Assessment of Bayesian guidance strategy to develop bake-hardening ferritic steel

References

• H. Jiang, Machine Learning Fundamentals: A Concise Introduction, Cambridge University Press, Cambridge, 2021.
   Google Scholar
• Q. Tao, P. Xu, M. Li, and W. Lu, Machine learning for perovskite materials design and discovery. npj Comput. Mater. 7 (2021), pp. 23.
   Web of Science ®Google Scholar
• W. Hussain, S. Sawarb, and M. Sultan, Leveraging machine learning to consolidate the diversity in experimental results of perovskite solar cells. RSC Adv. 13 (2023), pp. 22529–22537.
   PubMed Web of Science ®Google Scholar
• M. Srivastava, A.R. Hering, Y. An, J.-P. Correa-Baena, and M.S. Leite, Machine learning enables prediction of halide perovskites’ optical behavior with >90% accuracy. ACS Energy Lett. 8 (2023), pp. 1716–1722.
   PubMedGoogle Scholar
• Z. Hui, M. Wang, X. Yin, Y. Wang, and Y. Yue, Machine learning for perovskite solar cell design. Comput. Mater. Sci 226 (2023), pp. 112215.
   Web of Science ®Google Scholar
• Y. Xu, L. Jiang, and X. Qi, Machine learning in thermoelectric materials identification: Feature selection and analysis. Comput. Mater. Sci. 197 (2021), pp. 110625.
   Web of Science ®Google Scholar
• N. Parse and S. Pinitsoontorn, Machine learning for predicting ZT values of high-performance thermoelectric materials in mid-temperature range. APL Mater. 11 (2023), pp. 081117.
   Web of Science ®Google Scholar
• G.S. Na, Artificial intelligence for learning material synthesis processes of thermoelectric materials. Chem. Mater. 35 (2023), pp. 8272–8282.
   Web of Science ®Google Scholar
• F. Mayr, M. Harth, I. Kouroudis, M. Rinderle, and A. Gagliardi, Machine learning and optoelectronic materials discovery: A growing synergy. J. Phys. Chem. Lett. 13 (2022), pp. 1940.
   PubMed Web of Science ®Google Scholar
• C.S. Khare, V.T. Barone, and R.E. Irving, Investigation of optoelectronic properties of AgSbI4 using machine learning and first principles methods. J. Phys. Chem. Solids 187 (2024), pp. 111803.
   Web of Science ®Google Scholar
• B. Akhil1, A. Bajpai1, N.P. Gurao, and K. Biswas, Designing hexagonal close packed high entropy alloys using machine learning. Model. Simul. Mater. Sci. Eng. 29 (2021), pp. 085005.
   Web of Science ®Google Scholar
• A. Bajpai, J. Bhatt, N.P. Gurao, and K. Biswas, A new approach to design multicomponent metallic glasses using the Mendeleev number. Philos. Mag. 102 (2022), pp. 2554–2571.
   Web of Science ®Google Scholar
• H. Kumar, N. Esakkiraja, A. Dash, A. Paul, and S. Bhattacharyya, Utilising physics-informed neural networks for optimisation of diffusion coefficients in pseudo-binary diffusion couples. Philos. Mag. 103 (2023), pp. 1717–1737.
   Google Scholar
• M. Hu, Q. Tan, R. Knibbe, M. Xu, B. Jiang, S. Wang, X. Li, and M.-X. Zhang, Recent applications of machine learning in alloy design: A review. Mater. Sci. Eng. R: Rep. 155 (2023), pp. 100746.
   Web of Science ®Google Scholar
• I.G. Clayson, D. Hewitt, M. Hutereau, T. Pope, and B. Slater, High throughput methods in the synthesis, characterization, and optimization of porous materials. Adv. Mater. 32 (2020), pp. 2002780.
   Web of Science ®Google Scholar
• A. Ludwig, Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods. npj Comput. Mater. 5 (2019), pp. 70.
   Web of Science ®Google Scholar
• Y. Yao, Z. Huang, T. Li, H. Wang, Y. Liu, H.S. Stein, Y. Mao, J. Gao, M. Jiao, Q. Dong, J. Dai, P. Xie, H. Xie, S.D. Lacey, I. Takeuchi, J.M. Gregoire, R. Jiang, C. Wang, A.D. Taylor, R. Shahbazian-Yassar, and L. Hu, High-throughput, combinatorial synthesis of multimetallic nanoclusters. Proc. Nat, Acad. Sci. 117 (2020), pp. 6316–6322.
   PubMed Web of Science ®Google Scholar
• S. Greenhill, S. Rana, S. Gupta, P. Vellanki, and S. Venkatesh, Bayesian optimization for adaptive experimental design: A review. IEEE. Access. 8 (2020), pp. 13937–13948.
   Web of Science ®Google Scholar
• S. Das, O.N. Mohanty, and S.B. Singh, A phenomenological model for bake hardening in minimal carbon steels. Philos. Mag. 94 (2014), pp. 2046–2061.
   Web of Science ®Google Scholar
• E. Pereloma and I. Timokhina, Bake hardening of automotive steels, in Automotive Steels: Design, Metallurgy, Processing and Applications, R. Rana, S.B. Singh, eds., Elsevier, London, 2017. pp. 259–288.
   Google Scholar
• T.C. Le and D.A. Winkler, Discovery and optimization of materials using evolutionary approaches. Chem. Rev. 116 (2016), pp. 6107–6132.
   PubMed Web of Science ®Google Scholar
• C.A.C. Coello and R.L. Becerra, Evolutionary multiobjective optimization in materials science and engineering. Mater. Manuf. Processes 24 (2009), pp. 119–129.
   Web of Science ®Google Scholar
• Y. Cai and A. Sharma, Swarm intelligence optimization: An exploration and application of machine learning technology. J. Intell. Syst. 30 (2021), pp. 460–469.
   Web of Science ®Google Scholar
• S. Dash and A. Dutta, Design of high-manganese nanostructured austenitic steel with particle swarm optimization. Mater. Manuf. Processes 35 (2020), pp. 635–642.
   Web of Science ®Google Scholar
• S. Das, S.B. Singh, O.N. Mohanty, and H.K.D.H. Bhadeshia, Understanding the complexities of bake hardening. Mater. Sci. Technol. 24 (2008), pp. 107–111.
   Web of Science ®Google Scholar
• F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É Duchesnay, Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12 (2011), pp. 2825–2830.
   Web of Science ®Google Scholar
• A. Talapatra, S. Boluki, T. Duong, X. Qian, E. Dougherty, and R. Arróyave, Autonomous efficient experiment design for materials discovery with Bayesian model averaging. Phys. Rev. Mater. 2 (2018), pp. 113803.
   Web of Science ®Google Scholar
• S. Mondal and A. Dutta, Atomistic design of nanocrystalline samples: A Bayesian approach. Mater. Lett. 300 (2021), pp. 130203.
   Web of Science ®Google Scholar
• S.S.R. Pulagam and A. Dutta, Peierls-Nabarro modeling of twinning dislocations in fcc metals. Comput. Mater. Sci. 206 (2022), pp. 111269.
   Web of Science ®Google Scholar
• Y. Zhang, D.W. Apley, and W. Chen, Bayesian optimization for materials design with mixed quantitative and qualitative variables. Sci. Rep. 10 (2020), pp. 4924.
   PubMed Web of Science ®Google Scholar
• R. Garnett, Bayesian Optimization, Cambridge University Press, Cambridge, 2023.
   Google Scholar
• D. Packwood, Bayesian Optimization for Materials Science, Springer Nature, Singapore, 2017.
   Google Scholar
• J. Jiménez and J. Ginebra, pyGPGO: Bayesian optimization for python. J. Open Source Softw. 2 (2017), pp. 431.
   Google Scholar
• B. Shahriari, K. Swersky, Z.Y. Wang, R.P. Adams, and N. de Freitas, Taking the human out of the loop: A review of Bayesian optimization. Proc. IEEE 104 (2016), pp. 148–175.
   Web of Science ®Google Scholar