ProME: An integrated computational platform for material properties at extremes and its application in multicomponent alloy design

Xingyu Gao , William Yi Wang , Xin Chen , Xiaoyu Chong , Jiawei Xian , Fuyang Tian , Lifang Wang , Huajie Chen , Yu Liu , Houbing Huang , Haifeng Song

Materials Genome Engineering Advances ›› 2025, Vol. 3 ›› Issue (3) : e70029

PDF
Materials Genome Engineering Advances ›› 2025, Vol. 3 ›› Issue (3) : e70029 DOI: 10.1002/mgea.70029
RESEARCH ARTICLE

ProME: An integrated computational platform for material properties at extremes and its application in multicomponent alloy design

Author information +
History +
PDF

Abstract

We have built an integrated computational platform for material properties at extreme conditions, Professional Materials at Extremes (ProME) v1.0, which enables integrated calculations for multicomponent alloys, covering high temperatures up to tens of thousands of Kelvin, high pressures up to millions of atmospheres, and high strain rates up to millions per second. A series of software packages have been developed and integrated into ProME v1.0, including AI-based crystal search for crystal structure search under pressure, similar atomic environment for disordered configuration modeling, Multiphase Fast Previewer by Mean-Field Potential for multiphase thermodynamic properties, High-throughput Toolkit for Elasticity Modeling for thermo-elastic properties, TRansport at Extremes for electrical and thermal conductivity, High plastic phase model software for phase-field simulation of microstructure evolution under high strain rates, and AutoCalphad for modeling and optimization of phase diagrams with variable compositions. ProME v1.0 has been applied to design the composition of the quaternary alloys Platinum-Iridium-Aluminum-Chromium (Pt-Ir-Al-Cr) for engine nozzles of aerospace attitude-orbit control, achieving high-temperature strength comparable to the currently used Pt-Ir alloys but with significantly reduced costs for raw materials. ProME offers crucial support for advancing both fundamental scientific understanding and industrial innovation in materials research and development.

Keywords

extreme conditions / high strain rates / integrated computation / multicomponent alloys / wide temperature–pressure range

Cite this article

Download citation ▾
Xingyu Gao, William Yi Wang, Xin Chen, Xiaoyu Chong, Jiawei Xian, Fuyang Tian, Lifang Wang, Huajie Chen, Yu Liu, Houbing Huang, Haifeng Song. ProME: An integrated computational platform for material properties at extremes and its application in multicomponent alloy design. Materials Genome Engineering Advances, 2025, 3(3): e70029 DOI:10.1002/mgea.70029

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Glass CW, Oganov AR, Hansen N. USPEX—evolutionary crystal structure prediction. Comput Phys Commun. 2006; 175(11-12): 713-720.

[2]

Wang Y, Lv J, Zhu L, Ma Y. CALYPSO: a method for crystal structure prediction. Comput Phys Commun. 2012; 183(10): 2063-2070.

[3]

Zhu X, Lin DY, Fang J, Gao XY, Zhao YF, Song HF. Structure and thermodynamic properties of zirconium hydrides by structure search method and first principles calculations. Comput Mater Sci. 2018; 150: 77-85.

[4]

Laurent B, Vanderbilt D. Virtual crystal approximation revisited: application to dielectric and piezoelectric properties of perovskites. Phys Rev B. 2000; 61(12): 7877-7882.

[5]

Ruban AV, Abrikosov IA. Configurational thermodynamics of alloys from first principles: effective cluster interactions. Rep Prog Phys. 2008; 71(4):046501.

[6]

Sanchez JM, Ducastelle F, Gratias D. Generalized cluster description of multicomponent systems. Phys Stat Mech Appl. 1984; 128(1): 334-350.

[7]

Sanchez JM. Cluster expansions and the configurational energy of alloys. Phys Rev B. 1993; 48(18): 14013-14015.

[8]

Zunger A, Wei SH, Ferreira LG, Bernard JE. Special quasirandom structures. Phys Rev Lett. 1990; 65(3): 353-356.

[9]

Tian F, Lin DY, Gao X, Zhao YF, Song HF. A structural modeling approach to solid solutions based on the similar atomic environment. J Chem Phys. 2020; 153(3):034101.

[10]

Zhang X, Kang J, Wei SH. Defect modeling and control in structurally and compositionally complex materials. Nature Computational Science. 2023; 3(3): 210-220.

[11]

Wentzcovitch RM, Yu YG, Wu Z. Thermodynamic properties and phase relations in mantle minerals investigated by first principles quasiharmonic theory. Rev Mineral Geochem. 2010; 71(1): 59-98.

[12]

Zhang DB, Sun T, Wentzcovitch RM. Phonon quasiparticles and anharmonic free energy in complex systems. Phys Rev Lett. 2014; 112(5):058501.

[13]

Hellman O, Igor A, Simak S. Lattice dynamics of anharmonic solids from first principles. Phys Rev B. 2011; 84(18):180301.

[14]

Petros S, Eriksson O, Katsnelson MI, Rudin SP. Entropy driven stabilization of energetically unstable crystal structures explained from first principles theory. Phys Rev Lett. 2008; 100(9):095901.

[15]

Lin ST, Blanco M, Iii W. The two-phase model for calculating thermodynamic properties of liquids from molecular dynamics: validation for the phase diagram of Lennard-Jones fluids. J Chem Phys. 2003; 119(22): 11792-11805.

[16]

Grabowski B, Lars I, Hickel T, Neugebauer J. Ab initioup to the melting point: anharmonicity and vacancies in aluminum. Phys Rev B. 2009; 79(13):134106.

[17]

Moustafa SG, Schultz AJ, Zurek E, Kofke DA. Accurate and precise ab initio anharmonic free-energy calculations for metallic crystals: application to hcp Fe at high temperature and pressure. Phys Rev B. 2017; 96(1):014117.

[18]

Chen LQ. Phase-field models for microstructure evolution. Annu Rev Mater Res. 2002; 32(1): 113-140.

[19]

Boettinger WJ, Warren JA, Beckermann C, Karma A. Phase-field simulation of solidification. Annu Rev Mater Res. 2002; 32(1): 163-194.

[20]

Xiao Z, Hao M, Guo X, Tang G, Shi SQ. A quantitative phase field model for hydride precipitation in zirconium alloys: part II. Modeling of temperature dependent hydride precipitation. J Nucl Mater. 2015; 459: 330-338.

[21]

Wang L, Liu Z, Zhuang Z. Developing micro-scale crystal plasticity model based on phase field theory for modeling dislocations in heteroepitaxial structures. Int J Plast. 2016; 81: 267-283.

[22]

Sundman B, Jansson B, Andersson JO. The Thermo-Calc databank system. Calphad. 1985; 9(2): 153-190.

[23]

Chen SJ, Daniel SE, Zhang F, et al. The PANDAT software package and its applications. Calphad. 2002; 26(2): 175-188.

[24]

Bale CW, Bélisle E, Chartrand P, et al. FactSage thermochemical software and databases, 2010–2016. Calphad. 2016; 54: 35-53.

[25]

Saunders N, Guo UKZ, Li X, Miodownik AP, Schillé JP. Using JMatPro to model materials properties and behavior. JOM. 2003; 55(12): 60-65.

[26]

Shang SL, Wang Y, Liu ZK. ESPEI: extensible, self-optimizing phase equilibrium infrastructure for magnesium alloys. In: Magnesium Technology 2010 – Held During TMS 2010 Annual Meeting and Exhibition; 2010: 617-622.

[27]

Wang Y, Hu YJ, Chong X, et al. Quasiharmonic calculations of thermodynamic properties for La3–xTe4 system. Comput Mater Sci. 2017; 142: 417-426.

[28]

Chong X, Hu M, Wu P, et al. Tailoring the anisotropic mechanical properties of hexagonal M7X3 (M=Fe, Cr, W, Mo; X=C, B) by multialloying. Acta Mater. 2019; 169: 193-208.

[29]

Chong X, Guan PW, Hu M, Jiang Y, Li Z, Feng J. Exploring accurate structure, composition and thermophysical properties of η carbides in 17.90 wt% W-4.15 wt% Cr-1.10 wt% V-0.69 wt% C steel. Scr Mater. 2018; 154: 149-153.

[30]

Choudhary K, Garrity KF, Reid ACE, et al. The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design. npj Comput Mater. 2020; 6(1):173.

[31]

Jain A, Ong SP, Hautier G, et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 2013; 1(1):011002.

[32]

Voorhees PW, DePablo J, Olson G. (Invited) The center for hierarchical materials design: realizing the promise of the materials genome initiative. In: Meeting Abstracts/Meeting Abstracts (Electrochemical Society CD-ROM); 2017; MA2017-01(35):1684.

[33]

Wang G, Peng L, Li K, et al. ALKEMIE: an intelligent computational platform for accelerating materials discovery and design. Comput Mater Sci. 2021; 186:110064.

[34]

Yang XY, Wang Z, Zhao X, Jianlong S, Zhang M, Liu H. MatCloud: a high-throughput computational infrastructure for integrated management of materials simulation, data and resources. Comput Mater Sci. 2018; 146: 319-333.

[35]

The High-Throughput Computational Platform of Chinese Materials Genome Engineering. Nscc-tj.cn; 2017. http://mathtc.nscc-tj.cn/

[36]

Wales DJ, Doye JPK. Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms. J Phys Chem A. 1997; 101(28): 5111-5116.

[37]

Zhao Y, Chen X, Li J. TGMin: a global-minimum structure search program based on a constrained basin-hopping algorithm. Nano Res. 2017; 10(10): 3407-3420.

[38]

Chen X, Zhao Y, Zhang Y, Li J. TGMin: an efficient global minimum searching program for free and surface-supported clusters. J Comput Chem. 2018; 40(10): 1105-1112.

[39]

Shang C, Liu ZP. Stochastic surface walking method for structure prediction and pathway searching. J Chem Theor Comput. 2013; 9(3): 1838-1845.

[40]

Zhang L, Lin DY, Wang H, Car R, Weinan E. Active learning of uniformly accurate interatomic potentials for materials simulation. Phys Rev Mater. 2019; 3(2):023804.

[41]

Bisbo MK, Hammer B. Efficient global structure optimization with a machine-learned surrogate model. Phys Rev Lett. 2020; 124(8):086102.

[42]

Bisbo MK, Hammer B. Global optimization of atomic structure enhanced by machine learning. Phys Rev B. 2022; 105(24):245404.

[43]

Jacobsen TL, Jørgensen MS, Hammer B. On-the-Fly machine learning of atomic potential in density functional theory structure optimization. Phys Rev Lett. 2018; 120(2):026102.

[44]

Li C, Li WW, Zhang XL, Du L, Sheng HW. Predicted stable electrides in Mg-Al systems under high pressure. Phys Chem Chem Phys. 2022; 24(20): 12260-12266.

[45]

Chen X, Gao XY, Zhao YF, Lin DY, Chu WD, Song HF. TensorAlloy: an automatic atomistic neural network program for alloys. Comput Phys Commun. 2020; 250:107057.

[46]

Chen X, Wang LF, Gao XY, et al. Machine learning enhanced empirical potentials for metals and alloys. Comput Phys Commun. 2021; 269:108132.

[47]

Chen X, Ouyang Y, Chen X, et al. TensorMD: scalable tensor-diagram based machine learning interatomic potential on heterogeneous many-core processors. arXiv preprint arXiv:2310.08439v2. [physics.comp-ph]. 2023.

[48]

Liu PY, Zhang B, Niu R, et al. Engineering metal-carbide hydrogen traps in steels. Nat Commun. 2024; 15(1):724.

[49]

Zhai YX, Li YH, Yang TR, et al. Weakening the self-trapping of helium by electron density regulation in WTaVCr high-entropy alloys. Scr Mater. 2024; 242:115930.

[50]

Dong H, Chen H, Riyahi Khorasgani A, et al. Revealing the influence of Mo addition on interphase precipitation in Ti-bearing low carbon steels. Acta Mater. 2022; 223:117475.

[51]

Yao G, Wang WY, Zou C, et al. Local orders, lattice distortions, and electronic structure dominated mechanical properties of (ZrHfTaM1M2)C (M = Nb, Ti, V). J Am Ceram Soc. 2022; 105(6): 4260-4276.

[52]

Zou C, Li J, Wang WY, et al. Integrating data mining and machine learning to discover high-strength ductile titanium alloys. Acta Mater. 2021; 202: 211-221.

[53]

Liu X, Zhao H, Ding H, Lin DY, Tian F. Effect of short-range order on the mechanical behaviors of tensile and shear for NiCoFeCr. Appl Phys Lett. 2021; 119(13):131904.

[54]

Wang SD, Liu XJ, Lei ZF, et al. Chemical short-range ordering and its strengthening effect in refractory high-entropy alloys. Phys Rev B. 2021; 103(10):104107.

[55]

van de Walle A, Ceder G. Automating first-principles phase diagram calculations. J Phase Equil. 2002; 23(4): 348-359.

[56]

Sluiter MHF, Kawazoe Y. Invariance of truncated cluster expansions for first-principles alloy thermodynamics. Phys Rev B. 2005; 71(21):212201.

[57]

Ghosh G, van de Walle A, Asta M. First-principles phase stability calculations of pseudobinary alloys of (Al,Zn)Ti with L1, DO and DO structures. J Phase Equilibria Diffus. 2007; 28(1): 9-22.

[58]

Shin D, van de Walle A, Wang Y, Liu Z-K. First-principles study of ternary FCC solution phases from special quasirandom structures. Phys Rev B. 2007; 76(14):144204.

[59]

Szwacki N. 2D BxC1–x layers as predicted by the cluster-expansion approach. Acta Phys Pol, A. 2014; 126(5): 1215-1217.

[60]

Gao X, Yang Z, Fang J, Xian J, Liu H, Song H. A multiphase fast previewer based on mean-field potential approach. Unpublished.

[61]

Yang Z, Xian J, Xu Y, et al. HTEM: high-throughput toolkit for elasticity modeling. Unpublished.

[62]

Yang Z, Xian J, Gao X, Tian F, Song H. A semi-analytic universal model on elasticity across wide temperatures and pressures. J Chem Phys. 2024; 161(19):194101.

[63]

Song Y, Xian J, Xu Y, et al. Machine learning accelerated study on temperature dependent elastic properties of Ti-based refractory high entropy alloys. Mater Today Commun. 2025; 42:111559.

[64]

Zhou X, Gao Z, Chen H, et al. Developing a simulation program for transport properties of disordered crystalline materials based on linear response theory. Unpublished.

[65]

Chen H, Xu Y, Xian J, Gao X, Tian F, Song H. Electrical and thermal conductivity of Mg and typical Mg-Al alloys at high temperature and pressure. Acta Phys Sin. 2025; 74(12):127102.

[66]

Wang Y, Li L. Mean-field potential approach to thermodynamic properties of metal: Al as a prototype. Phys Rev B. 2000; 62(1): 196-202.

[67]

Jiuxun S, Lingcang C, Qiang W, Fuqian J. Equivalence of the analytic mean-field potential approach with free-volume theory and verification of its applicability based on the Vinet equation of state. Phys Rev B. 2005; 71(2):024107.

[68]

Kresse G, Furthmüller J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput Mater Sci. 1996; 6(1): 15-50.

[69]

Kohn W, Sham LJ. Self-consistent equations including exchange and correlation effects. Phys Rev. 1965; 140(4A): A1133-A1138.

[70]

Gilvarry JJ. The Lindemann and Grüneisen laws. Phys Rev. 1956; 102(2): 308-316.

[71]

Olijnyk H, Holzapfel WB. High-pressure structural phase transition in Mg. Phys Rev B. 1985; 31(7): 4682-4683.

[72]

Stinton GW, MacLeod SG, Cynn H, et al. Equation of state and high-pressure/high-temperature phase diagram of magnesium. Phys Rev B. 2014; 90(13):134105.

[73]

Cui C, Xian J, Liu H, Tian F, Gao X, Song H. Melting curve of magnesium up to 460 GPa from ab initio molecular dynamics simulations. J Appl Phys. 2022; 131(19):195901.

[74]

Errandonea D. The melting curve of ten metals up to 12 GPa and 1600 K. J Appl Phys. 2010; 108(3):033517.

[75]

Courac A, Le Godec Y, Solozhenko VL, Guignot N, Crichton WA. Thermoelastic equation of state and melting of Mg metal at high pressure and high temperature. J Appl Phys. 2020; 127(5):055903.

[76]

Errandonea D, Boehler R, Ross M. Melting of the alkaline-earth metals to 80 GPa. Phys Rev B. 2001; 65(1):012108.

[77]

Beason MT, Jensen BJ, Crockett SD. Shock melting and the HCP-BCC phase boundary of Mg under dynamic loading. Phys Rev B. 2021; 104(14):144106.

[78]

Li LT, Gao XY, Liu HF, Xian JW, Tian FY, Song HF. Multiphase equation of state for magnesium based on first-principles simulations. Phys Rev B. 2024; 110(22):224107.

[79]

Li L, Gao X, Yang Z, Xian J, Song H. First-principles simulation of the high-pressure melting points of dilute solid-solution Mg-Al and Al-Mg alloys. Foundry Technol. 2023; 44(2): 153-160.

[80]

Urtiew PA, Grover R. The melting temperature of magnesium under shock loading. J Appl Phys. 1977; 48(3): 1122-1126.

[81]

Wang Y, Wang J, Zhang H, et al. A first-principles approach to finite temperature elastic constants. J Phys Condens Matter. 2010; 22(22):225404.

[82]

Slutsky LJ, Garland CW. Elastic constants of magnesium from 4.2°K to 300°K. Phys Rev. 1957; 107(4): 972-976.

[83]

Tan Y, Xiao Y, Xue T, Li J, Jin K. Melting of MB2 alloy under shock compression. Chin J High Press Phys. 2019; 33(2):020106.

[84]

Kubo R, Yokota M, Nakajima S. Statistical-mechanical theory of irreversible processes. II. Response to thermal disturbance. J Phys Soc Jpn. 1957; 12(11): 1203-1211.

[85]

Slack GA. Nonmetallic crystals with high thermal conductivity. J Phys Chem Solid. 1973; 34(2): 321-335.

[86]

Takamichi I, Guthrie RIL. The Physical Properties of Liquid Metals. Oxford University Press; 1988.

[87]

Ho CY, Powell RW, Liley PE. Thermal conductivity of the elements. J Phys Chem Ref Data. 1972; 1(2): 279-421.

[88]

Pan H, Pan F, Wang X, et al. Correlation on the electrical and thermal conductivity for binary Mg–Al and Mg–Zn alloys. Int J Thermophys. 2013; 34(7): 1336-1346.

[89]

Chen H, Xu Y, Liu L, et al. Lattice distortion tuning resistivity Invar effect in high-entropy alloys. Phys Rev B. 2025; 111(9):094209.

[90]

Dong J, Lin T, Shao H, et al. Advances in degradation behavior of biomedical magnesium alloys: a review. J Alloys Compd. 2022; 908:164600.

[91]

He M, Chen L, Yin M, Xu S, Liang Z. Review on magnesium and magnesium-based alloys as biomaterials for bone immobilization. J Mater Res Technol. 2023; 23: 4396-4419.

[92]

Malik A, Nazeer F, Naqvi H, et al. Microstructure feathers and ASB susceptibility under dynamic compression and its correlation with the ballistic impact of Mg alloys. J Mater Res Technol. 2021; 16: 801-813.

[93]

Zhao P, Song E, Low T, Wang Y, Niezgoda SR. An integrated full-field model of concurrent plastic deformation and microstructure evolution: application to 3D simulation of dynamic recrystallization in polycrystalline copper. Int J Plast. 2016; 80: 38-55.

[94]

Sun Y, Luo J, Zhu J. Phase field study of the microstructure evolution and thermomechanical properties of polycrystalline shape memory alloys: grain size effect and rate effect. Comput Mater Sci. 2018; 145: 252-262.

[95]

Xu K, Sheng J, Liu Y. Insight into dynamic recrystallization of AZ31B magnesium alloys by phase-field simulations. Chin J High Press Phys. 2024; 38(3):030105.

[96]

Luque A, Ghazisaeidi M, Curtin WA. A new mechanism for twin growth in Mg alloys. Acta Mater. 2014; 81: 442-456.

[97]

Guo P, Liu X, Zhu B, Liu W, Zhang L. The microstructure evolution and deformation mechanism in a casting AM80 magnesium alloy under ultra-high strain rate loading. J Magnesium Alloys. 2021; 10(11): 3205-3216.

[98]

Li N, Huang G, Zhong X, Liu Q. Deformation mechanisms and dynamic recrystallization of AZ31 Mg alloy with different initial textures during hot tension. Mater Des. 2013; 50: 382-391.

[99]

Ен П, Brahme A, Staraselski Y, Agnew SR, Mishra RK, Inal K. Effect of extension 101-2 twins on texture evolution at elevated temperature deformation accompanied by dynamic recrystallization. Mater Des. 2016; 96: 446-457.

[100]

Xie C, He J, Zhu B, et al. Transition of dynamic recrystallization mechanisms of as-cast AZ31 Mg alloys during hot compression. Int J Plast. 2018; 111: 211-233.

[101]

Xu SW, Kamado S, Matsumoto N, Honma T, Kojima Y. Recrystallization mechanism of as-cast AZ91 magnesium alloy during hot compressive deformation. Mater Sci Eng. 2009; 527(1-2): 52-60.

[102]

Basu I, Al-Samman T. Twin recrystallization mechanisms in magnesium-rare earth alloys. Acta Mater. 2015; 96: 111-132.

[103]

Fatemi SM, Kazemi Asl AA, Paul H. Effects of pretwins on texture and microstructural evolutions of AZ31 magnesium alloy during high temperature deformation. J Alloys Compd. 2022; 894:162412.

[104]

Sun L, Xu ZT, Peng LF, Lai XM. Grain-size-dependent ductile-to-brittle fracture mechanism of titanium sheets. Scr Mater. 2022; 219:114877.

[105]

Yan CK, Feng AH, Qu SJ, et al. Dynamic recrystallization of titanium: effect of pre-activated twinning at cryogenic temperature. Acta Mater. 2018; 154: 311-324.

[106]

Hu X, Ji Y, Chen L, Lebensohn RA, Chen LQ, Cui X. Spectral phase-field model of deformation twinning and plastic deformation. Int J Plast. 2021; 143:103019.

[107]

Mecking H, Kocks UF. Kinetics of flow and strain-hardening. Acta Metall. 1981; 29(11): 1865-1875.

[108]

Liu ZK. Thermodynamics and its prediction and CALPHAD modeling: review, state of the art, and perspectives. Calphad. 2023; 82:102580.

[109]

Andersson J.-O, Helander T, Höglund L, Shi P, Sundman B. Thermo-Calc & DICTRA, computational tools for materials science. Calphad. 2002; 26(2): 273-312.

[110]

Cao W, Chen SL, Zhang F, et al. PANDAT software with PanEngine, PanOptimizer and PanPrecipitation for multi-component phase diagram calculation and materials property simulation. Calphad. 2009; 33(2): 328-342.

[111]

Björck Å. Least squares methods. Handb Numer Anal. 1990; 1: 465-652.

[112]

Otis R. Uncertainty reduction and quantification in computational thermodynamics. Comput Mater Sci. 2022; 212:111590.

[113]

Murnaghan FD. The compressibility of media under extreme pressures. Proc Natl Acad Sci. 1944; 30(9): 244-247.

[114]

Joubert JM, Crivello JC, Deffrennes G. Modification of Lu’s (2005) high pressure model for improved high pressure/high temperature extrapolations. Part I: modeling of platinum at high pressure/high temperature. Calphad. 2021; 74:102304.

[115]

Jain A, Ong SP, Chen W, et al. FireWorks: a dynamic workflow system designed for high-throughput applications. Concurr Comput Pract Exp. 2015; 27(17): 5037-5059.

[116]

Setlur AR, Jaya Nirmala S, Singh HS, Khoriya S. An efficient fault tolerant workflow scheduling approach using replication heuristics and checkpointing in the cloud. J Parallel Distr Comput. 2019; 136: 14-28.

[117]

Yu W, Chong X, Liang Y, et al. Discovering novel γ-γ’ Pt-Al superalloys via lattice stability in Pt3Al induced by local atomic environment distortion. Acta Mater. 2024; 281:120413.

[118]

Hill PJ, Biggs T, Ellis P, Hohls J, Taylor S, Wolff IM. An assessment of ternary precipitation-strengthened Pt alloys for ultra-high temperature applications. Mater Sci Eng. 2001; 301(2): 167-179.

[119]

Fischer B, Behrends A, Freund D, Lupton DF, Merker J. High temperature mechanical properties of the platinum group metals. Platin Met Rev. 1999; 43(1): 18-28.

[120]

Irving GN, Stringer J, Whittle DP. The oxidation behavior of Co-Cr-Al alloys at 1000 C. Corrosion. 1977; 33(2): 56-60.

[121]

Massalski TB, Okamoto H. Binary Alloy Phase Diagrams. American Society for Metals Metals Park; 1986.

[122]

Fischer B. New platinum materials for high temperature applications. Adv Eng Mater. 2001; 3(10): 811-820.

[123]

Jozwik P, Polkowski W, Bojar Z. Applications of Ni3Al based intermetallic alloys—current stage and potential perceptivities. Materials. 2015; 8(5): 2537-2568.

RIGHTS & PERMISSIONS

2025 The Author(s). Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.

AI Summary AI Mindmap
PDF

47

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/