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Abstract

There is increasing awareness of the imperative to accelerate materials discovery, design, development, and deployment. Materials design is essentially a goal-oriented activity that views the material as a complex system of interacting subsystems with models and experiments at multiple scales of materials structure hierarchy. The goal of materials design is effectively to invert quantitative relationships between process path, structure, and materials properties or responses to identify feasible materials. We first briefly discuss challenges in framing process-structure-property relationships for materials and the critical role of quantifying uncertainty and tracking its propagation through analysis and design. A case study exploiting inductive design of ultrahigh-performance concrete is briefly presented. We focus on important recent directions and key scientific challenges regarding the highly collaborative intersections of materials design with systems engineering, uncertainty quantification and management, optimization, and materials data science and informatics, which are essential to fueling continued progress in systems-based materials design.

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2019-07-01
2024-05-09
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Literature Cited

  1. 1.
    McDowell DL, Panchal J, Choi H-J, Seepersad C, Allen J, Mistree F 2010. Integrated Design of Multiscale, Multifunctional Materials and Products Oxford, UK: Butterworth-Heinemann
  2. 2.
    McDowell DL, Kalidindi SR. 2016. The materials innovation ecosystem: a key enabler for the Materials Genome Initiative. MRS Bull 41:326–37
    [Google Scholar]
  3. 3.
    Hale J. 2006. Boeing 787 from the ground up. AERO 4:7
    [Google Scholar]
  4. 4.
    Holdren JP. 2011. Materials Genome Initiative for global competitiveness Rep., Natl. Sci. Technol. Counc Washington, DC:
  5. 5.
    Breneman CM, Brinson LC, Schadler LS, Natarajan B, Krein M et al. 2013. Stalking the materials genome: a data‐driven approach to the virtual design of nanostructured polymers. Adv. Funct. Mater. 23:5746–52
    [Google Scholar]
  6. 6.
    White A. 2012. The Materials Genome Initiative: one year on. MRS Bull 37:715–16
    [Google Scholar]
  7. 7.
    Pollock TM, Allison JE, Backman DG, Boyce MC, Gersh M et al. 2008. Integrated computational materials engineering: a transformational discipline for improved competitiveness and national security. Rep 0-309-12000-4 Natl. Acad. Sci Washington, DC:
    [Google Scholar]
  8. 8.
    Olson GB. 1997. Computational design of hierarchically structured materials. Science 277:1237–42
    [Google Scholar]
  9. 9.
    Pollock TM, Tin S. 2006. Nickel-based superalloys for advanced turbine engines: chemistry, microstructure and properties. J. Propuls. Power 22:361–74
    [Google Scholar]
  10. 10.
    McDowell DL. 2007. Simulation-assisted materials design for the concurrent design of materials and products. JOM 59:21–25
    [Google Scholar]
  11. 11.
    Backman DG, Wei DY, Whitis DD, Buczek MB, Finnigan PM, Gao D 2006. ICME at GE: accelerating the insertion of new materials and processes. JOM 58:36–41
    [Google Scholar]
  12. 12.
    Rajan K. 2015. Materials informatics: the materials “gene” and big data. Annu. Rev. Mater. Res. 45:153–69
    [Google Scholar]
  13. 13.
    Lu G, Tadmor EB, Kaxiras E 2006. From electrons to finite elements: a concurrent multiscale approach for metals. Phys. Rev. B 73:024108
    [Google Scholar]
  14. 14.
    Curtin WA, Miller RE. 2003. Atomistic/continuum coupling in computational materials science. Model. Simul. Mater. Sci. Eng. 11:R33
    [Google Scholar]
  15. 15.
    Horstemeyer MF. 2012. Integrated Computational Materials Engineering (ICME) for Metals: Using Multiscale Modeling to Invigorate Engineering Design with Science Hoboken, NJ: Wiley
  16. 16.
    Allen JK, Seepersad C, Choi H, Mistree F 2006. Robust design for multiscale and multidisciplinary applications. J. Mech. Des. 128:832–43
    [Google Scholar]
  17. 17.
    Fullwood DT, Niezgoda SR, Adams BL, Kalidindi SR 2010. Microstructure sensitive design for performance optimization. Prog. Mater. Sci. 55:477–562
    [Google Scholar]
  18. 18.
    Thompson SC, Paredis CJ. 2010. An introduction to rational design theory Presented at ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Aug 15–18 Montreal:
  19. 19.
    Choi H, McDowell DL, Allen JK, Rosen D, Mistree F 2008. An inductive design exploration method for robust multiscale materials design. J. Mech. Des. 130:031402
    [Google Scholar]
  20. 20.
    Voorhees P, Spanos G. 2015. Modeling across scales: a roadmapping study for connecting materials models and simulations across length and time scales Rep., TMS
  21. 21.
    Olson GB. 2000. Designing a new material world. Science 288:993–98
    [Google Scholar]
  22. 22.
    Adams BL, Kalidindi SR, Fullwood DT 2012. Microstructure Sensitive Design for Performance Optimization Oxford, UK: Butterworth-Heinemann
  23. 23.
    Fast T, Knezevic M, Kalidindi SR 2008. Application of microstructure sensitive design to structural components produced from hexagonal polycrystalline metals. Comput. Mater. Sci. 43:374–83
    [Google Scholar]
  24. 24.
    Johnson L, Arróyave R. 2016. An inverse design framework for prescribing precipitation heat treatments from a target microstructure. Mater. Des. 107:7–17
    [Google Scholar]
  25. 25.
    McDowell DL, Olson G. 2008. Concurrent design of hierarchical materials and structures. Scientific Modeling and Simulations S Yip, T Diaz de la Rubia 207–40 Dordrecht, Neth: Springer
    [Google Scholar]
  26. 26.
    Panchal JH, Kalidindi SR, McDowell DL 2013. Key computational modeling issues in integrated computational materials engineering. Comput. Aided Des. 45:4–25
    [Google Scholar]
  27. 27.
    Chernatynskiy A, Phillpot SR, LeSar R 2013. Uncertainty quantification in multiscale simulation of materials: a prospective. Annu. Rev. Mater. Res. 43:157–82
    [Google Scholar]
  28. 28.
    Yabansu YC, Steinmetz P, Hötzer J, Kalidindi SR, Nestler B 2017. Extraction of reduced-order process-structure linkages from phase-field simulations. Acta Mater 124:182–94
    [Google Scholar]
  29. 29.
    Senn M. 2015. An integrated surrogate modeling approach for materials and process design. Proceedings of the 3rd World Congress on Integrated Computational Materials Engineering (ICME 2015), Vol. 35 W Poole, S Christensen, S Kalidindi, A Luo, J Martin, et al 331–38 Hoboken, NJ: Wiley
    [Google Scholar]
  30. 30.
    Seepersad CC, Kumar RS, Allen JK, Mistree F, McDowell DL 2004. Multifunctional design of prismatic cellular materials. J. Comput. Aided Mater. Des. 11:163–81
    [Google Scholar]
  31. 31.
    Taguchi G. 1993. Taguchi on Robust Technology Development: Bringing Quality Upstream New York: ASME
  32. 32.
    Taguchi G. 1986. Introduction to Quality Engineering: Designing Quality into Products and Processes White Plains, NY: Qual. Resour.
  33. 33.
    Chen W, Allen JK, Tsui K-L, Mistree F 1996. Procedure for robust design: minimizing variations caused by noise factors and control factors. J. Mech. Des. 118:478–85
    [Google Scholar]
  34. 34.
    Nair VN, Abraham B, MacKay J, Box G, Kacker RN et al. 1992. Taguchi's parameter design: a panel discussion. Technometrics 34:127–61
    [Google Scholar]
  35. 35.
    Ng KK, Tsui K-L. 1992. Expressing variability and yield with a focus on the customer. Qual. Eng. 5:255–67
    [Google Scholar]
  36. 36.
    Murphy TE, Tsui K-L, Allen JK 2005. A review of robust design methods for multiple responses. Res. Eng. Des. 15:201–15
    [Google Scholar]
  37. 37.
    Tsui K-L. 1999. Robust design optimization for multiple characteristic problems. Int. J. Prod. Res. 37:433–45
    [Google Scholar]
  38. 38.
    Agrawal A, Choudhary A. 2016. Perspective: materials informatics and big data: realization of the “fourth paradigm” of science in materials science. APL Mater 4:053208
    [Google Scholar]
  39. 39.
    Xiang X-D, Sun X, Briceno G, Lou Y, Wang K-A et al. 1995. A combinatorial approach to materials discovery. Science 268:1738–40
    [Google Scholar]
  40. 40.
    McFarland EW, Weinberg WH. 1999. Combinatorial approaches to materials discovery. Trends Biotechnol 17:107–15
    [Google Scholar]
  41. 41.
    Engstrom JR, Weinberg WH. 2000. Combinatorial materials science: paradigm shift in materials discovery and optimization. AIChE J 46:2–5
    [Google Scholar]
  42. 42.
    Koinuma H, Takeuchi I. 2004. Combinatorial solid-state chemistry of inorganic materials. Nat. Mater. 3:429–38
    [Google Scholar]
  43. 43.
    Xiong Z, He Y, Hattrick-Simpers JR, Hu J 2017. Automated phase segmentation for large-scale X-ray diffraction data using a graph-based phase segmentation (GPhase) algorithm. ACS Comb. Sci. 19:137–44
    [Google Scholar]
  44. 44.
    Curtarolo S, Hart GL, Nardelli MB, Mingo N, Sanvito S, Levy O 2013. The high-throughput highway to computational materials design. Nat. Mater. 12:191–201
    [Google Scholar]
  45. 45.
    Pulido A, Chen L, Kaczorowski T, Holden D, Little MA et al. 2017. Functional materials discovery using energy–structure–function maps. Nature 543:657–64
    [Google Scholar]
  46. 46.
    Hautier G, Jain A, Ong SP 2012. From the computer to the laboratory: materials discovery and design using first-principles calculations. J. Mater. Sci. 47:7317–40
    [Google Scholar]
  47. 47.
    Saal JE, Kirklin S, Aykol M, Meredig B, Wolverton C 2013. Materials design and discovery with high-throughput density functional theory: the Open Quantum Materials Database (OQMD). JOM 65:1501–9
    [Google Scholar]
  48. 48.
    Cohen AJ, Mori-Sánchez P, Yang W 2008. Insights into current limitations of density functional theory. Science 321:792–94
    [Google Scholar]
  49. 49.
    Rajan K. 2013. Informatics for Materials Science and Engineering: Data-Driven Discovery for Accelerated Experimentation and Application Oxford, UK: Butterworth-Heinemann
  50. 50.
    Ashby MF, Cebon D. 1993. Materials selection in mechanical design. J. Phys. IV 3:C7–1C7-9
    [Google Scholar]
  51. 51.
    Arroyave R, Shields S, Chang C-N, Fowler D, Malak R, Allaire D 2018. Interdisciplinary research on designing engineering materials systems: results from a National Science Foundation workshop. J. Mech. Des. 140:110801
    [Google Scholar]
  52. 52.
    Allison J. 2011. Integrated computational materials engineering: a perspective on progress and future steps. JOM 63:15–18
    [Google Scholar]
  53. 53.
    Srivastava A, Ghassemi-Armaki H, Sung H, Chen P, Kumar S, Bower AF 2015. Micromechanics of plastic deformation and phase transformation in a three-phase TRIP-assisted advanced high strength steel: experiments and modeling. J. Mech. Phys. Solids 78:46–69
    [Google Scholar]
  54. 54.
    Li SY. 2013. A computational-based approach for the design of TRIP steels PhD Thesis, Texas A&M Univ.
  55. 55.
    Saunders N, Miodownik AP. 1998. CALPHAD (Calculation of Phase Diagrams): A Comprehensive Guide Oxford, UK/New York: Pergamon Press479 pp.
    [Google Scholar]
  56. 56.
    Cao W, Chen S-L, Zhang F, Wu K, Yang Y et al. 2009. PANDAT software with PanEngine, PanOptimizer and PanPrecipitation for multi-component phase diagram calculation and materials property simulation. CALPHAD 33:328–42
    [Google Scholar]
  57. 57.
    Andersson J-O, Helander T, Höglund L, Shi P, Sundman B 2002. Thermo-Calc & DICTRA, computational tools for materials science. CALPHAD 26:273–312
    [Google Scholar]
  58. 58.
    Sundman B, Kattner UR, Palumbo M, Fries SG 2015. OpenCalphad: a free thermodynamic software. Integr. Mater. Manuf. Innov. 4:1
    [Google Scholar]
  59. 59.
    Turchi PE, Abrikosov IA, Burton B, Fries SG, Grimvall G et al. 2007. Interface between quantum-mechanical-based approaches, experiments, and CALPHAD methodology. CALPHAD 31:4–27
    [Google Scholar]
  60. 60.
    Bigdeli S. 2017. Developing the third generation of Calphad databases: What can ab-initio contribute? Dr. Thesis, KTH R. Inst. Technol .
    [Google Scholar]
  61. 61.
    Campbell CE, Boettinger WJ, Kattner UR 2002. Development of a diffusion mobility database for Ni-base superalloys. Acta Mater 50:775–92
    [Google Scholar]
  62. 62.
    Lu X-G, Selleby M, Sundman B 2005. Assessments of molar volume and thermal expansion for selected bcc, fcc and hcp metallic elements. CALPHAD 29:68–89
    [Google Scholar]
  63. 63.
    Kim D, Shang S-L, Liu Z-K 2009. Effects of alloying elements on elastic properties of Ni by first-principles calculations. Comput. Mater. Sci. 47:254–60
    [Google Scholar]
  64. 64.
    Xu W, Rivera-Díaz-del-Castillo P, Van der Zwaag S 2008. Designing nanoprecipitation strengthened UHS stainless steels combining genetic algorithms and thermodynamics. Comput. Mater. Sci. 44:678–89
    [Google Scholar]
  65. 65.
    Tancret F. 2012. Computational thermodynamics and genetic algorithms to design affordable γ′-strengthened nickel–iron based superalloys. Model. Simul. Mater. Sci. Eng. 20:045012
    [Google Scholar]
  66. 66.
    Menou E, Toda-Caraballo I, Rivera-Díaz-del-Castillo PEJ, Pineau C, Bertrand E et al. 2018. Evolutionary design of strong and stable high entropy alloys using multi-objective optimisation based on physical models, statistics and thermodynamics. Mater. Des. 143:185–95
    [Google Scholar]
  67. 67.
    Jha R, Dulikravich G, Colaco M, Fan M, Schwartz J, Koch C 2017. Magnetic alloys design using multi-objective optimization. Properties and Characterization of Modern Materials A Öchsner, H Altenbach 261–84 Singapore: Springer
    [Google Scholar]
  68. 68.
    Lu Q, Xu W, van der Zwaag S 2014. The design of a compositionally robust martensitic creep-resistant steel with an optimized combination of precipitation hardening and solid-solution strengthening for high-temperature use. Acta Mater 77:310–23
    [Google Scholar]
  69. 69.
    Gheribi AE, Harvey J-P, Bélisle E, Robelin C, Chartrand P et al. 2016. Use of a biobjective direct search algorithm in the process design of material science applications. Optim. Eng. 17:27–45
    [Google Scholar]
  70. 70.
    Lu Q, van der Zwaag S, Xu W 2017. Charting the ‘composition–strength’ space for novel austenitic, martensitic and ferritic creep resistant steels. J. Mater. Sci. Technol. 33:1577–81
    [Google Scholar]
  71. 71.
    Li S, Kattner UR, Campbell CE 2017. A computational framework for material design. Integr. Mater. Manuf. Innov. 6:229–48
    [Google Scholar]
  72. 72.
    Toda-Caraballo I, Galindo-Nava EI, Rivera-Díaz-del-Castillo PEJ 2013. Unravelling the materials genome: symmetry relationships in alloy properties. J. Alloys Comp. 566:217–28
    [Google Scholar]
  73. 73.
    Xiong W, Olson GB. 2015. Integrated computational materials design for high-performance alloys. MRS Bull 40:1035–44
    [Google Scholar]
  74. 74.
    Zhu J, Liu Z, Vaithyanathan V, Chen L 2002. Linking phase-field model to CALPHAD: application to precipitate shape evolution in Ni-base alloys. Scr. Mater. 46:401–6
    [Google Scholar]
  75. 75.
    Kim K, Roy A, Gururajan M, Wolverton C, Voorhees P 2017. First-principles/phase-field modeling of θ′ precipitation in Al-Cu alloys. Acta Mater 140:344–54
    [Google Scholar]
  76. 76.
    Wen Y, Wang B, Simmons J, Wang Y 2006. A phase-field model for heat treatment applications in Ni-based alloys. Acta Mater 54:2087–99
    [Google Scholar]
  77. 77.
    Kampmann R, Wagner R. 1984. Kinetics of precipitation in metastable binary alloys: theory and application to Cu–1.9 at % Ti and Ni–14 at % Al. Decomposition of Alloys: The Early Stages (Proceedings of the 2nd Acta-Scripta Metallurgica Conference) P Haasen, V Gerold, R Wagner, MF Ashby 91–103 Oxford, UK: Pergamon Press
    [Google Scholar]
  78. 78.
    Kozeschnik E, Svoboda J, Fischer F 2004. Modified evolution equations for the precipitation kinetics of complex phases in multi-component systems. CALPHAD 28:379–82
    [Google Scholar]
  79. 79.
    Kozeschnik E, Svoboda J, Fratzl P, Fischer F 2004. Modelling of kinetics in multi-component multi-phase systems with spherical precipitates. II. Numerical solution and application. Mater. Sci. Eng. A 385:157–65
    [Google Scholar]
  80. 80.
    Svoboda J, Fischer F, Fratzl P, Kozeschnik E 2004. Modelling of kinetics in multi-component multi-phase systems with spherical precipitates. I. Theory. Mater. Sci. Eng. A 385:166–74
    [Google Scholar]
  81. 81.
    Ghosh G, Olson G. 2007. Integrated design of Nb-based superalloys: ab initio calculations, computational thermodynamics and kinetics, and experimental results. Acta Mater 55:3281–303
    [Google Scholar]
  82. 82.
    Zhang C, Cao W, Chen S-L, Zhu J, Zhang F et al. 2014. Precipitation simulation of AZ91 alloy. JOM 66:389–96
    [Google Scholar]
  83. 83.
    Lang P, Rath M, Kozeschnik E, Rivera-Díaz-del-Castillo PEJ 2015. Modelling the influence of austenitisation temperature on hydrogen trapping in Nb containing martensitic steels. Scr. Mater. 101:60–63
    [Google Scholar]
  84. 84.
    Martin JH, Yahata BD, Hundley JM, Mayer JA, Schaedler TA, Pollock TM 2017. 3D printing of high-strength aluminium alloys. Nature 549:365–69
    [Google Scholar]
  85. 85.
    Galindo-Nava E, Rainforth W, Rivera-Díaz-del-Castillo PEJ 2016. Predicting microstructure and strength of maraging steels: elemental optimisation. Acta Mater 117:270–85
    [Google Scholar]
  86. 86.
    Houskamp JR, Proust G, Kalidindi SR 2007. Integration of microstructure-sensitive design with finite element methods: elastic-plastic case studies in FCC polycrystals. Int. J. Multiscale Comput. Eng. 5:261–72
    [Google Scholar]
  87. 87.
    Kalidindi SR. 2015. Data science and cyberinfrastructure: critical enablers for accelerated development of hierarchical materials. Int. Mater. Rev. 60:150–68
    [Google Scholar]
  88. 88.
    Kalidindi SR. 2012. Computationally efficient, fully coupled multiscale modeling of materials phenomena using calibrated localization linkages. ISRN Mater. Sci. 2012:305692
    [Google Scholar]
  89. 89.
    Wheeler D, Brough D, Fast T, Kalidindi S, Reid A 2014. PyMKS: Materials Knowledge System in Python https://doi.org/10.6084/m9.figshare.1015761
    [Crossref]
  90. 90.
    Niezgoda S, Fullwood D, Kalidindi S 2008. Delineation of the space of 2-point correlations in a composite material system. Acta Mater 56:5285–92
    [Google Scholar]
  91. 91.
    McDowell D, Ghosh S, Kalidindi S 2011. Representation and computational structure-property relations of random media. JOM 63:45–51
    [Google Scholar]
  92. 92.
    Niezgoda SR, Kanjarla AK, Kalidindi SR 2013. Novel microstructure quantification framework for databasing, visualization, and analysis of microstructure data. Integr. Mater. Manuf. Innov. 2:3
    [Google Scholar]
  93. 93.
    Gupta A, Cecen A, Goyal S, Singh AK, Kalidindi SR 2015. Structure–property linkages using a data science approach: application to a non-metallic inclusion/steel composite system. Acta Mater 91:239–54
    [Google Scholar]
  94. 94.
    Fast T, Niezgoda SR, Kalidindi SR 2011. A new framework for computationally efficient structure–structure evolution linkages to facilitate high-fidelity scale bridging in multi-scale materials models. Acta Mater 59:699–707
    [Google Scholar]
  95. 95.
    Yabansu YC, Kalidindi SR. 2015. Representation and calibration of elastic localization kernels for a broad class of cubic polycrystals. Acta Mater 94:26–35
    [Google Scholar]
  96. 96.
    Kröner E. 1977. Bounds for effective elastic moduli of disordered materials. J. Mech. Phys. Solids 25:137–55
    [Google Scholar]
  97. 97.
    Kröner E. 2001. Benefits and shortcomings of the continuous theory of dislocations. Int. J. Solids Struct. 38:1115–34
    [Google Scholar]
  98. 98.
    Korenberg MJ, Hunter IW. 1996. The identification of nonlinear biological systems: Volterra kernel approaches. Ann. Biomed. Eng. 24:250–68
    [Google Scholar]
  99. 99.
    Rudd RE, Broughton JQ. 2000. Concurrent coupling of length scales in solid state systems. Phys. Status Solid. B 217:251–91
    [Google Scholar]
  100. 100.
    Paulson NH, Priddy MW, McDowell DL, Kalidindi SR 2017. Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics. Acta Mater 129:428–38
    [Google Scholar]
  101. 101.
    Priddy MW, Paulson NH, Kalidindi SR, McDowell DL 2017. Strategies for rapid parametric assessment of microstructure-sensitive fatigue for HCP polycrystals. Int. J. Fatigue 104:231–42
    [Google Scholar]
  102. 102.
    Horstemeyer M, Wang P. 2003. Cradle-to-grave simulation-based design incorporating multiscale microstructure-property modeling: reinvigorating design with science. J. Comput. Aided Mater. Des. 10:13–34
    [Google Scholar]
  103. 103.
    Choi H-J. 2005. A robust design method for model and propagated uncertainty PhD Diss., Sch. Mech. Eng., Georgia Inst. Technol.
  104. 104.
    Ellis BD, McDowell DL. 2017. Application-specific computational materials design via multiscale modeling and the inductive design exploration method (IDEM). Integr. Mater. Manuf. Innov. 6:9–35
    [Google Scholar]
  105. 105.
    Kern PC, Priddy MW, Ellis BD, McDowell DL 2017. pyDEM: a generalized implementation of the inductive design exploration method. Mater. Des. 134:293–300
    [Google Scholar]
  106. 106.
    Wang R, Nellippallil AB, Wang G, Yan Y, Allen JK, Mistree F 2018. Systematic design space exploration using a template-based ontological method. Adv. Eng. Inform. 36:163–77
    [Google Scholar]
  107. 107.
    Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C 2017. Machine learning in materials informatics: recent applications and prospects. NPJ Comput. Mater. 3:54
    [Google Scholar]
  108. 108.
    Forsmark JH, Zindel JW, Godlewski L, Zheng J, Allison JE, Li M 2015. Using quality mapping to predict spatial variation in local properties and component performance in Mg alloy thin-walled high-pressure die castings: an ICME approach and case study. Integr. Mater. Manuf. Innov. 4:6
    [Google Scholar]
  109. 109.
    Lookman T, Balachandran PV, Xue D, Hogden J, Theiler J 2017. Statistical inference and adaptive design for materials discovery. Curr. Opin. Solid State Mater. Sci. 21:121–28
    [Google Scholar]
  110. 110.
    Brochu E, Cora VM, De Freitas N 2010. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv:1012.2599 [cs.LG]
  111. 111.
    Good IJ, Hacking I, Jeffrey R, Törnebohm H 1966. The estimation of probabilities: an essay on modern Bayesian methods. Synthese 16:234–44
    [Google Scholar]
  112. 112.
    Jones DR, Schonlau M, Welch WJ 1998. Efficient global optimization of expensive black-box functions. J. Glob. Optim. 13:455–92
    [Google Scholar]
  113. 113.
    Xue D, Balachandran PV, Hogden J, Theiler J, Xue D, Lookman T 2016. Accelerated search for materials with targeted properties by adaptive design. Nat. Commun. 7:11241
    [Google Scholar]
  114. 114.
    Xue D, Balachandran PV, Yuan R, Hu T, Qian X et al. 2016. Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning. PNAS 113:13301–6
    [Google Scholar]
  115. 115.
    Xue D, Xue D, Yuan R, Zhou Y, Balachandran PV et al. 2017. An informatics approach to transformation temperatures of NiTi-based shape memory alloys. Acta Mater 125:532–41
    [Google Scholar]
  116. 116.
    Emmerich MT, Deutz AH, Klinkenberg JW 2011. Hypervolume-based expected improvement: monotonicity properties and exact computation Presented at 2011 IEEE Congress on Evolutionary Computation (CEC) June 5–8 New Orleans:
  117. 117.
    Solomou A, Zhao G, Boluki S, Joy JK, Qian X et al. 2018. Multi-objective Bayesian materials discovery: application on the discovery of precipitation strengthened NiTi shape memory alloys through micromechanical modeling. Mater. Des. 160:810–27
    [Google Scholar]
  118. 118.
    Talapatra A, Boluki S, Duong T, Qian X, Dougherty E, Arróyave R 2018. Autonomous efficient experiment design for materials discovery with Bayesian model averaging. Phys. Rev. Mater. 2:113803
    [Google Scholar]
  119. 119.
    Talapatra A, Boluki S, Duong T, Qian X, Dougherty E, Arróyave R 2018. Towards an autonomous efficient materials discovery framework: an example of optimal experiment design under model uncertainty. arXiv 180305460
  120. 120.
    Ghoreishi SF, Molkeri A, Srivastava A, Arroyave R, Allaire D 2018. Multi-information source fusion and optimization to realize ICME: application to dual-phase materials. J. Mech. Des. 140:111409
    [Google Scholar]
  121. 121.
    Frazier P, Powell W, Dayanik S 2009. The knowledge-gradient policy for correlated normal beliefs. INFORMS J. Comput. 21:599–613
    [Google Scholar]
  122. 122.
    Chen S, Jiang Z, Yang S, Chen W 2016. Multimodel fusion based sequential optimization. AIAA J 55:241–54
    [Google Scholar]
  123. 123.
    Pilania G, Gubernatis JE, Lookman T 2017. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput. Mater. Sci. 129:156–63
    [Google Scholar]
  124. 124.
    Qian PZ, Wu CJ. 2008. Bayesian hierarchical modeling for integrating low-accuracy and high-accuracy experiments. Technometrics 50:192–204
    [Google Scholar]
  125. 125.
    Tallman AE, Swiler LP, Wang Y, McDowell DL 2017. Reconciled top-down and bottom-up hierarchical multiscale calibration of bcc Fe crystal plasticity. Int. J. Multiscale Comput. Eng. 15:505–23
    [Google Scholar]
  126. 126.
    McDowell DL. 2014. Critical path issues in ICME. Models, Databases and Simulation Tools Needed for Realization of Integrated Computational Materials Engineering SM Arnold, TT Wong 31–37 Materials Park, OH: ASM Int.
    [Google Scholar]
  127. 127.
    Chang C-N, Semma B, Fowler D, Arroyave R 2017. An interdisciplinary graduate education model for the materials engineering field Presented at American Society of Engineering Education Conference and Exposition, June 25–28 Columbus, OH:
  128. 128.
    Chang C-N, Semma B, Pardo ML, Fowler D, Shamberger P, Arroyave R 2017. Data-enabled discovery and design of energy materials (D3EM): structure of an interdisciplinary materials design graduate program. MRS Adv 2:1693–98
    [Google Scholar]
  129. 129.
    Fowler D, Arroyave R, Ross J, Malak R, Banerjee S 2017. Looking outwards from the “central science”: an interdisciplinary perspective on graduate education in materials chemistry. Educational and Outreach Projects from the Cottrell Scholars, Vol. 1 R Waterman, A Feig 65–89 Washington, DC: Am. Chem. Soc.
    [Google Scholar]
  130. 130.
    Georgia Inst. Technol., Univ. Wisconsin, Univ. Michigan 2014. Building an integrated materials genome initiative accelerator network Rep. of Workshop, June 5–6 Atlanta, GA: http://acceleratornetwork.org/wp-uploads/2014/09/MAN-MGI-REPORT-2015.pdf
  131. 131.
    Nellippallil AB, Rangaraj V, Gautham BP, Singh AK, Allen JK, Mistree F. 2018. An inverse, decision-based design method for integrated design exploration of materials, products, and manufacturing processes. ASME J. Mech. Design 140:11111403
    [Google Scholar]
  132. 132.
    Nellippallil AB, Mohan P, Allen JK, Mistree F. 2018. Robust concept exploration of materials, products and associated manufacturing processes Presented at ASME Design Automation Conference, Pap. DETC2018-85913, Quebec City, Can
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