The Guided Investigation of Materials with Outstanding Mechanical Response
The emergence of materials informatics has led to a paradigm shift in materials science, presenting the opportunity to expedite materials discovery. This dissertation demonstrates an integrated approach using a combination of experiment and machine learning (ML) to identify and characterize syntheti...
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Format: | Dissertation |
Language: | English |
Published: |
ProQuest Dissertations & Theses
01-01-2022
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Online Access: | Get full text |
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Summary: | The emergence of materials informatics has led to a paradigm shift in materials science, presenting the opportunity to expedite materials discovery. This dissertation demonstrates an integrated approach using a combination of experiment and machine learning (ML) to identify and characterize synthetically accessible superhard materials. First, an ML model is implemented to identify Mo0.9W1.1BC and ReWC0.8, materials with potentially exceptional bulk (K) and shear (G) moduli, and experiment reveals that each compound exceeds the superhard threshold at low indentation loads. Hydrostatic diamond anvil cell compression experiments further corroborate the ML predictions, as each compound demonstrates ultraincompressibility. Isostructural Mo2−xWxBC solid solutions also express ultraincompressibility, while K trends with increased W content. To clarify the anisotropic deformation behavior of Mo0.9W1.1BC and ReWC0.8, non-hydrostatic compression experiments are employed and observed lattice strain and texture development are attributed to nonuniform covalent support and planar density. Ultimately, this work demonstrates the successful application of a data-driven approach to guide the discovery of new materials with extraordinary mechanical properties. Data-driven methodologies may also accelerate the study of novel multi-principal element alloys (MPEAs), a relatively new class of materials with extraordinary properties. Unfortunately, composition-microstructure-property relationships among MPEAs remain unclear and a current lack of data availability impedes the predictive capabilities of ML approaches. This contribution ventures to construct an MPEA dataset to elucidate these relationships. An investigation of microstructure, mechanical response, and radiation tolerance is presented for four MPEAs fabricated via spark plasma sintering (SPS), including Al0.3CrCuFeNi, AlCrCuFeNi, Al0.3CrFeMnNi, and AlCrFeMnNi. Microstructural analyses reveal the multiphase nature of each alloy, primarily consisting of simple ordered and disordered cubic structures. The irradiated alloys generally demonstrate phase stability, while trends in nanoindentation hardness are consistent among the pristine and irradiated mate- rial with no discernible radiation-induced effect on hardness. In a separate investigation, the phase content and nanomechanical properties of twenty-two distinct SPS-fabricated MPEAs are reported. Consistent with observations made in the first study, all alloys exhibit multiphase structures and hardness scales with body-centered cubic phase fraction. This dissertation reflects an initiative to support design frameworks with the potential to guide the search for compositionally complex materials with tailored properties. |
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ISBN: | 9798368485317 |