Michael C. Gao
Leidos
Configuration entropyEntropy of mixingLattice (order)Solid solutionDensity functional theoryChemistryMetallurgyMaterials scienceStructural materialCorrosionIntermetallicAlloyHigh entropy alloysEnthalpyPhase (matter)CrystallographyPhase diagramMicrostructureCALPHADThermodynamics
92Publications
36H-index
6,386Citations
Publications 87
Newest
#1Michael C. Gao (DOE: United States Department of Energy)H-Index: 36
#2E-Wen HuangH-Index: 17
Last. Liang Jiang (Yantai University)
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#1Martin DetroisH-Index: 12
#2Zongrui PeiH-Index: 12
Last. Jeffrey A. HawkH-Index: 31
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Abstract The effect of Si contamination when using B to improve the creep properties of a Ni-based superalloy was investigated using advanced characterization techniques and first-principles simulations on alloys with high and low B levels with varying Si contents. The positive effect of B segregation along grain boundaries on the creep properties was mitigated by the presence of Si which showed a similar segregation preference. Density functional theory calculations were used for validation by ...
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#1Rui Feng (UT: University of Tennessee)H-Index: 14
#1Rui Feng (UT: University of Tennessee)H-Index: 39
Last. Lizhi Ouyang (Tennessee State University)H-Index: 26
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Developing affordable and light high-temperature materials alternative to Ni-base superalloys has significantly increased the efforts in designing advanced ferritic superalloys. However, currently developed ferritic superalloys still exhibit low high-temperature strengths, which limits their usage. Here we use a CALPHAD-based high-throughput computational method to design light, strong, and low-cost high-entropy alloys for elevated-temperature applications. Through the high-throughput screening,...
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#2Michael WidomH-Index: 41
Last. Michael C. GaoH-Index: 36
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Comparison of free energies between different phases and different compositions underlies the prediction of alloy phase diagrams. To allow direct comparison, consistent reference points for the energies or enthalpies are required, and the entropy must be placed on an absolute scale, yielding absolute free energies. Here we derive absolute free energies of liquids from ab-initio molecular dynamics (AIMD) by combining the directly simulated enthalpies with an entropy derived from simulated densiti...
This chapter presents an innovative framework for the application of machine learning and data analytics for the identification of alloys or composites exhibiting certain desired properties of interest. The main focus is on alloys and composites with large composition spaces for structural materials. Such alloys or composites are referred to as high-entropy materials (HEMs) and are here presented primarily in context of structural applications. For each output property of interest, the correspon...
#1Martin DetroisH-Index: 12
#2Zongrui Pei (ORNL: Oak Ridge National Laboratory)H-Index: 12
Last. Jeffrey A. HawkH-Index: 31
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Abstract An alloy's processing history, including melting, remelting or the choice of stock material, affects its purity and eventually involves tramp element pickup or retention. In this investigation, variants of a novel Ni-based superalloy were manufactured with different levels of purity. The so-called low purity alloys contained 0.138 wt. % Cu and 0.019 wt. % Si while the Cu and Si levels were below x-ray fluorescence (XRF) detection limits of 0.003 and 0.010 wt. %, respectively, in the hig...
1 CitationsSource
#1Kyle A. Rozman (Leidos)H-Index: 5
#2Martin Detrois (Leidos)H-Index: 12
Last. Jeffrey A. HawkH-Index: 31
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The potential of high-entropy alloys (HEAs) to meet or exceed austenitic stainless steel performance with the additional benefit of improved hot corrosion/oxidation resistance makes FCC HEAs attractive for use in energy applications. While shorter-term creep tests have been reported in the literature on HEAs, not all methodologies utilize repeatable techniques. This manuscript reports on over 23,500 accumulated hours of tensile creep testing with adherence to ASTM standards on a melt-solidified ...
2 CitationsSource
#1Zongrui Pei (ORNL: Oak Ridge National Laboratory)H-Index: 12
#2Junqi Yin (ORNL: Oak Ridge National Laboratory)H-Index: 11
Last. Michael C. Gao (Leidos)H-Index: 2
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The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability. Some rules with seemingly good predictability were, however, tested using small data sets. Based on an unprecedented large dataset containing 1252 multicomponent alloys, machine-learning methods showed that the formation of solid solutions can be very accurately predicted (93%). The machine-learning results help identify the most important features, s...
32 CitationsSource
#1William Yi Wang (NPU: Northwestern Polytechnical University)H-Index: 18
#2Bin Tang (NPU: Northwestern Polytechnical University)H-Index: 16
Last. Jinshan Li (NPU: Northwestern Polytechnical University)H-Index: 41
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This article presents a brief review of our case studies of data-driven Integrated Computational Materials Engineering (ICME) for intelligently discovering advanced structural metal materials, including light-weight materials (Ti, Mg, and Al alloys), refractory high-entropy alloys, and superalloys. The basic bonding in terms of topology and electronic structures is recommended to be considered as the building blocks/units constructing the microstructures of advanced materials. It is highlighted ...
6 CitationsSource
#1Yong ZhangH-Index: 112
#2Min ZhangH-Index: 2
Last. Tongde ShenH-Index: 1
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10 CitationsSource