Materials Design Through Batch Bayesian Optimization with Multisource Information Fusion

Integrated computational materials engineering (ICME) calls for the integration of simulation tools and experiments to accelerate the development of materials. ICME approaches tend to be computationally costly, and recently, Bayesian optimization (BO) has been proposed as a way to make ICME more res...

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Veröffentlicht in:	JOM (1989) Jg. 72; H. 12; S. 4431 - 4443
Hauptverfasser:	Couperthwaite, Richard, Molkeri, Abhilash, Khatamsaz, Danial, Srivastava, Ankit, Allaire, Douglas, Arròyave, Raymundo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2020 Springer Nature B.V
Schlagworte:	Alloys Augmenting Physics-based Models in ICME with Machine Learning and Uncertainty Quantification Bayesian analysis Case studies Chemistry/Food Science Data integration Design optimization Dual phase steels Ductility Earth Sciences Engineering Environment Experiments Fashion models Information sources Materials engineering Materials science Materials selection Optimization Physics Simulation Stochastic models model fusion Computational materials science and engineering knowledge gradient reification
ISSN:	1047-4838, 1543-1851
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Abstract	Integrated computational materials engineering (ICME) calls for the integration of simulation tools and experiments to accelerate the development of materials. ICME approaches tend to be computationally costly, and recently, Bayesian optimization (BO) has been proposed as a way to make ICME more resource efficient. Conventional BO, however, is sequential (i.e., one-at-a-time) in nature, which makes it very time-consuming when the evaluation of a materials design choice is costly. While conventional high-throughput approaches enable the synthesis and characterization (or simulation) of materials in a parallel manner, they tend to be “open loop” and are unable to provide recommendations of what to try next once the parallel experiment/simulation has been carried out and analyzed. Here, we address this problem by introducing a batch BO framework that enables the exploration of the material’s design space in a parallel fashion. We augment this approach by incorporating information fusion frameworks capable of integrating multiple information sources. Demonstrating the proposed approach in the computational design of dual-phase steel, we show that batch BO can result in a significant reduction in the time and resources needed to carry out the design task. The proposed approach has wider applicability, well beyond the ICME example used to demonstrate it.
AbstractList	Integrated computational materials engineering (ICME) calls for the integration of simulation tools and experiments to accelerate the development of materials. ICME approaches tend to be computationally costly, and recently, Bayesian optimization (BO) has been proposed as a way to make ICME more resource efficient. Conventional BO, however, is sequential (i.e., one-at-a-time) in nature, which makes it very time-consuming when the evaluation of a materials design choice is costly. While conventional high-throughput approaches enable the synthesis and characterization (or simulation) of materials in a parallel manner, they tend to be "open loop" and are unable to provide recommendations of what to try next once the parallel experiment/ simulation has been carried out and analyzed. Here, we address this problem by introducing a batch BO framework that enables the exploration of the material's design space in a parallel fashion. We augment this approach by incorporating information fusion frameworks capable of integrating multiple information sources. Demonstrating the proposed approach in the computational design of dual-phase steel, we show that batch BO can result in a significant reduction in the time and resources needed to carry out the design task. The proposed approach has wider applicability, well beyond the ICME example used to demonstrate it.
Author	Khatamsaz, Danial Arròyave, Raymundo Couperthwaite, Richard Allaire, Douglas Srivastava, Ankit Molkeri, Abhilash
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Cites_doi	10.1137/070693424 10.1023/A:1008306431147 10.1002/9781118309858 10.1007/978-3-662-40293-1 10.1016/j.mtphys.2020.100180 10.1146/annurev-matsci-070218-125955 10.1016/j.jmat.2017.08.001 10.1016/j.jmat.2017.07.002 10.1007/3-540-07165-2_55 10.1016/j.actamat.2019.09.009 10.1115/1.3653121 10.1051/metal:2002182 10.1007/s11837-006-0223-5 10.3389/fmats.2019.00082 10.1007/978-3-642-10701-6_6 10.1016/j.apmt.2020.100560 10.1103/PhysRevMaterials.2.113803 10.1115/1.4041034 10.1016/j.cossms.2014.02.003 10.1007/978-1-4612-1494-6 10.1287/mnsc.27.4.479 10.1002/andp.18892741206 10.1016/j.jmat.2017.07.004 10.1016/j.knosys.2019.06.026
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References	D. Ginsbourger, R. Le Riche, L. Carraro, in Computational Intelligence in Expensive Optimization Problems, ed. by Y. Tenne, C.K. Goh (Springer Berlin Heidelberg, Berlin, Heidelberg, 2010), pp. 131–162. LiuPGuoBAnTFangHZhuGJiangCJiangXJ. Materiomics2017320210.1016/j.jmat.2017.07.004 W.D. Thomison, D.L. Allaire, in 19th AIAA Non-Deterministic Approaches Conference, AIAA SciTech Forum (American Institute of Aeronautics and Astronautics, 2017) AllisonJBackmanDChristodoulouLJOM200658112510.1007/s11837-006-0223-5 VoigtWAnn. Phys. Chem.188927457310.1002/andp.18892741206 BouazizOBuesslerPRev. Metall.2002997110.1051/metal:2002182 WangTXiongYWangYQiuPSongQZhaoKYangJXiaoJShiXChenLMater. Today Phys.20201210018010.1016/j.mtphys.2020.100180 P. Ludwik, Elemente der technologischen Mechanik. 57 p. 20 illus., III fold. diagr. (J. Springer, Berlin, 1909). TalapatraABolukiSHonarmandiPSolomouAZhaoGGhoreishiSFMolkeriAAllaireDSrivastavaAQianXFront. Mater.201968210.3389/fmats.2019.00082 KushnerHJJ. Basic Eng.1964869710.1115/1.3653121 J. Gonzalez, Z. Dai, P. Hennig, N. Lawrence, in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol. 51, ed. by A. Gretton, C.C. Robert (PMLR, Cadiz, Spain, 2016), Proceedings of Machine Learning Research, vol. 51, pp. 648–657. LyuYLiuYChengTGuoBJ. Materiomics2017322110.1016/j.jmat.2017.08.001 J. Močkus, in Optimization Techniques IFIP Technical Conference Novosibirsk, July 1-7, 1974, ed. by G.I. Marchuk (Springer Berlin Heidelberg, Berlin, Heidelberg, 1975), pp. 400–404 GhoreishiSFMolkeriAArróyaveRAllaireDSrivastavaAActa Mater.201918026010.1016/j.actamat.2019.09.009 FrazierPIPowellWBDayanikSSIAM J. Control Optim.20084752410244846710.1137/070693424 de PabloJJJonesBKovacsCLOzolinsVRamirezAPCurr. Opin. Solid State Mater. Sci.2014189910.1016/j.cossms.2014.02.003 A. Aspuru-Guzik, K. Persson, Materials Acceleration Platform: Accelerating Advanced Energy Materials Discovery by Integrating High-throughput Methods and Artificial Intelligence, Mission Innovation (2018) SteinMInterpolation of Spatial Data1999New YorkSpringer-Verlag10.1007/978-1-4612-1494-6 ArróyaveRMcDowellDLAnnu. Rev. Mater. Res.20194910310.1146/annurev-matsci-070218-125955 RasmussenCEWilliamsCKGaussian Processes for Machine Learning2006OxfordThe MIT Press1177.68165 N.M. Al Hasan, H. Hou, S. Sarkar, S. Thienhaus, A. Mehta, A. Ludwig, I. Takeuchi, Engineering (2020). JoyTTRanaSGuptaSVenkateshSKnowl.-Based Syst.202018710481810.1016/j.knosys.2019.06.026 GhoreishiSFMolkeriASrivastavaAArroyaveRAllaireDJ. Mech. Des.20181401110.1115/1.4041034 MeliaMAWhettenSRPuckettRJonesMHeidenMJArgibayNKustasABAppl. Mater. Today20201910056010.1016/j.apmt.2020.100560 N. Srinivas, A. Krause, S. Kakade, M. Seeger, in Proceedings of the 27th ICML (Omnipress, Madison, WI, USA, 2010), ICML’10, pp. 1015–1022. Haifa, Israel. J. Azimi, A. Jalali, X. Fern, Hybrid Batch Bayesian Optimization (2012). DesautelsTKrauseABurdickJWJ. Mach. Learn. Res.2014151194053 WinklerRLManage. Sci.19812747910.1287/mnsc.27.4.479INFORMS E. Contal, D. Buffoni, A. Robicquet, N. Vayatis, in Machine Learning and Knowledge Discovery in Databases, ed. by H. Blockeel, K. Kersting, S. Nijssen, F. Železný (Springer Berlin Heidelberg, Berlin, Heidelberg, 2013), pp. 225–240. JonesDRSchonlauMWelchWJJ. Global Optim.199813455167346010.1023/A:1008306431147 ZhangXXiangYJ. Materiomics20173320910.1016/j.jmat.2017.07.002 P.I. Frazier, A tutorial on bayesian optimization, arXiv preprint arXiv:1807.02811 (2018). TalapatraABolukiSDuongTQianXDoughertyEArróyaveRAutonomous efficient experiment design for materials discovery with bayesian model averagingPhys. Rev. Mater.2018211380310.1103/PhysRevMaterials.2.113803 ReussAJ. Appl. Math. Mech.1929949 PowellWBRyzhovIOOptimal Learning2012New YorkWiley10.1002/9781118309858 SF Ghoreishi (4396_CR5) 2019; 180 4396_CR7 Y Lyu (4396_CR9) 2017; 3 TT Joy (4396_CR20) 2020; 187 DR Jones (4396_CR28) 1998; 13 O Bouaziz (4396_CR34) 2002; 99 PI Frazier (4396_CR30) 2008; 47 T Desautels (4396_CR18) 2014; 15 4396_CR13 CE Rasmussen (4396_CR22) 2006 4396_CR35 4396_CR16 4396_CR15 W Voigt (4396_CR32) 1889; 274 SF Ghoreishi (4396_CR4) 2018; 140 WB Powell (4396_CR31) 2012 A Reuss (4396_CR33) 1929; 9 P Liu (4396_CR10) 2017; 3 4396_CR17 4396_CR19 JJ de Pablo (4396_CR6) 2014; 18 X Zhang (4396_CR12) 2017; 3 RL Winkler (4396_CR25) 1981; 27 MA Melia (4396_CR8) 2020; 19 M Stein (4396_CR23) 1999 J Allison (4396_CR1) 2006; 58 A Talapatra (4396_CR2) 2019; 6 4396_CR24 4396_CR27 R Arróyave (4396_CR3) 2019; 49 4396_CR21 HJ Kushner (4396_CR26) 1964; 86 T Wang (4396_CR11) 2020; 12 4396_CR29 A Talapatra (4396_CR14) 2018; 2
References_xml	– reference: BouazizOBuesslerPRev. Metall.2002997110.1051/metal:2002182 – reference: N. Srinivas, A. Krause, S. Kakade, M. Seeger, in Proceedings of the 27th ICML (Omnipress, Madison, WI, USA, 2010), ICML’10, pp. 1015–1022. Haifa, Israel. – reference: JoyTTRanaSGuptaSVenkateshSKnowl.-Based Syst.202018710481810.1016/j.knosys.2019.06.026 – reference: TalapatraABolukiSHonarmandiPSolomouAZhaoGGhoreishiSFMolkeriAAllaireDSrivastavaAQianXFront. Mater.201968210.3389/fmats.2019.00082 – reference: RasmussenCEWilliamsCKGaussian Processes for Machine Learning2006OxfordThe MIT Press1177.68165 – reference: J. Močkus, in Optimization Techniques IFIP Technical Conference Novosibirsk, July 1-7, 1974, ed. by G.I. Marchuk (Springer Berlin Heidelberg, Berlin, Heidelberg, 1975), pp. 400–404 – reference: PowellWBRyzhovIOOptimal Learning2012New YorkWiley10.1002/9781118309858 – reference: WinklerRLManage. Sci.19812747910.1287/mnsc.27.4.479INFORMS – reference: E. Contal, D. Buffoni, A. Robicquet, N. Vayatis, in Machine Learning and Knowledge Discovery in Databases, ed. by H. Blockeel, K. Kersting, S. Nijssen, F. Železný (Springer Berlin Heidelberg, Berlin, Heidelberg, 2013), pp. 225–240. – reference: DesautelsTKrauseABurdickJWJ. Mach. Learn. Res.2014151194053 – reference: KushnerHJJ. Basic Eng.1964869710.1115/1.3653121 – reference: GhoreishiSFMolkeriASrivastavaAArroyaveRAllaireDJ. Mech. Des.20181401110.1115/1.4041034 – reference: LiuPGuoBAnTFangHZhuGJiangCJiangXJ. Materiomics2017320210.1016/j.jmat.2017.07.004 – reference: LyuYLiuYChengTGuoBJ. Materiomics2017322110.1016/j.jmat.2017.08.001 – reference: JonesDRSchonlauMWelchWJJ. Global Optim.199813455167346010.1023/A:1008306431147 – reference: P. Ludwik, Elemente der technologischen Mechanik. 57 p. 20 illus., III fold. diagr. (J. Springer, Berlin, 1909). – reference: J. Azimi, A. Jalali, X. Fern, Hybrid Batch Bayesian Optimization (2012). – reference: ReussAJ. Appl. Math. Mech.1929949 – reference: de PabloJJJonesBKovacsCLOzolinsVRamirezAPCurr. Opin. Solid State Mater. Sci.2014189910.1016/j.cossms.2014.02.003 – reference: J. Gonzalez, Z. Dai, P. Hennig, N. Lawrence, in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol. 51, ed. by A. Gretton, C.C. Robert (PMLR, Cadiz, Spain, 2016), Proceedings of Machine Learning Research, vol. 51, pp. 648–657. – reference: W.D. Thomison, D.L. Allaire, in 19th AIAA Non-Deterministic Approaches Conference, AIAA SciTech Forum (American Institute of Aeronautics and Astronautics, 2017) – reference: TalapatraABolukiSDuongTQianXDoughertyEArróyaveRAutonomous efficient experiment design for materials discovery with bayesian model averagingPhys. Rev. Mater.2018211380310.1103/PhysRevMaterials.2.113803 – reference: SteinMInterpolation of Spatial Data1999New YorkSpringer-Verlag10.1007/978-1-4612-1494-6 – reference: P.I. Frazier, A tutorial on bayesian optimization, arXiv preprint arXiv:1807.02811 (2018). – reference: GhoreishiSFMolkeriAArróyaveRAllaireDSrivastavaAActa Mater.201918026010.1016/j.actamat.2019.09.009 – reference: ZhangXXiangYJ. Materiomics20173320910.1016/j.jmat.2017.07.002 – reference: ArróyaveRMcDowellDLAnnu. Rev. Mater. Res.20194910310.1146/annurev-matsci-070218-125955 – reference: AllisonJBackmanDChristodoulouLJOM200658112510.1007/s11837-006-0223-5 – reference: WangTXiongYWangYQiuPSongQZhaoKYangJXiaoJShiXChenLMater. Today Phys.20201210018010.1016/j.mtphys.2020.100180 – reference: MeliaMAWhettenSRPuckettRJonesMHeidenMJArgibayNKustasABAppl. Mater. Today20201910056010.1016/j.apmt.2020.100560 – reference: FrazierPIPowellWBDayanikSSIAM J. Control Optim.20084752410244846710.1137/070693424 – reference: A. Aspuru-Guzik, K. Persson, Materials Acceleration Platform: Accelerating Advanced Energy Materials Discovery by Integrating High-throughput Methods and Artificial Intelligence, Mission Innovation (2018) – reference: D. Ginsbourger, R. Le Riche, L. Carraro, in Computational Intelligence in Expensive Optimization Problems, ed. by Y. Tenne, C.K. Goh (Springer Berlin Heidelberg, Berlin, Heidelberg, 2010), pp. 131–162. – reference: N.M. Al Hasan, H. Hou, S. Sarkar, S. Thienhaus, A. Mehta, A. Ludwig, I. Takeuchi, Engineering (2020). – reference: VoigtWAnn. Phys. Chem.188927457310.1002/andp.18892741206 – ident: 4396_CR16 – volume: 47 start-page: 2410 issue: 5 year: 2008 ident: 4396_CR30 publication-title: SIAM J. Control Optim. doi: 10.1137/070693424 – volume: 13 start-page: 455 year: 1998 ident: 4396_CR28 publication-title: J. Global Optim. doi: 10.1023/A:1008306431147 – volume-title: Optimal Learning year: 2012 ident: 4396_CR31 doi: 10.1002/9781118309858 – ident: 4396_CR35 doi: 10.1007/978-3-662-40293-1 – ident: 4396_CR24 – volume: 12 start-page: 100180 year: 2020 ident: 4396_CR11 publication-title: Mater. Today Phys. doi: 10.1016/j.mtphys.2020.100180 – volume: 49 start-page: 103 year: 2019 ident: 4396_CR3 publication-title: Annu. Rev. Mater. Res. doi: 10.1146/annurev-matsci-070218-125955 – ident: 4396_CR29 – volume: 3 start-page: 221 year: 2017 ident: 4396_CR9 publication-title: J. Materiomics doi: 10.1016/j.jmat.2017.08.001 – volume: 3 start-page: 209 issue: 3 year: 2017 ident: 4396_CR12 publication-title: J. Materiomics doi: 10.1016/j.jmat.2017.07.002 – ident: 4396_CR27 doi: 10.1007/3-540-07165-2_55 – ident: 4396_CR13 – volume: 180 start-page: 260 year: 2019 ident: 4396_CR5 publication-title: Acta Mater. doi: 10.1016/j.actamat.2019.09.009 – volume: 86 start-page: 97 year: 1964 ident: 4396_CR26 publication-title: J. Basic Eng. doi: 10.1115/1.3653121 – volume: 99 start-page: 71 year: 2002 ident: 4396_CR34 publication-title: Rev. Metall. doi: 10.1051/metal:2002182 – volume: 58 start-page: 25 issue: 11 year: 2006 ident: 4396_CR1 publication-title: JOM doi: 10.1007/s11837-006-0223-5 – volume: 6 start-page: 82 year: 2019 ident: 4396_CR2 publication-title: Front. Mater. doi: 10.3389/fmats.2019.00082 – ident: 4396_CR15 doi: 10.1007/978-3-642-10701-6_6 – volume-title: Gaussian Processes for Machine Learning year: 2006 ident: 4396_CR22 – ident: 4396_CR17 – ident: 4396_CR19 – volume: 19 start-page: 100560 year: 2020 ident: 4396_CR8 publication-title: Appl. Mater. Today doi: 10.1016/j.apmt.2020.100560 – volume: 2 start-page: 113803 year: 2018 ident: 4396_CR14 publication-title: Phys. Rev. Mater. doi: 10.1103/PhysRevMaterials.2.113803 – volume: 140 start-page: 11 year: 2018 ident: 4396_CR4 publication-title: J. Mech. Des. doi: 10.1115/1.4041034 – volume: 18 start-page: 99 year: 2014 ident: 4396_CR6 publication-title: Curr. Opin. Solid State Mater. Sci. doi: 10.1016/j.cossms.2014.02.003 – volume-title: Interpolation of Spatial Data year: 1999 ident: 4396_CR23 doi: 10.1007/978-1-4612-1494-6 – ident: 4396_CR21 – volume: 27 start-page: 479 year: 1981 ident: 4396_CR25 publication-title: Manage. Sci. doi: 10.1287/mnsc.27.4.479 – volume: 15 start-page: 4053 issue: 119 year: 2014 ident: 4396_CR18 publication-title: J. Mach. Learn. Res. – volume: 274 start-page: 573 year: 1889 ident: 4396_CR32 publication-title: Ann. Phys. Chem. doi: 10.1002/andp.18892741206 – ident: 4396_CR7 – volume: 9 start-page: 49 year: 1929 ident: 4396_CR33 publication-title: J. Appl. Math. Mech. – volume: 3 start-page: 202 year: 2017 ident: 4396_CR10 publication-title: J. Materiomics doi: 10.1016/j.jmat.2017.07.004 – volume: 187 start-page: 104818 year: 2020 ident: 4396_CR20 publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2019.06.026
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