REFERENCES
4. Kalidindi SR, Brough DB, Li S, et al. Role of materials data science and informatics in accelerated materials innovation. MRS Bull 2016;41:596-602.
5. Kalidindi SR, Medford AJ, Mcdowell DL. Vision for data and informatics in the future materials innovation ecosystem. JOM 2016;68:2126-37.
7. Liu Z. Ocean of data: integrating first-principles calculations and CALPHAD modeling with machine learning. J Phase Equilib Diffus 2018;39:635-49.
8. A national strategic plan for advanced manufacturing. Available from: https://www.nist.gov/oam/national-strategic-plan-advanced-manufacturing [Last accessed on 23 Feb 2022].
9. Wang WY, Li P, Lin D, et al. DID code: a bridge connecting the materials genome engineering database with inheritable integrated intelligent manufacturing. Engineering 2020;6:612-20.
10. Olson GB. Computational design of hierarchically structured materials. Science 1997;277:1237-42.
11. Raccuglia P, Elbert KC, Adler PD, et al. Machine-learning-assisted materials discovery using failed experiments. Nature 2016;533:73-6.
13. Kalinin SV, Sumpter BG, Archibald RK. Big-deep-smart data in imaging for guiding materials design. Nat Mater 2015;14:973-80.
15. Curtarolo S, Hart GL, Nardelli MB, Mingo N, Sanvito S, Levy O. The high-throughput highway to computational materials design. Nat Mater 2013;12:191-201.
16. Debnath A, Krajewski AM, Sun H, et al. Generative deep learning as a tool for inverse design of high entropy refractory alloys. J Mater Inf 2021;1:3.
17. National Science and Technology Council. Materials genome initiative for global competitiveness. Available from: https://www.researchgate.net/publication/267901251_Materials_Genome_Initiative_for_Global_Competitiveness [Last accessed on 23 Feb 2022].
18. Wang W, Li J, Liu W, Liu Z. Integrated computational materials engineering for advanced materials: a brief review. Computational Materials Science 2019;158:42-8.
19. Zhou J, Li P, Zhou Y, Wang B, Zang J, Meng L. Toward new-generation intelligent manufacturing. Engineering 2018;4:11-20.
20. Zhong RY, Xu X, Klotz E, Newman ST. Intelligent manufacturing in the context of Industry 4.0: a review. Engineering 2017;3:616-30.
21. Rickman J, Lookman T, Kalinin S. Materials informatics: from the atomic-level to the continuum. Acta Materialia 2019;168:473-510.
22. Lookman T, Alexander F, Rajan K. Information science for materials discovery and design. Cham: Springer; 2016.
23. Liu Z, Mcdowell DL. The Penn State-Georgia Tech CCMD: ushering in the ICME Era. Integr Mater Manuf Innov 2014;3:409-28.
24. . National Academies of Sciences, Engineering, and Medicine; Policy and Global Affairs; Government-University-Industry Research Roundtable. The Fourth Industrial Revolution: Proceedings of a Workshop - In Brief. Washington: The National Academies Press; 2017.
25. The future of manufacturing: a new era of opportunity and challenge for the UK. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/25 [Last accessed on 23 Feb 2022].
27. Cui B, Mei H, Ooi BC. Big data: the driver for innovation in databases. Natl Sci Rev 2014;1:27-30.
28. Boschert S, Rosen R. Digital twin - the simulation aspect. In: Hehenberger P, Bradley D, editoes. Mechatronic Futures. Cham: Springer; 2016. p. 59-74.
29. Wang WY, Tang B, Lin D, et al. A brief review of data-driven ICME for intelligently discovering advanced structural metal materials: Insight into atomic and electronic building blocks. J Mater Res 2020;35:872-89.
30. Yi Y, Yan Y, Liu X, Ni Z, Feng J, Liu J. Digital twin-based smart assembly process design and application framework for complex products and its case study. J Manuf Syst 2021;58:94-107.
31. Ferguson S. Apollo 13: the first digital twin. Available from: https://blogs.sw.siemens.com/simcenter/apollo-13-the-first-digital-twin/ [Last accessed on 23 Feb 2022].
32. Jose R, Ramakrishna S. Materials 4.0: materials big data enabled materials discovery. Applied Materials Today 2018;10:127-32.
33. Xiong W, Olson GB. Cybermaterials: materials by design and accelerated insertion of materials. npj Comput Mater 2016:2.
34. Craig PL. Modeling software for Materials 4.0. Available from: https://news.psu.edu/story/670090/2021/09/21/academics/modeling-software-materials-40 [Last accessed on 23 Feb 2022].
35. Bocklund B, Otis R, Egorov A, Obaied A, Roslyakova I, Liu Z. ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg. MRS Communications 2019;9:618-27.
37. Kaufman L, Ågren J. CALPHAD, first and second generation - Birth of the materials genome. Scripta Materialia 2014;70:3-6.
38. Olson G, Kuehmann C. Materials genomics: from CALPHAD to flight. Scripta Materialia 2014;70:25-30.
40. Liu Z. First-principles calculations and CALPHAD modeling of thermodynamics. J Phase Equilib Diffus 2009;30:517-34.
41. Wang Y, Liao M, Bocklund BJ, et al. DFTTK: Density Functional Theory ToolKit for high-throughput lattice dynamics calculations. Calphad 2021;75:102355.
42. Shin D, Saal J. Computational materials system design. 1st ed. Cham: Springer; 2018. DOI: 10.1007/978-3-319-68280-8
43. Zhou BC, Wang WY, Liu ZK, Arroyave R. Electrons to phases of magnesium. In: Horstemeyer MF, editor. Integrated computational materials engineering (ICME) for metals: concepts and case studies. Hoboken: John Wiley & Sons; 2018; p. 237-82.
44. Liu X, Furrer D, Kosters J, Holmes J. Vision 2040: a roadmap for integrated, multiscale modeling and simulation of materials and systems. Available from: https://ntrs.nasa.gov/api/citations/20180002010/downloads/20180002010.pdf [Last accessed on 23 Feb 2022].
45. National Science & Technology Council. Strategy for American Leadership in Advanced Manufacturing. Available from: https://trumpwhitehouse.archives.gov/wp-content/uploads/2018/10/Advanced-Manufacturing-Strategic-Plan-2018.pdf [Last accessed on 23 Feb 2022].
46. Broderick SR, Santhanam GR, Rajan K. Harnessing the big data paradigm for ICME: shifting from materials selection to materials enabled design. JOM 2016;68:2109-15.
47. Zhang M, Tao F, Huang B, et al. Digital twin data: methods and key technologies. digitaltwin 2021;1:2.
48. Robbins DW, Hartwig JF. A simple, multidimensional approach to high-throughput discovery of catalytic reactions. Science 2011;333:1423-7.
49. Otis RA, Liu Z. High-throughput thermodynamic modeling and uncertainty quantification for ICME. JOM 2017;69:886-92.
50. de Walle A, Sun R, Hong Q, Kadkhodaei S. Software tools for high-throughput CALPHAD from first-principles data. Calphad 2017;58:70-81.
51. Mounet N, Gibertini M, Schwaller P, et al. Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Nat Nanotechnol 2018;13:246-52.
52. Li R, Xie L, Wang WY, Liaw PK, Zhang Y. High-throughput calculations for high-entropy alloys: a brief review. Front Mater 2020;7:290.
53. Shang S, Zhou B, Wang WY, et al. A comprehensive first-principles study of pure elements: vacancy formation and migration energies and self-diffusion coefficients. Acta Materialia 2016;109:128-41.
54. Saal JE, Kirklin S, Aykol M, Meredig B, Wolverton C. Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD). JOM 2013;65:1501-9.
55. Jain A, Hautier G, Moore CJ, et al. A high-throughput infrastructure for density functional theory calculations. Computational Materials Science 2011;50:2295-310.
56. Krajewski AM, Siegel JW, Xu J, Liu ZK. Extensible structure-informed prediction of formation energy with improved accuracy and usability employing neural networks. Available from: https://arxiv.org/abs/2008.13654. https://arxiv.org/abs/2008.13654 [Last accessed on 23 Feb 2022].
57. Kim K, Ward L, He J, Krishna A, Agrawal A, Wolverton C. Machine-learning-accelerated high-throughput materials screening: discovery of novel quaternary Heusler compounds. Phys Rev Materials 2018;2:123801.
58. Curtarolo S, Setyawan W, Hart GL, et al. AFLOW: an automatic framework for high-throughput materials discovery. Computational Materials Science 2012;58:218-26.
59. Oganov AR, Pickard CJ, Zhu Q, Needs RJ. Structure prediction drives materials discovery. Nat Rev Mater 2019;4:331-48.
60. Mathew K, Montoya JH, Faghaninia A, et al. Atomate: a high-level interface to generate, execute, and analyze computational materials science workflows. Computational Materials Science 2017;139:140-52.
61. Baskes MI. Application of the embedded-atom method to covalent materials: a semiempirical potential for silicon. Phys Rev Lett 1987;59:2666-9.
62. Daw MS, Baskes MI. Semiempirical, quantum mechanical calculation of hydrogen embrittlement in metals. Phys Rev Lett 1983;50:1285-8.
63. Mishin Y, Lozovoi A. Angular-dependent interatomic potential for tantalum. Acta Materialia 2006;54:5013-26.
64. Mishin Y, Mehl M, Papaconstantopoulos D. Phase stability in the Fe-Ni system: investigation by first-principles calculations and atomistic simulations. Acta Materialia 2005;53:4029-41.
65. Onat B, Cubuk ED, Malone BD, Kaxiras E. Implanted neural network potentials: application to Li-Si alloys. Phys Rev B 2018;97:094106.
66. Zong H, Pilania G, Ding X, Ackland GJ, Lookman T. Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning. npj Comput Mater 2018;4:48.
67. 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;120:143001.
68. 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. SC20: International Conference for High Performance Computing, Networking, Storage and Analysis; 2020 Nov 9-19; Atlanta, GA, USA. IEEE; 2020. p. 1-14.
69. Chen X, Gao X, Zhao Y, Lin D, Chu W, Song H. TensorAlloy: an automatic atomistic neural network program for alloys. Computer Physics Communications 2020;250:107057.
70. Chen X, Wang L, Gao X, et al. Machine learning enhanced empirical potentials for metals and alloys. Computer Physics Communications 2021;269:108132.
71. Shang HH, Chen X, Gao XY, et al. TensorKMC: kinetic monte Carlo simulation of 50 trillion atoms driven by deep learning on a new generation of Sunway supercomputer. The International Conference for High Performance Computing, Networking, Storage and Analysis (SC’ 21); St. Louis, Missouri. New York, NY, USA: Association for Computing Machinery; 2021. p. 1-14.
75. Zou C, Li J, Wang WY, et al. Integrating data mining and machine learning to discover high-strength ductile titanium alloys. Acta Materialia 2021;202:211-21.
76. Wang WY, Shang SL, Wang Y, et al. Atomic and electronic basis for the serrations of refractory high-entropy alloys. npj Comput Mater 2017:3.
77. Wang WY, Darling KA, Wang Y, et al. Power law scaled hardness of Mn strengthened nanocrystalline AlMn non-equilibrium solid solutions. Scripta Mater 2016;120:31-6.
78. Umehara M, Stein HS, Guevarra D, Newhouse PF, Boyd DA, Gregoire JM. Analyzing machine learning models to accelerate generation of fundamental materials insights. npj Comput Mater 2019;5:34.
80. Rickman JM, Chan HM, Harmer MP, et al. Materials informatics for the screening of multi-principal elements and high-entropy alloys. Nat Commun 2019;10:2618.
81. Ye Y, Wang Q, Lu J, Liu C, Yang Y. High-entropy alloy: challenges and prospects. Materials Today 2016;19:349-62.
83. Bramer M. Decision tree induction: using entropy for attribute selection. Principles of data mining. London: Springer; 2016. p. 49-62.
84. Bramer M. Decision tree induction: using frequency tables for attribute selection. Principles of data mining. London: Springer; 2016. p. 63-78.
85. Liu ZK. Materials 4.0 and the Materials Genome Initative. Adv Mater Process 2020;178:50.
86. Chiarello F, Trivelli L, Bonaccorsi A, Fantoni G. Extracting and mapping industry 4.0 technologies using wikipedia. Comput Ind 2018;100:244-57.
87. Qi Q, Tao F. Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access 2018;6:3585-93.
88. Qi Q, Tao F, Hu T, et al. Enabling technologies and tools for digital twin. J Manuf Syst 2021;58:3-21.
89. Tao F, Qi Q, Wang L, Nee A. Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering 2019;5:653-61.
90. Pettey C. Prepare for the impact of digital twins. Available from: https://www.gartner.com/smarterwithgartner/prepare-for-the-impact-of-digital-twins [Last accessed on 23 Feb 2022].
91. Lim KYH, Zheng P, Chen C, Huang L. A digital twin-enhanced system for engineering product family design and optimization. J Manuf Syst 2020;57:82-93.
92. Mukhtarkhanov M, Perveen A, Talamona D. Application of stereolithography based 3D printing technology in investment casting. Micromachines (Basel) 2020;11:946.