High-Throughput DFT Screening Method Via Local Bonding Model for Corrosion Resistant Alloy Designed for Naval Applications

随机性 密度泛函理论 从头算 高熵合金 合金 腐蚀 材料科学 伊辛模型 计算机科学 格子(音乐) 统计物理学 计算化学 化学 物理 数学 量子力学 冶金 统计 声学
作者
Sirui Li,Anup Panindre,Huibin Ke,Tianshu Li,G. S. Frankel,Christopher D. Taylor
出处
期刊:Meeting abstracts 卷期号:MA2020-02 (13): 1323-1323
标识
DOI:10.1149/ma2020-02131323mtgabs
摘要

In this talk, first principles density functional theory (DFT) implemented in the Vienna Ab-initio Package (VASP) 1,2 is employed to compile a reproducible and high-fidelity adsorption energy database to lend insights into searching and designing for corrosion resistant alloys (CRAs) on the atomic scale 3–6 . The current compilation and theory development begin with the high entropy alloy (HEA). Since the independent investigation of HEA by Yeh 7 and Cantor 8 , this class of alloy has shown a range of desirable features, such as good mechanical properties, phase stability at high temperature, and excellent corrosion resistance 7,9,10 . For these reasons, HEA has drawn attentions as possible candidate to supersede stainless steel for naval applications. One of the outstanding challenges to investigate HEA at the atomic scale, however, is the manifestation of randomness in the individual atoms that occupy the lattice sites. Two popular schools of theories have emerged over the years to tackle this issue. One is to overlay the potential fields of constituent atoms on top of the same lattice site, thereby ensuring “true” randomness 11 . The other is to score correlation function between first, second, third and so on nearest neighbor atoms with an Ising model approach to explicitly represent atoms within reasonable number for DFT computation 12 . Upon surveying the literature, both theories have been employed to mainly investigate the mechanical behavior of HEA 13,14 , while comparatively less has been adapted to study the corrosion resistant properties 15 . Herein, we propose a local bonding model to aid high throughput calculations that is necessary for both establishing a database and the subsequent screening of beneficial dopant elements in the design of CRAs. Our local bonding model is derived from the observation that the outer most layer atoms that come into contact with environment contribute the most to the computed adsorption energies. For HEA that has face-center-cubic (fcc) lattice, the local bonding model reduces to account for the most frequently occurring low-index fcc (111) surface, and the adsorption position on the three-fold fcc site. We will show that within this approach, the total number of DFT calculations become much more manageable and tractable. Furthermore, we expand upon the previously established concepts of aqueous species surface coverage 16 and chloride susceptibility index 17 , and include the alloy surface affinity for hydron and oxygen atoms. Altogether, we believe that the surface competition amongst chloride, hydrogen, and oxygen species can lend useful insights into re-passivation potential framework 18–22 for the CRAs of interest by establishing an unforeseen correlation. Reference G. Kresse and J. Furthmüller, Comput. Mater. Sci. , 6 , 15–50 (1996). G. Kresse and J. Furthmüller, Phys. Rev. B - Condens. Matter Mater. Phys. , 54 , 11169–11186 (1996). C. D. Taylor, P. Lu, J. Saal, G. S. Frankel, and J. R. Scully, npj Mater. Degrad. , 2 (2018) http://dx.doi.org/10.1038/s41529-018-0027-4. H. Ke and C. D. Taylor, Corrosion , 75 , 708–726 (2019). C. D. Taylor, Corros. Eng. Sci. Technol. , 50 , 490–508 (2015) http://www.tandfonline.com/doi/full/10.1179/1743278215Y.0000000012. D. R. Gunasegaram, M. S. Venkatraman, and I. S. Cole, Int. Mater. Rev. , 59 , 84–114 (2014) http://www.tandfonline.com/doi/full/10.1179/1743280413Y.0000000024. M. H. Tsai and J. W. Yeh, Mater. Res. Lett. , 2 , 107–123 (2014) https://doi.org/10.1080/21663831.2014.912690. B. Cantor, I. T. H. Chang, P. Knight, and A. J. B. Vincent, Mater. Sci. Eng. A , 375 – 377 , 213–218 (2004) http://dx.doi.org/10.1016/j.msea.2003.10.257. M. C. Gao, J.-W. Yeh, P. K. Liaw, and Y. Zhang, Eds., High-Entropy Alloys: Fundamentals and Applications , Springer, (2016). T. Li et al., Electrochim. Acta , 306 , 71–84 (2019) https://doi.org/10.1016/j.electacta.2019.03.104. E. Materials, The EMTO-CPA Method , p. 83–94, (2007). S. Wei, L. G. Ferreira, J. E. Bernard, and A. Zunger, 42 (1990). C. Niu, C. R. LaRosa, J. Miao, M. J. Mills, and M. Ghazisaeidi, Nat. Commun. , 9 , 1–9 (2018) http://dx.doi.org/10.1038/s41467-018-03846-0. C. Niu, A. J. Zaddach, C. C. Koch, and D. L. Irving, J. Alloys Compd. , 672 , 510–520 (2016) http://dx.doi.org/10.1016/j.jallcom.2016.02.108. A. J. Samin and C. D. Taylor, Corros. Sci. , 134 , 103–111 (2018) https://doi.org/10.1016/j.corsci.2018.02.017. C. D. Taylor, S. Li, and A. J. Samin, Electrochim. Acta , 269 , 93–101 (2018) https://doi.org/10.1016/j.electacta.2018.02.150. H. Ke, T. Li, P. Lu, G. S. Frankel, and C. D. Taylor, SSRN Electron. J. (2020). G. S. Frankel, T. Li, and J. R. Scully, J. Electrochem. Soc. , 164 , C180–C181 (2017) http://jes.ecsdl.org/lookup/doi/10.1149/2.1381704jes. T. Li, J. R. Scully, and G. S. Frankel, J. Electrochem. Soc. , 165 , C484–C491 (2018). T. Li, J. R. Scully, and G. S. Frankel, J. Electrochem. Soc. , 165 , C762–C770 (2018). T. Li, J. R. Scully, and G. S. Frankel, J. Electrochem. Soc. , 166 , C115–C124 (2019). T. Li, J. R. Scully, and G. S. Frankel, J. Electrochem. Soc. , 166 , C3341–C3354 (2019).

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