci. Eng.A917, 147390.

[53] [53] Kong D C, Ni X Q, Dong C F, Zhang L, Man C, Yao J Z, Xiao K and Li X G. 2018. Heat treatment effect on the microstructure and corrosion behavior of 316L stainless steel fabricated by selective laser melting for proton exchange membrane fuel cells.Electrochim. Acta276, 293–303.

[54] [54] Stern F, Becker L, Tenkamp J, Boes J, Lentz J, Weber S and Walther F. 2023. Influence of nitrogen content on the corrosion fatigue behavior of additively manufactured AISI 316L stainless steel in chloride solution.Int. J. Fatigue172, 107666.

[55] [55] Pathote D, Jaiswal D, Singh V and Behera C K. 2022. Optimization of electrochemical corrosion behavior of 316L stainless steel as an effective biomaterial for orthopedic applications.Mater. Today Proc.57, 265–269.

[56] [56] Pathote D, Jaiswal D, Singh V and Behera C K. 2023. Electrochemical corrosion behavior of tantalum coated 316L stainless steel by D.C. Magnetron sputtering for orthopedic applications.Appl. Surf. Sci. Adv.13, 100365.

[57] [57] Zhao C L, Bai Y C, Zhang Y, Wang X P, Xue J M and Wang H. 2021. Influence of scanning strategy and building direction on microstructure and corrosion behaviour of selective laser melted 316L stainless steel.Mater. Des.209, 109999.

[58] [58] Zhang L et al. 2024. Wood-inspired metamaterial catalyst for robust and high-throughput water purification.Nat. Commun.15, 2046.

[59] [59] Zhang C, Li X-M, Liu S-Q, Liu H, Yu L-J and Liu L. 2019. 3D printing of Zr-based bulk metallic glasses and components for potential biomedical applications.J. Alloys Compd.790, 963–973.

[60] [60] Xue D Z, Tian Y, Yuan R H and Lookman T. 2020. Bayesian Global Optimization applied to the design of shape-memory alloys.In Uncertainty Quantification in Multiscale Materials Modeling(eds Wang Y and McDowell D L) (Woodhead Publishing, Duxford) pp 519–537.

[61] [61] Chen Y F, Tian Y, Zhou Y M, Fang D Q, Ding X D, Sun J and Xue D Z. 2020. Machine learning assisted multi-objective optimization for materials processing parameters: a case study in Mg alloy.J. Alloys Compd.844, 156159.

[62] [62] Tian Y, Lookman T and Xue D Z. 2021. Efficient sampling for decision making in materials discovery.Chin. Phys.B30, 050705.

[63] [63] Efron B. 2014. Estimation and accuracy after model selection.J. Am. Stat. Assoc.109, 991–1007.

[64] [64] Beyaztas U and Alin A. 2014. Sufficient jackknifeafter-bootstrap method for detection of influential observations in linear regression models.Stat. Pap.55, 1001–1018.

[65] [65] Bisbo M K and Hammer B. 2020. Efficient global structure optimization with a machine-learned surrogate model.Phys. Rev. Lett.124, 086102.

[66] [66] Jones D R, Schonlau M and Welch W J. 1998. Efficient global optimization of expensive black-box functions.J. Glob. Optim.13, 455–492.

[67] [67] Ryzhov I O, Powell W B and Frazier P I. 2012. The knowledge gradient algorithm for a general class of online learning problems.Oper. Res.60, 180–195.